REDUCTION 2011‐2014
Deliverable 5.2 Report on Collective Evaluation from Field Trials in Phase‐1
31 August 2014
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
Public Document
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
Project acronym: REDUCTION
Project full title: Reducing Environmental Footprint based on Multi‐Modal Fleet management Systems for Eco‐Routing and Driver Behaviour Adaptation
Work Package: WP5
Document title: Report on Collective Evaluation from Field Trials in Phase‐1
Version: 5.0
Official delivery date: 31/08/2014
Actual publication date: 31/08/2014
Type of document: Report
Nature: Public
Authors: Dimitrios Katsaros (UTH), Chrysi Laspidou (UTH), Stavroula Maglavera (UTH), Nikolaos Lemonas (UTH), Kristian Torp (AAU), Ove Andersen (AAU), Kyriacos Mouskos (CTL), Athanasios Lois (TrainOSE), Marcel Morssink (TRI)
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Approved by: REDUCTION consortium partners
Version Date Sections Affected
0.1 27/12/2012 Initial empty template
1.0 25/01/2013 Updated by Aalborg, FlexDanmark
1.1 05/02/2013 Updated by TrainOSE
1.2 18/02/2013 Updated by Aalborg, FlexDanmark
1.3 21/02/2013 Updated by UTH
1.4 22/02/2013 Updated by CTL
2.1 23/02/2013 Updated to correct various issues
2.2 27/02/2013 Review comments processed
3.0 19/08/2013 Major updates by TRI and CTL
4.0 12/02/2014 Updated to reflect 2nd review comments
5.0 26/08/2014 Various changes and corrections
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Executive Summary Field operational testing is widely recognized as an effective instrument to test new transport technologies and methodologies in the real world. Previous experience has shown that field trials are an excellent way to raise awareness, collect real data, and enhance the take‐up of ICT solutions. Field tests have also proved to be a powerful tool for gaining insight into the way new functions and systems suit the user when operated in a real context. Following these best practises, REDUCTION has designed three field trials. One is run by TrainOSE to develop and test multimodal services in Greece by combining train services, with taxi and bus services. The other field trial run by FlexDanmark deploys a large taxi fleet in order to develop techniques for the reduction of the GHG emissions from vehicles, establishing environmental profiles of vehicle types, and also estimate GHG emissions based on GNSS measurements. The third field trial run by CTL (and its two complementary simulation‐based trials one run by CTL itself and other run by UTH), aims at collecting data for driver behaviour analysis, for hardware and communications/networking protocols testing. In the course of project lifetime, one more field study – that by Trinite Automation – were added to investigate the traffic control center’s perspective. The present document describes the outcomes of the first phase of the field trials.
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Table of Contents Executive Summary...... V
Table of Contents ...... 6
List of Tables...... 12
List of Figures ...... 15
Glossary...... 17
1. Introduction ...... 18
1.1 Project Overview...... 18
1.2 Work Package Objectives and Tasks...... 18
1.3 Objective of this Deliverable...... 19
2. Related work to REDUCTION’s field trials ...... 21
2.1 EuroFOT field trials ...... 21
2.2 simTD field trials...... 22
2.3 CVIS ...... 22
2.4 EcoDriver...... 23
2.5 In‐Time ...... 23
2.6 What’s appropriate and what’is missing for REDUCTION ...... 24
3. Field Trials: A generic description...... 25
4. FlexDanmark Field Trial: Phase‐1...... 26
4.1 Introduction ...... 26
4.2 Nature of the trial...... 26
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4.3 Description of the input ...... 27
4.3.1 Travel‐time Map...... 27
4.3.2 Eco Map...... 27
4.3.3 Trips ...... 28
4.3.4 Web Services Details...... 29
(1) FlexDanmark Web Service ...... 29
(a) Input ...... 29
(b) Output...... 29
(c) Implementation...... 29
(2) Speed Chart Web Service ...... 30
(a) Input ...... 30
(b) Output...... 31
(c) Implementation...... 31
(3) Eco‐Routing Web Service ...... 32
(a) Input ...... 32
(b) Output...... 33
(c) Implementation...... 34
4.4 Description of the output...... 35
4.4.1 Use Scenario...... 35
4.4.2 Use Cases from D3.1...... 40
4.4.3 Business Goals ...... 42
4.5 Obstacles in running the field trial ...... 43
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4.5.1 Risks from D3.1 ...... 43
4.5.2 Open Issues from D3.3 ...... 44
4.5.3 Open Issues from D5.1 ...... 44
4.5.4 Obstacles...... 45
4.6 Obtained Results ...... 46
4.6.1 Single Trip ...... 46
4.6.2 Multiple Trips...... 50
4.6.3 Business Goals ...... 51
4.7 Trustworthiness of obtained results...... 51
4.8 Planning for Phase‐2 of the Field Trial...... 52
4.9 Suggested Changes in REDUCTION’s architecture and methods ...... 52
4.10 Conclusions...... 52
5. CTL Cyprus Fleet Field Trial: Phase‐1 and Phase 2 Plan ...... 54
5.1 Introduction ...... 54
5.2 Nature of the fleet driver behavior field trial...... 55
5.3 Plan for Carrying out the Cyprus Fleet Driver Behavior Field Study (Revised) ...... 58
5.4 Main Responsibilities of the Cyprus Fleet Field Trial...... 63
5.5 Specifications of the Cyprus Fleet Trial Technologies ...... 64
5.5.1 DELPHI MyFI V2X/CCU Technical Data Summary...... 64
5.5.2 DDE MyFI Installation Requirements...... 65
5.5.3 Costas Papaellinas Organization (CPO) fleet technical summary ...... 66
5.5.4 CPO Installation Requirements...... 68
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Obstacles in running the field trial ...... 69
Tasks ‐ Cyprus Fleet Field Trial Plan – Phase I (Revised) ...... 71
5.6 Tasks – Proposed Driving Behavior Fleet Field Trial‐ Phase II ...... 77
6. Trinité Amsterdam Field Trail: Phase‐1...... 82
6.1 Goals of the field trail ...... 83
6.2 Field trail Amsterdam ...... 84
6.3 Use case ...... 85
6.4 Best REDUCTION driver contest ...... 86
6.5 Status of the field trial ...... 86
7. CTL Nicosia Simulation Field Trial: Phase‐1 ...... 88
7.1 Introduction ...... 88
7.2 Supplemental Literature on DTA and Environmental Modeling...... 89
7.2.1 User Equilibrium and System Optimal Traffic Assignment...... 89
7.2.2 Environmental Objectives and Traffic Assignment ...... 90
7.2.3 Eco‐Routing Based Navigation ...... 91
7.2.4 Environmental Models...... 91
7.3 Nature of the trial...... 98
7.3.1 Geography of the Nicosia Simulation Field Trial...... 98
7.3.2 Time Schedule of the Nicosia Simulation Field Study...... 99
7.4 Hardware and Software Characteristics of the Simulation Environment ...... 99
7.5 Description of the input ...... 100
7.5.1 GIS and Roadway Geometry...... 100
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7.5.2 Bus Data...... 100
7.5.3 Traffic control Data...... 100
7.5.4 Historical Traffic Flow data...... 101
7.6 VISTA Software Update with Environmental Modeling Capability...... 101
7.6.1 Implementation methodology of the VISTA DTA with environmental modeling in Nicosia, Cyprus and Austin, TX, USA...... 106
7.7 Description of the output...... 108
7.8 Conclusions and Future Work ...... 119
7.9 Obstacles in running the field trial ...... 121
7.10 Planning for Phase‐2 of the Field Trial...... 121
7.10.1 Nicosia VISTA‐DTA Simulation Field Trial Task Plan...... 121
8. UTH’s Simulated Trial: Phase‐1...... 124
8.1 Introduction ...... 124
8.2 Nature of the trial...... 124
8.3 Description of the input ...... 128
8.4 Description of the output...... 128
8.5 Obstacles in running the field trial ...... 128
8.6 Obtained Results ...... 129
8.6.1 Total average CO2 emissions per meter traveled...... 129
8.6.2 Moderate vehicle density [100 vehicles] ...... 131
Impact of truck percentage………………………………………………………………….132
Impact of speed………………………………………………………………………………135
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Impact of time the engine is on…………………………………………………………….136
Impact of CO2 emissions model…………………………………………………………….137
8.6.3 Small vehicle density [40 vehicles] ...... 138
Impact of truck percentage………………………………………………………………….138
Impact of speed………………………………………………………………………………140
Impact of time engine is on…………………………………………………………………142
Impact of CO2 emissions model…………………………………………………………….143
8.6.4 Large vehicle density [200 vehicles] ...... 143
Impact of truck percentage…………………………………………………………………144
Impact of speed………………………………………………………………………………145
Impact of time engine is on…………………………………………………………………147
Impact of CO2 emissions model…………………………………………………………….148
8.7 Trustworthiness of obtained results...... 148
8.8 Planning for Phase‐2 of the Field Trial...... 149
8.9 Suggested Changes in REDUCTION’s architecture and methods ...... 149
8.10 Conclusions...... 149
9. TrainOSE Field Trial: Phase‐1 ...... 150
9.1 Introduction ...... 150
9.2 Nature of the trial...... 150
9.3 Description of the input ...... 156
9.4 Description of the output...... 157
9.5 Obstacles in running the field trial ...... 159
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9.6 Obtained Results ...... 160
9.7 Trustworthiness of obtained results...... 163
9.8 Planning for Phase‐2 of the Field Trial...... 163
9.9 Suggested Changes in REDUCTION’s architecture and methods ...... 165
9.10 Conclusions...... 165
10. Risk analysis and lessons learned...... 166
11. Conclusion ...... 169
References ...... 171
12. Appendix...... 177
List of Tables Table 1. What can REDUCTION use from existing projects, and where it needs to innovate. ....24
Table 2. Summary of REDUCTIONʹs field trials...... 25
Table 3 Zone Approach used for Trips (time in minutes, price in DK krone )...... 29
Table 4 Details for Specific Route ...... 37
Table 5 Gistrup‐Billund: Fuel, Fastest and Shortest Details...... 48
Table 6 Billund‐Gistrup: Fuel, Fastest, and Shortest...... 48
Table 7 University‐Airport: Fuel, Fastest, and Shortest...... 48
Table 8 Airport‐University: Fuel, Fastest, Shortest ...... 48
Table 9 Estimated Fuel Consumption and Actual Fuel Consumption...... 49
Table 10 Price and Fuel Consumption Fastest Routes...... 50
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Table 11 Price and Fuel Consumption Fuel‐Efficient Routes ...... 50
Table 12 Cyprus Fleet Driver Behavior Field Trial Schedule...... 80
Table 13. Nicosia Fleet Driver Behavior Field Trial – Deliverables and Milestones...... 80
Table 14. PAMVEC, Nicosia, Cyprus ‐ Network Results (15‐min assignment)...... 109
Table 15. PAMVEC, Austin, TX ‐ Network Results (15‐min assignment) ...... 109
Table 16. VISTA simulation field trial schedule for phase II...... 123
Table 17. Nicosia simulation field study‐ Phase II eliverables and milestones...... 123
Table 18: Simulation parameters...... 126
Table 19. Comparison of BA, NIVC, IVC for 100 vehicles, 30% trucks, 50 Km/h and 5 minutes engine on...... 132
Table 20. Comparison of BA, NIVC, IVC for 100 vehicles, 70% trucks, 50 Km/h and 5 minutes engine is on...... 133
Table 21. Comparison of BA, NIVC, IVC for 100 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on...... 134
Table 22. Comparison of BA, NIVC, IVC for 100 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on...... 135
Table 23. Comparison of IVC for 100 vehicles, 30% trucks, 5 minutes engine on and speed 50Km/h vs. 70Km/h...... 136
Table 24. Comparison of IVC for 100 vehicles, 30% trucks, 50Km/h, and engine on 2min, 5 min and 10 min...... 137
Table 25. Comparison of IVC for 100 vehicles, 30% trucks, 50Km/h, 5 minutes engine on: Emission models EMIT vs. SIDRA...... 138
Table 26. Comparison of NIVC and IVC for 40 vehicles, 30% trucks, 50 Km/h and 5 minutes engine is on...... 139
Table 27. Comparison of NIVC and IVC for 40 vehicles, 70% trucks, 50 Km/h and 5 minutes
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Table 28. Comparison of NIVC and IVC for 40 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on...... 140
Table 29. Comparison of NIVC and IVC for 40 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on...... 141
Table 30. Comparison of IVC for 40 vehicles, 30% trucks, 5 minutes engine is on and speed 50Km/h vs. 70Km/h...... 141
Table 31. Comparison of IVC for 40 vehicles, 30% trucks, 50Km/h, and engine is on for 2min, 5 min and 10 min...... 142
Table 32. Comparison of IVC for 40 vehicles, 30% trucks, 50Km/h, 5 minutes engine is on: Emission models EMIT vs. SIDRA...... 143
Table 33. Comparison of NIVC and IVC for 200 vehicles, 30% trucks, 50 Km/h and 5 minutes engine is on...... 144
Table 34. Comparison of NIVC and IVC for200 vehicles, 70% trucks, 50 Km/h and 5 minutes engine is on...... 144
Table 35. Comparison of NIVC and IVC for 200 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on...... 145
Table 36. Comparison of NIVC and IVC for 200 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on...... 146
Table 37. Comparison of IVC for 200 vehicles, 30% trucks, 5 minutes engine is on and speed 50Km/h vs. 70Km/h...... 146
Table 38. Comparison of IVC for 200 vehicles, 30% trucks, 50Km/h, and engine is on for 2min, 5 min and 10 min...... 147
Table 39. Comparison of IVC for 200 vehicles, 30% trucks, 50Km/h, 5 minutes engine is on: Emission models EMIT vs. SIDRA...... 148
Table 40. Traveling from Volos to Kalamata...... 159
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Table 41. A summary of risks for the field trials...... 168
List of Figures Figure 1. Screendump of FlexDanmark Web Service...... 30
Figure 2 Screendump of Speed‐chart Web Service ...... 32
Figure 3 Screendump of EcoRouting Web Service...... 34
Figure 4 Shortest (blue), Fastest (red), and Most Fuel‐efficient (green) Routes...... 35
Figure 5 a) Gistrup‐Billund, b) University‐Airport, c)Gistrup‐Hirtshals, d) Gistrup‐Aarhus...... 47
Figure 6. OSEL Bus Route 116 Figure 7. OSEL Bus Route 158 ...... 57
Figure 8. OSEL Bus Route 160 Figure 9. OSEL Bus Route 110 ...... 57
Figure 10. OSEl Bus Route 112 Figure 11. OSEL Bus Route 157 ...... 57
Figure 12. CPO OBD Guard...... 66
Figure 13. Overview of the Amsterdam use‐case...... 83
Figure 14. Routes field trail Amsterdam...... 85
Figure 15 Nicosia, Cyprus and Austin TX, USA Networks implemented ...... 99
Figure 16. VISTA framework for modeling eco‐routing impacts ...... 107
Figure 17. Travel Time/veh (h/veh) for Nicosia, Cyprus...... 110
Figure 18. Travel Time/veh (h/veh) for Austin, TX, USA...... 110
Figure 19. Nicosia ‐ Energy Consumption/veh (kWh/veh)...... 111
Figure 20. Austin, TX, USA ‐ Energy Consumption/veh (kWh/veh)...... 112
Figure 21. Nicosia – Energy Saved/Eco‐routed veh. (kWh/veh) ...... 113
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Figure 22. Austin, TX, USA – Energy Saved/Eco‐routed veh. (kWh/veh) ...... 114
Figure 23. Nicosia – Average Distance Travelled (km/veh)...... 114
Figure 24. Austin TX, USA – Average Distance Travelled (km/veh) ...... 115
Figure 25. Energy savings vs. OD path‐length(distance) Nicosia, Cyprus...... 116
Figure 26. Energy savings vs. OD path‐length (distance), Austin, TX, USA...... 117
Figure 27. Change in eco path length as a percentage of OD distance vs. OD distance, Nicosia, Cyprus ...... 118
Figure 28. Change in eco path length as a percentage of OD distance vs. OD distance (a) Austin, TX, (b) Nicosia, Cyprus ...... 118
Figure 29. The arrow indicates the area where the accident occurred...... 126
Figure 30. The re‐route followed by vehicles after becoming aware of the incident...... 127
Figure 31. A screenshot of the All Blocked scenario with 200 vehicles...... 127
Figure 32. Vehicles with access to the exit reroute after waiting for 3 minutes...... 128
Figure 33. Average CO2 per meter traveled for the EMIT emission model...... 130
Figure 34. Average CO2 per meter traveled for the SIDRA emission model...... 131
Figure 35. Age distribution...... 151
Figure 36. Public transportation usage...... 151
Figure 37. Eco‐driving...... 152
Figure 38. Answers chart to 1st question...... 152
Figure 39. Answers chart to 2nd question...... 153
Figure 40. Answers chart to 3rd question...... 153
Figure 41. Answers chart to 4th question...... 154
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Figure 42. Full coverage of Greek territory...... 155
Figure 43. From the original address to the closest departure point...... 157
Figure 44. From arrival point to desired address...... 159
Figure 45. Energy –frequency distribution for electrified locomotives. (Domokos – Thessaloniki)...... 163
Figure 46. Energy – energy consumption differences between (same type) locomotives. (Domokos – Thessaloniki)...... 164
Figure 47. Impact of driving behaviour in energy consumption...... 164
Figure 48. Approaches for ITS fuel efficiency and CO2 emission reduction...... 169
Glossary GHG Green House Gases GNSS Global Navigation Satellite System C2X Car‐to‐X CAN Controller Area Network JSON JavaScript Object Notation ETL Extract‐Transform‐Load V2X Vehicle‐to‐X IVC InterVehicle Communications
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1. Introduction
1.1 Project Overview Reduction of CO2 emissions is a great challenge for the transport sector nowadays. Despite recent progress in vehicle manufacturing and fuel technology, still a significant fraction of CO2 emissions in EU cities is resulting from vehicular transportation. Therefore, additional innovative technologies are needed to address the challenge of reducing emissions. The REDUCTION project focuses on advanced ICT solutions for managing multi‐modal fleets and reducing their environmental footprint. REDUCTION collects historic and real‐time data about driving behavior, routing information, and emissions measurements, that are processed by advanced predictive analytics to enable fleets enhancing their current services as follows:
1) Optimizing driving behavior: supporting effective decision making for the enhancement of drivers’ education and the formation of effective policies about optimal traffic operations (speeding, braking, etc.), based on the analytical results over the data that associate driving‐ behavior patterns with CO2 emissions;
2) Eco‐routing: suggesting environmental‐friendly routes and allowing multi‐modal lets to reduce their overall mileage automatically; and
3) Support for multi‐modality: offering a transparent way to support multiple transportation modes and enabling co‐modality.
REDUCTION follows an interdisciplinary approach and brings together expertise from several communities. Its innovative, decentralized architecture allows scalability to large fleets by combining both V2V and V2I approaches. Its planned commercial exploitation, based on its proposed cutting edge technology, aims at providing a major breakthrough in the fast growing market of services for ʺgreenʺ fleets in EU and worldwide, and present substantial impact to the challenging environmental goals of EU.
1.2 Work Package Objectives and Tasks The goal of WP5 is twofold; firstly, to confirm that the architecture of the REDUCTION system is generic enough to encompass diverse “application” scenarios, and secondly, to provide useful input to the partners for any omissions concerning the operational part of the system, that might have got unnoticed, or to develop more advanced features for the system. Therefore, the existence of several field trials is mandatory – multimodal and traditional, as well. We have planned three field trials, by Bektra/FlexDanmark (taxi fleet), CTL (bus fleet), and TrainOSE (multimodal deploying trains and taxis).
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After the completion of the first task of the work package, which was the collection of requirements/specifications, the first phase of the field trials run by Bektra/FlexDanmark, CTL and TrainOSE are the three tasks that comprise the evolution of the work package.
In the first trial of Bektra/FlexDanmark, the main focus is to validate the eco‐routes. The validation part must examine if the environmental indicates lead to trips that are practical to use. The first field trial by TrainOSE aims at demonstrating the appropriateness of the system in multimodal scenarios that involves transport of passengers in Greece. The first field trial for CTL will involve the following tasks: 1) Identifying the area that will be used to implement the REDUCTION vehicle routing algorithms, 2) Conduct travel time and traffic count studies on a selected set of routes for model calibration, 3) Design the field test of REDUCTION. 4) Request and secure the participation of the Cyprus Public Works Department in data gathering such as GIS, historical traffic counts, historical travel times, bus routes and schedules, historical bus occupancy and fares, 5) Request and secure the participation of at least one delivery company in the filed trial, 6) Request and secure the participation of one or more bus fleet operators of Nicosia and the greater Nicosia region, 7) Assist in the development of a green route points system for the participants. The first phase of CTL’ field trial was complemented by the first phase of a simulated field trial (run by UTH) in order to validate the usefulness of V2V communications for CO2 reduction
Whereas the first phase of the field trials run by Bektra/FlexDanmark, TrainOSE and UTH ended as initially planned and agreed, some delays caused by the Cyprus Public Works Department resulted in subsequent delays in the timely completion of the first phase of CTL’s field trial. That resulted in a delay in the evaluation of the efficacy of DELPHI’s hardware, but this is not considered a major problem, since it has been extensively tested by the company in other setting. The most significant problem will be the delay in the collection of data for driver behaviour. As far, as the impact of V2V communications on CO2 which was another goal for this field study, again it is not considered severe, because UTH’s simulated field trial covered that point at a significant degree.
1.3 Objective of this Deliverable The goal of this deliverable is to describe in details the methods, procedures and results obtained after the completion of the first phase of the three field trials. In particular – without referring to which of the field trials will achieve what – this deliverable sets as its targets the following: ¾ Reduction of the GHG emissions from vehicles. ¾ Establishing environmental profiles of vehicle types. ¾ Estimation of GHG emissions based on GNSS measurements. ¾ Estimation of the GHG emissions of a single vehicle.
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¾ Provide to the users a way to identify how to move around (e.g., Greek territory) with the most eco‐way among others modes of transportation. ¾ Highlight the applicability and usefulness of vehicle‐to‐vehicle communications not only for safety applications, but also for ecological purposes, such as the reduction of GHG emissions.
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2. Related work to REDUCTION’s field trials
2.1 EuroFOT field trials The field testings of euroFOT focus on 8 distinct functions that assist the driver in detecting hazards, preventing accidents and making driving more efficient. The functions are the following:
¾ Adaptive Cruise Control
¾ Forward Collision Warning
¾ Speed Regulation System
¾ Blind Spot Information System
¾ Lane Departure Warning
¾ Curve Speed Warning
¾ Safe Human/Machine Interface
¾ Fuel Efficiency Advisor
More than 1000 cars and trucks equipped with a range of different intelligent technologies are tested on European roads across France, Germany, Italy and Sweden. During this field testing, a multitude of sensors and devices monitor every aspect of individual driver behaviour in real‐world traffic conditions. Questionnaires are also used to get driver feedback on the usefulness of the various systems. The vehicle management centers (VMC) play a key role in collecting the data from more than 1000 vehicles. They provide an operational platform for the entire project where practical details are treated in line with the recommendations made during FOT preparation and piloting. The eight functions were tested in about 1000 vehicles from 9 European OEM brands. A total of 460 data loggers were distributed across 500 vehicles (some loggers will be rotated between vehicles). An additional 300 vehicles were studied through questionnaires for the Lane Departure Warnings (LDW) functions, and an additional 50 vehicles used the telematics capabilities of a fleet‐management system for gathering fuel‐efficiency data. The data loggers are of three types:
¾ 275 CAN‐only data loggers
¾ 150 CAN+Video data loggers, and
¾ 35 CAN+Video+Extra sensing data loggers
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2.2 simTD field trials Audi, GM and Daimler, are looking at ways to improve the communications capabilities of vehicles to allow them to easily exchange information with each other and infrastructure to help improve safety, efficiency and driver convenience.
Daimler’s effort , called car‐to‐X (C2X) has begun its largest ever field trial with 120 network‐ linked vehicles hitting the roads in Germany’s Rhine‐Main region. Building on technologies emerging from Daimler’s Network on Wheels (NoW) and Fleetnet projects, the C2X system sees a network link included on each vehicle that not only allows these vehicles to share information with each other, but also with traffic infrastructure, such as traffic lights. This is designed to allow drivers to be alerted to potential traffic hazards on the road ahead to give them more time to slow down or take a detour, while traffic light systems can be triggered according to demand as a way to improve traffic flow. The system can also be used for more mundane tasks, such as providing the best route, based on traffic data, to the nearest car park.
The field trial is being conducted as part of the simTD (Safe Intelligent Mobility – test field Germany) research project, which is a collaboration between German car makers, automotive suppliers, communications companies, research institutes and the public sector. During the first eight weeks of the field operational test, drivers spent 13,000 testing hours on the road, making up 460,000 kilometers of test drives. In the upcoming blocks, this will increase to more than 150 kilometers of test drives per day and vehicle. The test drivers perform specific experiments based on scripted road scenarios. During the tests, their responses to these scenarios are being collected. Based on this data, researchers will then assess the system’s functionality, suitability, and acceptance for everyday use as well as its impact on traffic safety and mobility.
All data gained in the field test is transferred to the simTD test center located at the DRIVE‐ Center Hessen. With an estimated data volume of 30 terabytes requiring processing and evaluation, this aspect of the test constitutes a great challenge. The evaluation is very complex due to the huge amount of data and the necessity to consider the interaction of multiple vehicles. This is essential because the nature of cooperative systems is that actions or information of one vehicle affect the state or actions of other vehicles.
2.3 CVIS CVIS (Cooperative Vehicle‐Infrastructure Systems) is a major new European research and development project aiming to design, develop and test the technologies needed to allow cars to communicate with each other and with the nearby roadside infrastructure. The CVIS Test sites are located in six European countries. The true highlight of all trials has been the
22 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] test site London trial with eight fleet operators (incl. Coca‐Cola and Alliance Healthcare), testing CVIS in they every day operation during a period of three months. In addition to the local trials, another important aspect of the CVIS test sites is that they been used in important demonstrations. Test Site Sweden hosted the CVIS demonstrations at the ITS World Congress in 2009 and shortly after a demonstration event for the European transport ministers. The Test Site Netherlands‐Belgium hosted the Cooperative Mobility Showcase, the most important event on cooperative systems in 2010. A typical field trial consisted of:
¾ A CVIS equipped vehicle drives sending out its vehicle beaconing.
¾ A SAFESPOT equipped RSU receives the beaconing.
¾ A Service center
2.4 EcoDriver ΕcoDriver is an EC co‐funded integrated project under the FP7, which main focus is on driver interaction with the vehicle and optimized “green” driving feedback strategies to ensure user acceptance and compliance. (www.ecodriver‐project.eu ). The project will address technical aspects in the vehicle‐environment‐driver loop across a wide range of vehicles and powertrains. The target is to achieve a sustained 20% reduction in energy use. The main innovative aspects of the project are to: a) optimize feedback for both nomadic devices and built‐in systems and compare the effectiveness of each (measured by reduced energy consumption as compared with an existing baseline system), b) tailor feedback to driving style and traffic conditions, c) minimize any side‐effects of eco‐driving support in terms of drivers distraction and safety, d) use real‐time fuel use calculators to ensure the most accurate feedback. ecoDriver will carry out real world trials across a range of driving scenarios, powertrains, and vehicle types. Data gathered with these experiments will enable the validation of the proposed reduction of 20% in CO2 emissions. A total of seven vehicle management centers and 57 vehicles (including trucks, buses and passenger cars) are foreseen.
2.5 In‐Time The main focus of In‐Time (www.in‐time‐project.eu) is with Multimodal Real Time Traffic and Travel Information (RTTI) services provided to drivers and travelers with the goal to reduce drastically energy consumption in urban transport, resulting in: less pollution, including CO2 emissions, particle emissions and noise, less traffic congestion, less energy consumption, a shift away from individual transport towards public transport, and responsive and adaptive traffic management. Pilot operations where performed in six European cities (Vienna, Munich, Florence, Oslo, Bucharest, Brno) where about 900 test users validated a core part of In‐Time architecture and B2B CAI. The approach of in‐Time was proved very successful: around 5% of users were able to compare transport modes and
23 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] make a choice. Using traffic management equipment they achieved cuts in energy consumption via traffic control, and reduced power consumption by using LED technologies.
2.6 What’s appropriate and what’is missing for REDUCTION It is apparent that there are a number of completed projects that have touched some issues that are/seem relevant to the REDUCTION’s goals and could offer solutions. In Table 1, we summarize what is appropriate and what is missing from the existing related work with respect to REDUCTION’s objectives.
REDUCTION requirements‐1: REDUCTION requirements‐2: Provided by earlier projects & Technologies involved Additional innovations being useful to REDUCTION needed for REDUCTION Management of “coordinated” Preparation & running Field trials methodologies: sets of vehicles, i.e., fleets methodologies. EuroFOT, simTD, CVIS aiming to collectively achieve a Collection of results. final goal. Annotated road network Plain shortest‐paths graphs. Shortest paths in the Eco‐routing: eCoMove algorithms presence of dynamic links and probabilistic weights. Innovations with respect to eco‐friendly shortest‐path algorithms. Also, vehicle‐ Concept of multimodality: In‐ Plain shortest‐path pooling will be a goal for Time algorithms REDUCTION (actually a taxi‐ pooling approach for emissions reduction). Intelligence in discovering Driving behavior adaptation: Statistical analysis of hidden patterns, i.e., data ecoDriver driver behaviour mining (e.g., classification) methodologies. V2V communication mode for V2V for content management safety applications (usually (may involve multi‐hop DSRC, Geonetworking involves 1‐hop communication). Manage bulks communication): CVIS, simTD of data, not small packets.
Table 1. What can REDUCTION use from existing projects, and where it needs to innovate.
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3. Field Trials: A generic description Field operational testing is widely recognized as an effective instrument to test new transport technologies. Previous experience in Europe, the US and Japan has shown that field trials are an excellent way to raise awareness, collect real data, and enhance the take‐up of ICT solutions. Field tests have also proved to be a powerful tool for gaining insight into the way new functions and systems suit the user when operated in a real context. n Europe, such testing has traditionally been carried out at national level. Following these best practises, REDUCTION has (initially) designed three field trials. All of them are related to passenger vehicle fleets. One is run by TrainOSE to develop, test and enhance multimodal services in Greece by combining train services, with taxi and bus services. The other field trial run by Bektra/FlexDanmark deploys a large taxi fleet in order to develop techniques for the reduction of the GHG emissions from vehicles, establishing environmental profiles of vehicle types, and also estimate GHG emissions based on GNSS measurements. Finally, the third field trial run by CTL (and its two complementary simulation‐based trials one run by CTL and the other run by UTH), aims at collecting real data for drive behaviour analysis, for hardware and communications/networking protocols testing. During the evolution of the project a fourth field trial – that run by Trinite Automation and briefly described in this deliverable, was designed to investigate the traffic control center’s perspective. Table 2 summarizes the field trials. The present document describes the outcomes of the first phase of these field trials.
Country Partner Goals Type ¾ Establishing environmental Flexanmark Denmark profiles of vehicle types Field Op. Trial [Aalborg (country‐wide) ¾ Estimation of the GHG emissions (passenger fleet) Aarhus] of a single vehicle CTL ¾ Demonstrate fuel efficiency by a)Field Op. Trial [Delphi bus/delivery vehicles monitoring Cyprus (Nicosia) (passenger fleet) UTH ¾ Demonstrate fuel/emissions b)Simulated UHI] reduction by simulation Greece ¾ provide multimodal services Field Op. Trial TrainOSE (country‐wide) ¾ emissions reduction (passenger fleet) The Netherlands Field Op. Trial Trinite Aut. ¾ I2V for emissions reduction (Amsterdam) (freight fleet) ¾ V2V communications for Simulated Greece (Volos) UTH emissions reduction (mixed)
Table 2. Summary of REDUCTIONʹs field trials.
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4. FlexDanmark Field Trial: Phase‐1
4.1 Introduction The main purpose of the FlexDanmark field is to use the results from WP3 “Data Management for Environment Aware Routing and Geo‐Locational Analysis Application” to address the following business goals (from Delivery 3.1 (D3.1)). In particular, the following business goals from D3.1 are pursued.
• Reduction of the GHG emissions from vehicles used in flex‐traffic [1] [2]. • Establishing environmental profiles of vehicle types. • Estimation of GHG emissions based on GNSS measurements. • Estimation of the GHG emissions of a single vehicle.
In addition, the case study should provide feedback to WP4 “System Design and Integration” and should validate the detailed functionality for computing eco‐routes as done in WP3.
4.2 Nature of the trial The field trial is conducted based on real‐world GNSS and CANBus data. The data is loaded into a software prototype that is based on the design from WP3 (mainly D3.1). Two set of trips are being evaluated
• A small set of canonical trips taken from the northern part of Denmark where both large quantities of GNSS and CANBus data is available. This small set of trips is examined in details to evaluate the trustworthiness of the GNSS and CANBus data foundation.
• A large set of trips from Region North Denmark for the month November 2012. This set of trips is used to examine what the differences are in pricing and fuel consumption if trips are optimized for fastest route or for most fuel‐efficient route.
For the set of canonical trips the start and end points of the trips are plotted into web site that is a front‐end to the software prototype built. The web site can visualize the shortest, fastest, and most fuel‐efficient route.
For each trip in the set of trips from Region North Denmark the duration, the length, and the estimated fuel‐consumption is added when optimizing for fastest trips and when optimization for most fuel‐efficient trips.
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The first field trial has been conducted as a number of iterations where mistakes and errors from the previous iteration have been corrected and the results of the new iterations have been discussed.
The participants in the first field trial at FlexDanmark are FlexDanmark and AAU.
4.3 Description of the input The data foundation or the input to the first field trial is the following.
1. A travel‐time map created as described in WP3.
2. An eco‐map created as described in WP3.
3. A real‐world set of passenger requests provided by FlexDanmark.
Each of these is discussed in more details in next subsections.
4.3.1 Travel‐time Map A complete travel‐time map for all of Denmark has been developed, based on GNSS measurements from more than 11,000 vehicles (mainly taxis and mini buses). There are more than 1.2 billion GNSS measurements. The GNSS have been map‐matched and a travel‐time for each segment has been computed for each 15 minutes of the day (96 values for each day) one for weekdays and one for weekends.
If sufficient data is not available for a segment (currently set to 10 GNSS measurements) a distribution algorithm using the following principles are used.
• Use data from the segment from all day data.
• Use data from similar segments within the time period considered.
• Use data from similar segments from all day data
• Use data from all segments the time period considered.
• Use data from all segments from all day data.
In conclusion, the REDUCTION project has sufficient data for point‐to‐point travel time for all addresses in Denmark.
4.3.2 Eco Map A complete eco‐map for all of Denmark based on CANBus data has been developed. Compared to the travel‐time map the CANBus data set available to the REDUCTION project is significantly smaller than the GNSS data set. The CANBus data set consists of approximately 40 million measurements from approximately 100 vehicles. The CANBus
27 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] data set is concentrated around the three Danish cites Aalborg, Herning, and Viborg (cities with between 50,000 and 150,000 inhabitants). In total only 2 % of the road segments have more than 10 measurements (there are CANBus measurements on 8 % of the segments).
The CANBus data set first got available to the REDUCTION project during the autumn of 2012. Since access to the CANBus data is very recent a significant effort has been put into cleaning the data (as part of the ETL process). The cleaning has been necessary because the CANBus data needs to be calibrated. The cleaning has revealed a number of issues with the CANBus data set such as:
• Vehicles could suddenly travel very large distances in very short time periods, e.g., 600 km in three minutes. Such data has been cleansed.
• Vehicles could report very fuel‐efficient driving, e.g., more than 67 km/l. Again such data has been removed by using the vehicle type (taxi, mini bus, and bus) and maximum km/l for each vehicle type.
• Vehicles only traveling short distance reported in some cases what seemed to be “weird” values. The reason can be that the engine is very cold and do not yet provide reliable CANBus measurements. Such data has been removed by only considering vehicles that have driven at least 10 km on a day.
The same approach as for the travel‐time map for dealing with insufficient data has been used, see the previous section.
4.3.3 Trips To evaluate the total travel‐time and total fuel consumption for a larger set of trips 54.106 trips from the month of November 2012 has been picked. These trips are all the trips from Region North Denmark scheduled by FlexDanmark.
FlexDanmark is doing transportation of sick and disabled persons. It is therefore vital to ensure the anonymity of the persons transport. To ensure anonymity the exact source and destination of trips are not used in the field trial. Instead, a zone approach is used such that a specific address is converted to a zone. The format is shown in Table 3. The latitude and longitude of the zone names are naturally know to the project. Denmark is divided into 13,000 zones. Within each zone the driving time is approximately below two minutes. This fine‐grained zone structure should ensure that the results from multiple trips are accurate compare to the actual trips driven.
From Zone To Zone Start‐Time Travel‐Time Total Price
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DRO705 ÅLB211 12:30 31 412.85
LØG020 ÅLB210 06:05 44 413.17
SEJ018 FAR100 07:55 55 482.00
ÅRS011 ÅRS206 09:00 10 120.50
Table 3 Zone Approach used for Trips (time in minutes, price in DK krone ).
4.3.4 Web Services Details The functionality of the prototype use is implemented using web‐services that are described in details in this section
(1) FlexDanmark Web Service The FlexDanmark web service has been developed to provide a feedback telling the time and distance of a route from A to B, at a given time of a given weekday.
The web service is developed as a restful stateless service, where all information required to compute the route is sent from the client using a HTTP request.
(a) Input The request from the frontend is a URL, with the following parameters:
• from_lat: Latitude position of the source. • from_lon: Longitude position of the source. • to_lat: Latitude position of the destination. • to_lon: Longitude position of the destination. • day_of_week: Weekday for route computation. • time_of_week: Time of day, to perform route computation.
(b) Output The output is a JSON object, with the following fields:
• fuel: String value, describing amount of fuel used for route in liters. • distance: String value, describing length of route in kilometers. • time: String object, describing the duration of route in the format ‘hh:mm:ss’. • error: String object, describing eventually errors occurred.
(c) Implementation The web service frontend has been developed and implemented by FlexDanmark and is running at FlexDanmark. Figure 1 shows the interface, where one enters a “from” and a “to”
29 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] address. The service then looks up the coordinates for the address, and when the user has chosen a weekday and a time of day they can submit the request to the backend service.
Figure 1. Screendump of FlexDanmark Web Service.
The output from the web service is the text labels at the lower part of the page, describing the time taken for traveling the route along with the distance.
(2) Speed Chart Web Service The speed‐chart web service has been developed to show the velocity of vehicles on a given segment in both directions during working days. It also describes the amount of data available and everything is displayed as a chart on Google Maps.
(a) Input The request sent from the frontend is a restful stateless URL with the following parameters:
• lat: Latitude position of the segment. • lon: Longitude position of the segment. • zoom: Zoom level of map, to help identifying the correct segment. • la: Language of output format, e.g., da for Danish or uk for English .
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(b) Output The output is a custom defined XML document, due to the Google Maps API does not have any specification for how charts should be defined.
Here is an example of an output retuned from the backend speed‐graph web service:
The XML output consists of several elements, with different attributes.
• MMCoordinate: Nearest map‐matched coordinate on a segment, determined from the input coordinate. • caption: Road name of the segment that is selected. • graphtype: tell Google Maps chart to smoothen the graph • speed1Name: Name of first series of speed values (first direction). • speed2Name: Name of second series of speed values (second direction). Only exists if segment is bi‐directional. • timeaxix: Name of the horizontal axis, describing period of the day. • speedAxix: Name of the first vertical axis, describing speed (to the left). • noDataAxix: Name of the second vertical axis, describing number of observations (to the right). • p: For every 15 minute of the day, the speed in both directions are plotted, along with the number of observations during this 15 minute interval. The noData attribute is synonym for observations.
(c) Implementation The frontend for the speed‐chart web service is developed by AAU. Figure 2 shows a screendump of the web service, where it can be seen, that a bridge is selected crossing the Limfjord in the Aalborg area.
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Figure 2 Screendump of Speed‐chart Web Service
The graph describes is the output to the end user, where the XML has been visualized. Three lines can be seen in the graph. A blue line describes the speed at a given interval in south‐ west direction (downwards), a purple line describes the speed in north‐east direction (upwards), and an orange line describes the number of observations available for both directions. For all three lines one measurement is available for every fifteen minute of the day.
(3) Eco‐Routing Web Service The eco‐routing web‐service has been developed to visualize the three kinds of optimal routes from A to B, namely the fastest route, the shortest route and the most ecofriendly route.
When choosing a route one must decide at what weekday the route is going to be planned for and at what time of the day. It is also possible to add additional via points for the trip
(a) Input The request from the frontend is a stateless restful URL request, with the following parameters:
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• lats: Semicolon separated list of latitude coordinates. When multiple via points on a route, this list determines the order of the points on a route. • lons: Semicolon separated list of longitude coordinates. When multiple via points on a route, this list determines the order of the points on a trip. • day_of_week: Weekday for route computation. • time_of_week: Time of day, to perform route computation. • type: What kind of measures to compute the shortest route for. This can be fuel, time, length, or a semicolon separated list of multiple options. • outtype: Determine the type of output from the backend. This can be either kmz or meta. When asking for kmz a kmz file is outputted to be visualized on Google Maps, and when asking for meta the meta data for a route is outputted. • la: Language of output format.
(b) Output Two kinds of output exists for the ecoRouting web service, depending on the outtype input parameter. If this parameter is kmz, a kmz document is returned with the optimal routes, for being visualized on Google Maps. If the outtype parameter is meta, a XML document is returned with meta data.
An example of a meta XML document can be seen here:
The XML output consists of a route, with a description of each point traversed on the route. For each point on the route, the values of the selected measures from input type parameter is available.
• route: The computed route. • point0, point1,..: The selected points traversed on a route. • time: Describes the fastest path in term of time. The amount of fuel used, the length and the time taken is returned as attributes on the element. • fuel: Describes the most ecofriendly path in term of fuel. The amount of fuel used, the length and the time taken is returned as attributes on the element.
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• length: Describes the shortest path in term length. The amount of fuel used, the length and the time taken is returned as attributes on the element.
(c) Implementation The webservice frontend has been developed at AAU. Figure 3 shows a screendump of the webservice. Here a computed route can be seen from A to B and the routes, along with the meta data, can be seen.
Figure 3 Screendump of EcoRouting Web Service
Three lines describe the three different routes. The red line shows the fastest route in term of time, which takes advantage of the faster speeds of the motorway. The blue line shows the shortest route in term of distance and this goes through the center of Aalborg. The green line describes the most eco‐friendly route and follows the blue route some of the way.
It can be seen from the popup box at Figure 3 that there is quite a difference in the amount of estimated fuel consumption on the three routes though.
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4.4 Description of the output
4.4.1 Use Scenario The scenarios that are studied in details in the field trial because there are alternative routes that are interesting to explore. In addition, the scenarios are in areas where both high‐ frequency GNSS and CANBus is available.
Figure 4 Shortest (blue), Fastest (red), and Most Fuel‐efficient (green) Routes
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The functionality requirements in D5.1 for the first FlexDanmark field trial are illustrated in
Figure 4. Here the shortest (blue), the fastest (red), and the most fuel‐efficient routes between two points in the Greater Copenhagen, Denmark area is shown. As can be seen there can be a significant difference between these routes.
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If the show details button in
Figure 4 is pressed a table as the following is shown
Jagtvej to Smedeland Time Distance Fuel
Fuel‐Efficient 0:22:33 13.90 km 1.48l
Fastest 0:18:05 22.41 km 2.41l
Shortest 0:20:54 13.33 km 1.50l
Table 4 Details for Specific Route
As can be seen from Table 4 there can be a significant difference in the distance, travel‐time, and the fuel‐economy for the route between two points.
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The prototype shown in
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Figure 4 works for all of Denmark and is available from the following URL http://daisy.aau.dk/its/. The Routes functionality is currently considered a beta version as shown in
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Figure 4 because the fuel‐economy is based on a small CANBus data set. The functionality is complete the beta status is only due to a lack of access to data.
4.4.2 Use Cases from D3.1 In Deliverable 3.1 (D3.1) a number of use cases are listed. In this section the implementation status of each use case is shortly explained.
• Load GNSS data.
o Fully implemented. GNSS data is automatically downloaded each night and added to the set of GNSS data. Approximately 1.8 million rows from ~3.000 vehicles are loaded every week day. In weekends these numbers are approximately half. The data warehouse can be updated in approximately 3 hours.
o GNSS data has been available to the project since its start. Therefore the cleaning of GNSS data in the ETL process is considered stable and well understood.
o The operator is warned when no data is present.
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• Load CANBus data
o Fully implemented. As for GNSS data, CANBus data is downloaded each night and added. There is approximately 0.5 million rows from ~100 vehicles for workdays and a third in weekends.
o The cleaning of the CANBus data major has required a major effort (in the ETL process) CANBus data first got available to the project during the Autumn of 2012 and understanding the details of cleaning the CANBus data is still ongoing.
o The operator is warned when no data is present.
• Update Basic Map (both a new map and update to existing)
o Fully implemented. Both a NAVTEQ map and an OSM map are supported. Multiple versions of the OSM are used.
o The OSM map is updated every 2‐3 months. Updating the map requires map‐ matching all GNSS data again. This process can be done over a weekend.
• Update Specialized Map
o This has not be implemented yet, because the map has not been available
• Load Passenger Requests
o Not implemented yet. This may requires access to person sensitive data and has therefore been postpone to the second field trial.
• Load Tours
o Implemented in an offline fashion, i.e., a combination of script running outside the data warehouse an Excel spreadsheet. Further studies will determine if a closer integration to the data warehouse solution is needed.
• Modify Basic Costs
o Implemented using Excel spreadsheet and data extracted from the data warehouse.
• Compute Coverage
o Fully implemented for both all four data types, {low‐frequent, high‐frequent} x {GNSS, GNSS+CANBus}
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• Annotate Data
o Fully implemented.
• Build Eco Map
o Postponed to the second field trial.
• Evaluate Single Trip
o Fully implemented.
• Evaluate Fleet Trips
o Implemented in offline fashion using scripts running outside the data warehouse and Excel spreadsheet. Further studies will show if a closer integration with the data warehouse solution is needed.
4.4.3 Business Goals This section describes if the business goals from D3.1 has been fulfilled in field trail 1 or has been postponed to field trial 2.
• Reduction of the GHG emissions from vehicles used in flex‐traffic o The most fuel‐efficient routes can be determined. o The fastest routes can be compared are to the most fuel‐efficient routes. o From the first field trial the basic building block for evaluating the GHG emissions from vehicles is therefore possible. A more detailed study is needed during the second field trial on how to convert fuel consumption (from CANBus data) to GHG emission numbers. • Establishing environmental profiles of vehicle types. o In the project there is at the end of first field trial sufficient CANBus data for establishing an environmental profile for mini buses (Mercedes Sprinter). o There is insufficient data for comparing make and models, e.g., compare a VW Passat to a Ford C‐Max. • Estimation of GHG emissions based on GNSS measurements. o Has been implemented using a single instantaneous model from D3.2. However, this will be studied further in the second field trial, if CANBus data cannot be used. • Estimation of the GHG emissions of a single vehicle. o Has been implemented using the fuel consumption as recorded in the CANBus data.
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In conclusion, the basic building block for estimating the fuel‐consumption/GHG emission from vehicles is available. However, a more detailed study involving multi‐modal transportation is needed in the second field trial.
4.5 Obstacles in running the field trial Here the obstacles and open issues after the first FlexDanmark field trial are listed. In addition, the risks and open issues from previous deliverables are considered.
4.5.1 Risks from D3.1 Here the risks from Section 6 in D3.1 are discussed.
• Insufficient GNSS data. This is not considered an issue!
• Insufficient CANBus data. Now there is access to sufficient CANBus data for a smaller geographical region. Within this region the field trials can be conducted. Therefore this risk is no longer a concern.
• Too large diversity in input data. This has been fixed using a plug‐in architecture. CSV is the preferred format for many of the data providers because it is the lowest common denominator. It has been a major implementation effort to build the plug‐in architecture. However, it now supports more than 10 different formats. This risk is therefore eliminated.
• No access to bus or train data. Still an issue. During the first field trial some bus data has become available. However, it has been too small a data set to use. It is estimated that sufficient data from busses should be available for the second field trial. This risk is therefore assumed to be very low. The case of lack of train data is not expected to have a major impact on the findings. This is because car and bus data will be available in excessive numbers and therefore they will be a solid basis for trustworthy statistical analysis, i.e., instead of relying on many (three, in our case) different type of data to filter out uncertainty, we will do this with the huge volumes of data belonging to less types (i.e., two).
• Significant decrease in fuel prices. Not considered realistic and therefore not considered a risk.
The only risk from D3.1 that is considered a risk currently is insufficient access to bus and train data. Train data is not available; however, CANBus data from busses have been made available and for the second field trial it is assumed that sufficient data is available to be able to make a more realistic evaluation of the effect of multi‐modal traffic.
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4.5.2 Open Issues from D3.3 Here the open issues from D3.3 are addressed in the order listed in D3.3.
• Many formats of CANBus. This has been handled by using only the fuel consumption. This metric is the most relevant measure here. This is therefore no longer an open issue.
• Parameters for the algorithms Time‐to‐Eco and Trajectory‐to‐Eco. Because larger quantities of CANBus data got available to the project during the first field trials. This issue has been postponed to the second field trial. Instead a major effort has been put into understanding, cleaning, and using the CANBus data.
• Weights for different algorithms. Again because CANBus data became available during the first field trials this has been postpone to the second field trial.
• Access to CANBus data. Not an issue any longer. Very hard work has been put into getting access to CANBus data for a reasonable large geographical area such that the field trials can be conducted.
• Converting from fuel to GHG emission. There are various conversion tables, e.g., http://www.epa.gov/cpd/pdf/brochure.pdf. This is therefore no longer considered an open issue.
4.5.3 Open Issues from D5.1 The following are open issues
• Estimate eco‐routes.
o Uses fuel consumption in the prototype and not GHG emission because the first number is available to the project the latter is not. Adding GHG emission to the prototype is fairly straight forward if such data should become available (or derived from the fuel consumption).
o The most/least/average fuel‐efficient driver has not been covered in details in the first field trial. This is mainly because driver information has not been available.
o This issue is therefore fixed with a single item postponed to the second field trial.
• Sufficient CANBus data and GNSS data. Sufficient GNSS data and sufficient CANBus data to make realistic estimates for a limited part of Denmark.
o It is assume that sufficient data from minibuses is available. The mini buses will be assumed to be the average vehicle.
o The issue is considered fixed.
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• Fast‐enough prototype
o Made use of Google’s infrastructure to present statically generated maps. Further considerable database tuning using partitioning of tables and index structures. The queries therefore currently run fast enough to be used for online queries. Platform is still running on moderate hardware.
o This issue is considered fixed.
• Outliers eliminated by cleansing CANBus data, e.g., avoid very short trips, e.g., movement of vehicles in garage complex.
o There has been a considerable effort in the ETL process removing so called dirty data. This is an ongoing process that will continue in the second field trials. However, it is estimated that the current cleaning process is sufficiently good to ensure that eco‐route estimates are reasonable accurate.
o This issue is partly fixed. Further cleaning the CANBus data may be needed in the second field trial.
4.5.4 Obstacles The obstacles addressed during the first field trials are the following.
• Limited CANBus data available and allowing distribution of data. Before and during the first field trial a number of visits to transport companies has been made and individual agreements with a number of smaller companies to gain access to CANBus data has been made. It has been necessary to promise that only aggregated results can be used in the field trials and only allowing access to CANBus data for a very limited set of persons.
• Demonstrate the possibilities with GNSS and CANBus data. To demonstrate the possibilities when having access to GNSS and CANBus, the software design from WP3 has very little focus on presentation of data to the user, i.e., a graphical user‐ interface (GUI). This has resulted in a thorough survey of appropriate techniques1. It has therefore been necessary to build a GUI for demonstration purposes. In addition, it has been necessary to make a number of database performance optimizations to the software back‐end to make the GUI respond fast to user inputs. The positive effect is that the software prototype now has a nice and responsive GUI (http://daisy.aau.dk/its) and that a very large number of users have tried out this
1 Marcus, A. Graphic Design for User Interfaces. In SIGGRAPH 93 tutorial notes (1993).
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GUI. The negative effect is that the first field trial has had little focus on multi‐modal transportation. The business goal “Reduction of the GHG emissions from vehicles used in flex‐traffic” has therefore not been sufficiently covered in the first field trial. However, this will be covered in details the second field trial. There is time for this in the second field trial because the efficiency of the final prototype already has been accomplished.
• Diverse set of GNSS and CANBus input formats. To gain access to GNSS and CANBus data it has not been possible to set any requirements to the data received from the data providers. To deal with this the software prototype has been made very flexible with respect to the input data. This has been realized by using data plugins where a plug‐in is made for each data provider. At the time of writing, 11 difference plugins have been created.
• Ensuring anonymity of persons transported. To avoid that it is possible to deduce which persons are being transported the set of trips are not exactly address to address but between zones. Denmark has been subdivided into approximately 13,000 zones.
4.6 Obtained Results
4.6.1 Single Trip For evaluating single trips four cases have been studied in details these will be discussed in details. The routes for the trips are shown in Figure 5. In all cases the fastest (red) route has been picked. The vehicle use is an Opel Zafira all driven by the same driver. The trips have been driven on different days during the Autumn/Winther of 2012/2013. The driving conditions have been in weekends or outside rush hours on mostly dry road and no snow. Full GPS tracks of the trips and the receipts from the fuel pumps are stored. The four trips are shown in Figure 5.
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a b
c d
Figure 5 a) Gistrup‐Billund, b) University‐Airport, c)Gistrup‐Hirtshals, d) Gistrup‐Aarhus
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Gistrup‐ Billund Time (hh:mm:ss) Length (km) Consumption (l)
Fuel 2:42:32 176.80 17.15
Fastest 2:04:54 208.70 21.10
Shortest 2:22:04 169.31 17.48
Table 5 Gistrup‐Billund: Fuel, Fastest and Shortest Details.
Billund‐Gistrup Time (hh:mm:ss) Length (km) Consumption (l)
Fuel 2:41:38 176.49 17.07
Fastest 2:04:11 207.86 21.01
Shortest 2:17:24 168.63 17.56
Table 6 Billund‐Gistrup: Fuel, Fastest, and Shortest
University‐Airport Time (hh:mm:ss) Length (km) Consumption (l)
Fuel 0:17:55 12.76 1.37
Fastest 0:15:05 14.06 1.49
Shortest 0:18:20 12.75 1.37
Table 7 University‐Airport: Fuel, Fastest, and Shortest
Airport‐University Time (hh:mm:ss) Length (km) Consumption (l)
Fuel 0:18:12 12.57 1.35
Fastest 0:14:19 13.54 1.45
Shortest 0:18:13 12.56 1.35
Table 8 Airport‐University: Fuel, Fastest, Shortest
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Table 5 and Table 6 show that there is a small difference in all three values estimated (time, length, and consumption). Table 7 and Table 8 show that the route out and home can have different values. In particular is the travel‐time out for the fastest route estimated to be 15:05 while the travel‐time home is estimated to be 14:19. The Gistrup‐Hirtsthals and Gistrup‐ Aarhus routes are similar to the Gistrup‐Billund details and therefore not included.
The overview estimated and actual fuel consumption (at the gas pump) is shown in Table 9 with the following comments. For the Gistrup‐Billund route the average fuel‐consumption at the gas pump is shown for a return trip (Gistrup‐Billund‐Gistrup). For the University‐ Airport route the average fuel consumption at the gas pump of driving 6 return trips (University‐Airport‐University) is reported. This is because the route is shorter than the other routes considered. For both the Gistrup‐Hirtshals and the Gistrup‐Aarhus routes in the fuel consumption of the return trip at the gas pump that is reported.
Trip (no trips) Estimated Fuel (l) Gas Pump (l) Difference %
Gistrup – Billund (2) 42.02 32.88 21.75
University – Airport (6) 2.94 2.41 18.03
Gistrup ‐ Hirtshals (1) 15.65 12.64 19.23
Gistrup – Aarhus (1) 25.42 20.80 18.17
Table 9 Estimated Fuel Consumption and Actual Fuel Consumption
As can been seen from Table 9 the estimated fuel consumption is 18‐22% higher than the actual fuel consumption (at the gas pump). This is expected because the actual vehicle used in a 2002 Opel Zafira petrol driven car and the estimated fuel‐consumption is based on CANBus data from Mercedes Sprinter diesel minibuses. It is encouraging that the difference is quite stable.
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4.6.2 Multiple Trips Having looked at single trips the study is not turned to what the consequences of switching from optimizing from the fastest route between destinations to optimizing for the most fuel‐ efficient route will have of impact on the price that FlexDanmark’s customers will have. For this purpose all the trips from the month of November 2012 for Region North Denmark is used.
FlexDanmark’s cost per minute for is 9.85 DDK/minute. It is assumed that only diesel vehicles are used and that the diesel price is 8.75DKK/liter (exclusive VAT). The total cost for all trips in minute prices and fuel prices is shown in Table 10. The total number of kilometers driven is also listed.
Fastest Units Unit Price (DKK) Price (DKK)
Travel Time (m) 1,071,464 9.65 10,339,627
Fuel (l) 117,025 8.75 1,023,969
Distance (km) 1,141,118
Table 10 Price and Fuel Consumption Fastest Routes
Table 11 lists the same information as Table 10 when optimizing for the most fuel efficient routes.
Fuel Efficient Units Unit Price (DKK) Price (DKK)
Travel Time (m) 1,186,050 9.65 11,445,383
Fuel (l) 104,097 8.75 910,849
Distance (km) 1,111,976
Table 11 Price and Fuel Consumption Fuel‐Efficient Routes
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The minute price will increase by 10.7 % and the fuel will decrease by 11.0 %. However in actual prices the minute price is increased by 1,105,756 DKK and the drop in fuel price is 113,123. The saving in fuel can simply not outweigh the increase minute price (there is almost a fact 10 in between). Note that the average fuel consumption for the fastest routes is 9.75 km/l where as the fuel consumption for the most fuel‐efficient routes is 10.68 km/l. This is considered a significant saving.
Based on this it may be very hard to the FlexDanmark customers to accept this extra price for driving the most fuel‐efficient routes. However, it may be possible to use a hybrid approach where the most fuel‐efficient routes are used in city zones and the fastest routes are used in rural zones. Such as hybrid approach will also partly deal with the issue that the most fuel‐efficient routes in rural areas may be considered less safe because the most fuel‐ efficient routes tend to main roads (that are not motorways) whereas the fastest routes in general follow the motorways. The effects of such a hybrid approach will be studied in the second field trial.
4.6.3 Business Goals This section must conclude if the business goals from D3.1 has been fulfilled in field trail 1 or has been postponed to field trial 2.
• Reduction of the GHG emissions from vehicles used in flex‐traffic o Can determine the most fuel‐efficient routes o Can evaluate how close the fastest routes are to the most fuel‐efficient routes • Establishing environmental profiles of vehicle types. o In the project there is at the end of field trial 1 sufficient data for establishing an environmental profile for minibuses. o There is insufficient data for comparing make and models, e.g., compare a VW Passat to a Ford C‐Max. • Estimation of GHG emissions based on GNSS measurements. o Has been implemented using a single instantaneous model from D3.2 • Estimation of the GHG emissions of a single vehicle. o Can compare vehicles.
4.7 Trustworthiness of obtained results It has not been possible to find actual fuel numbers from other project to compare the result presented here for the first FlexDanmark field trial. Therefore the numbers presented here should not be over interpreted. However, it is encouraging that the results presented in Table 9 are consistent.
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4.8 Planning for Phase‐2 of the Field Trial Compared to the Task 5.5 in the DOW the following issues will also be addressed in the second field trial.
• Multi‐modal using busses and mini‐busses/taxis will be covered in details. • The set of trips will be large and cover a larger geographical area, ideally the second field trial should look at one year of trips from FlexDanmark covering all of Denmark. • Implement eco map. • Convert GNSS measurements to GHG emission numbers if needed. If enough CANBus data is available the fuel consumption from CANBus data will be used instead. • Parameters for the algorithms Time‐to‐Eco and Trajectory‐to‐Eco must be considered (open issue from D3.3 not fixed yet) • Weights for different algorithms must be tested (open issue from D3.3 not fixed yet). • Convert fuel consumption to GHG emission (open issue from D3.3 not fixed yet) • Find the least/most/average fuel consumption for driver (open issue from D5.1 not yet fixed). If not driver information is available a vehicle will be assume to only be driven by one driver. • Hybrid optimization model, i.e., using fuel‐efficient routes in city zones and fastest routes in rural zones.
4.9 Suggested Changes in REDUCTION’s architecture and methods During the first field trial no major issues have been discovered in the architecture or methods proposed in WP3. Naturally a number of minor issues have been discovered. These are the following.
• The plugin‐architecture to support many different formats of GNSS and CANBus data. • The physical database tuning using partitioned tables and additional indexes to ensure fast response time of the most used queries. • The computation of multiple trips has been moved from the data warehouse to offline python scripts and Excel spreadsheet. Spreadsheets have been used because of the flexibility that these have and that business users are typically very familiar with spreadsheets.
4.10 Conclusions In the first field trial large quantities of GNSS and CANBus data has been used to compare the fastest and most fuel‐efficient routes. This has been done for both a canonical set of trips and a large set of real‐world trips.
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The first field trial uses as much GNSS and CANBus data sets then first envisioned and has a more polished graphical user‐interface than planned. Further, there has been a considerable effort in performance tuning the data structures and methods to provide the users with a responsive user interface. There has been a huge interest from the public in using the interface with estimated more than 25,000 unique visitors to the web site in December 2012.
The large effort in providing a user‐friendly and responsive GUI was planned for the second field trial. However, this was moved forward to accommodate interaction with transport companies and to support a large number of users. The change in schedule has cause that the first field trial has only look at the basic of multi‐modal transportation. Multi‐modal will have a must larger focus in the second field trial. Thus the same functionality will be provided in the software prototype as described in WP3, however, the order in which the functionality is provided has been changed.
The preliminary results from the first field trials are encouraging; however, a more detailed study of the estimated fuel consumption is needed. One should therefore be careful not over interpreting the results presented here.
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5. CTL Cyprus Fleet Field Trial: Phase‐1 and Phase 2 Plan
5.1 Introduction Main objectives: 1. Demonstrate the Delphi Delco Electronics GMBH (DDE) vehicle‐to‐vehicle and vehicle‐to‐infrastructure (V2V/V2I) device capabilities 2. Demonstrate a fuel efficiency and/or emissions reduction through a driver bus and delivery fleet monitoring system using the REDUCTION technologies: a. Read, store and send to a server CANbus data b. Analyze the driving pattern of the OSEL bus and CPO fleet drivers c. Develop a bus drivers’ eco‐guide to reduce fuel consumption and emissions d. Validate the drivers’ eco‐guide at a field trial. The original fleet trial that would have started in October 2012 has been delayed and is expected to start in August 2013 due to: 1) the REDUCTION consortium could not reach into an agreement with Mercedes‐Benz in assisting DDE to read the CANbus data from the Citaro Mercedes‐Benz buses operated by OSEL; 2) A diagnostic test conducted on the 6th of March, 2013 to read the data by sniffing the data via the wiring system of the OSEL buses was not successful.
Main changes to the original Nicosia, Cyprus field trial
Given these obstacles, CTL during the April of 2013 identified one fleet operator company ‐ Cyprus Papaellinas Organization (CPO) ‐ that was willing to participate in REDUCTION. CPO was chosen due to the fact that they were getting ready to install their own fleet management system for their fleet that had the capability of extracting CANbus data including GPS location and speed as well as fuel consumption. CTL informed the REDUCTION consortium that this was a potential change in the field trial in the month of May 2013 and provided further assurances at the Volos, Greece meeting on the 9‐10th of July 2013. CTL in cooperation with Istognosis Ltd. (IST), the system integrator and operator of the fleet management system for CPO, met with CPO in mid‐July and obtained permission to utilise the data that can be collected by the CPO fleet management system to develop fuel efficient driving patterns for their drivers.
Given these changes, the Cyprus fleet field trial is expected to be carried out on two different fleet types:
1) The Nicosia OSEL bus driver behaviour field trial will be conducted to test the capabilities of the DELPHI MyFI V2X/CCU devices in retrieving CANbus fuel
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consumption, GHG emissions and GPS vehicle location and speed data, storing them on its solid state disk and disseminating this data remotely to a server for post processing. The bus field trial is conditional on the ability of the MyFI devices to read the Citaro Mercedes‐Benz CANbus data.
a. A preliminary test that was conducted in March 2013 was not successful in reading the CITARO bus CANbus data.
b. DDE in cooperation with CTL will conduct one more test using the CITARO bus and try to read the CANbus data via the buses’ fleet management port in August 2013. If the second bus trial is not successful then the 5 MyFi devices will be installed at 5 fleet vehicles of the CPO company.
2) The Cyprus CPO fleet field trial will involve up to 68 delivery vehicles from the company CPO with the support of IST. A selected set from these 68 vehicles will be utilised by CTL and its REDUCTION partners to analyse the driving behavior of the drivers to develop fuel‐efficient driving guidelines for the CPO drivers. A preliminary data collection will start in August 2013 and is expected to continue until February 2014 for both phases of the field trial. As stated earlier, if it is found that it is not feasible to read the CANbus data from the OSEL Citaro buses then the five MyFI devices will be installed at five CPO vehicles such that the objectives set for the field trial are fulfilled. Further, the REDUCTION consortium will decide whether to extend the field trial beyond February, 2014 as necessary.
5.2 Nature of the fleet driver behavior field trial Bus Driver Behavior Field Trial
The bus field trial will involve five OSEL Citaro Mercedes‐Benz buses each of which will be equipped with an DDE MyFI V2X/CCU device to read, store and disseminate fuel, GHG emissions and GPS vehicle location/speed data. A naturalized study will be conducted where the drivers will follow their regular routes as they normally do without any influence from the devices. The DDE MyFI devices will gather all data in an automated way and they will not influence the driving task in any form ‐ a precondition set by the OSEL management.
Preliminary diagnostic test on the OSEL buses, 06‐03‐2013, Nicosia, Cyprus. A preliminary test was conducted by DDE and CTL on the 6th of March 2013 using two CITARO OSEL buses. Given that Mercedes‐Benz could not provide any assistance in the reading of the CANbus data DDE and CTL tried to read the data through the “sniffing” method using the wiring system. Unfortunately, whereas various messages could be read it was not possible to utilise them for any purpose as it proved to be difficult to decipher them from their original
55 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] protected format. DDE, based on the diagnostic test, informed CTL that it will be worthwhile to try one more time to read the data through the Citaro bus fleet management port ‐ this port was discovered to be available during the diagnostic test, yet it is not known whether it will produce any data messages from the CANbus. This second diagnostic test is expected to take place in August 2013. If this second trial is successful then the first phase will start as early as September, 2013 and the second phase in November, 2013 and be completed by January 2014.
Geography of the OSEL Bus Field Trial
Five OSEL Citaro Mercedes‐Benz buses will be selected from either from the first or the second group of OSEL bus routes as outlined below. These bus routes have been selected to achieve joint coverage by three different routes of either the Archbishop Makariou Ave. or the Strovolos‐Severi Avenues. A final decision on the bus routes/buses that will be selected for installation is expected to be reached during the second diagnostic test of the DELPHI MyFI V2X/CCU device that will be carried out in August, 2013, during which it will be determined whether the bus field trial will or will‐not be carried out based on the results of the second diagnostic test. The following bus routes have been tentatively selected that jointly cover partially the Nicosia Archbishop Makarios Avenue: • Route 116 (Fig. 6): ΣΥΝΟΙΚΙΣΜΟΣ ΣΤΡΟΒΟΛΟΥ 3 - ΛΕΩΦ. ΚΕΝΝΕΤΥ - ΛΕΩΦ. ΜΑΚΑΡΙΟΥ - ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=116 • Route 158 (Fig. 7): ΣΤΑΘΜΟΣ ΚΡΥΟΝΕΡΙ / ΠΕΡΑ ΧΩΡΙΟΥ ΝΗΣΟΥ - ΓΕΝΙΚΟ ΝΟΣΟΚΟΜΕΙΟ - ΛΕΩΦ. ΜΑΚΑΡΙΟΥ - ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=158 • Route 160 (Fig. 8): ΓΕΡΙ - ΓΕΝΙΚΟ ΝΟΣΟΚΟΜΕΙΟ - ΛΕΩΦ. ΜΑΚΑΡΙΟΥ - ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=160
Alternatively the following bus routes covering partially the Strovolos Avenue maybe selected instead: • Route 110 (Fig. 9): ΠΑΝΩ ΛΑΚΑΤΑΜΕΙΑ ‐ ΣΥΝΟΙΚΙΣΜΟΣ ΑΣΠΡΕΣ ‐ ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=110 • Route 112 (Fig. 10): ΤΣΕΡΙ / ΣΑΛΑΜΙΝΟΣ - ΛΕΩΦ. ΤΣΕΡΙΟΥ - ΛΕΩΦ. ΣΤΡΟΒΟΛΟΥ - ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=112 • Route 157 (Fig. 11): ΣΤΑΘΜΟΣ ΛΕΩΦΟΡΕΙΩΝ ΑΡΕΔΙΟΥ ‐ ΑΝΑΓΥΙΑ ‐ ΔΕΥΤΕΡΑ ‐ ΛΕΦ. ΣΤΡΟΒΟΛΟΥ ‐ ΠΛΑΤΕΙΑ ΣΟΛΩΜΟΥ, http://www.osel.com.cy/?wp=routedetails&route=157
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Figure 6. OSEL Bus Route 116 Figure 7. OSEL Bus Route 158
Figure 8. OSEL Bus Route 160 Figure 9. OSEL Bus Route 110
Figure 10. OSEl Bus Route 112 Figure 11. OSEL Bus Route 157
Geography of the CPO fleet field trial
CPO operates a fleet of 120 vehicles that cover most of the main cities in Cyprus. The focus of the CPO field trial will be the greater Nicosia, Cyprus region. However, the final decision
57 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] will be taken during the preliminary data collection of August 2013, in cooperation between CTL, IST, CPO and the REDUCTION consortium partners.
5.3 Plan for Carrying out the Cyprus Fleet Driver Behavior Field Study (Revised) The Nicosia OSEL bus driver behavior field trial will take place from the first of September 2013 to the 15th of February 2014. The main phases of the bus field trial are:
Phase I – Cyprus Fleet Field Trial Using Existing Driving Patterns
Completed ‐ DDE MyFI V2X/CCU First Diagnostic Test (2013‐03‐05 – 2013‐03‐07)
DDE and CTL conducted a diagnostic test on two CITARO Mercedes‐Benz buses on the 6th of March, 2013. The diagnostic test included a detailed trial to sniff out messages from the CANbus wiring system.
OSEL allocated one bus and a driver for a few hours in the morning of the 6th of March 2013. DDE found out that the messages were encrypted which were difficult to convert to real data. During this test it was revealed that the CITARO buses were equipped also with a fleet management port that was not activated, which is used to hook‐up the corresponding CITARO Mercedes‐Benz fleet management system. In the afternoon of the same day OSEL allocated one more bus, one driver and one employee from their maintenance team to examine whether there was a way to read the CANbus data in a usable form – the maintenance team utilises a third party software for the maintenance of the Citaro buses. DDE examined the main components of the maintenance software and gathered various data streams. This second trial also failed as the first guess that Mercedes‐Benz is using its own encrypted system was further confirmed. DDE decided that our last chance of reading the data was from the fleet management system of the CITARO buses.
Alternative Fleets to carry out the REDUCTION fleet trial study in Cyprus
Given the setback with the Citaro buses, CTL and DDE decided that we needed to find an alternative fleet as a back‐up to carry out the Nicosia fleet trial. CTL contacted Travel Express (an intercity taxi/minivan) company; the Cyprus Papaellinas Organization (CPO) fleet operator ‐ Istognosis Ltd informed CTL that CPO was about to purchase and install a fleet management system; the EMEL Limassol bus company as potential alternative. The CPO was chosen as the best alternative since they were operating a large fleet while they were installing a new FMT.
Alternative Fleet Solution Chosen ‐ Cyprus Papaellinas Organization (CPO) Fleet Operator
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IST informed CPO in May 2013 that CTL made a request to utilise their fleet management system ‐ once installed ‐ for the purposes of REDUCTION and specifically for the development of bus fuel consumption driver profiles. IST during the months of May, June and July kept on updating CTL on the progress of the installation of the CPO fleet management system. IST also informed CTL in early July that CPO was looking favorably towards a potential cooperation with REDUCTION. As such CTL confirmed to the REDUCTION consortium at the Volos, Greece meeting (2013‐07‐09‐10) that an alternative company has been found which could be used for carrying out the fleet field trial regardless of the outcome of the second diagnostic test using the OSEL buses. CTL subsequently met with CPO and IST and received a verbal agreement from CPO at a meeting conducted at the offices of CPO in mid‐July, 2013.
CTL proceeded to confirm to DDE that indeed we could utilise the CPO delivery company as a back‐up – and install the five MyFI devices ‐ in case the second diagnostic test using OSEL Citaro Mercedes‐Benz buses fails in August 2013.
Completed: CPO Fleet Management System Installation by IST (2013‐07)
IST completed the installation and fine tuning of the CPO Fleet Management System on 68 delivery vehicles operating throughout Cyprus in early July 2013 – this activity was conducted independent of REDUCTION.
IST demonstrated to CTL a preliminary stream of data that the fleet management system (FMT) was collecting and storing in real time in the third week of July, 2013. The FMT system produces automated real time reports as follows: 1) Data from each vehicle are transmitted at one minute time intervals using the MTN wireless network from each vehicle to a central server (owned and operated by IST), 2) Vehicle trip fuel consumption, 3) Vehicle GNSS data (location and speed) recorded at 10 sec. time intervals, 4) Drivers’ sudden acceleration alarms above a certain default threshold (recorded per trip), 5) A five stage driving scoring system where A is the best and E is the worst (recorded per trip).
It was determined that additional data would be needed to carry out the REDUCTION fleet trial as the existing software was producing only aggregated data on fuel consumption on a per trip basis. The main objective of the Nicosia field trial is to develop fleet drivers’ driving profiles using fuel consumption and develop fuel efficient driving guidelines per driver. It is therefore necessary to gather data on a link by link basis, which will be used to develop the driving profile of each driver. CTL requested from IST to gather also the following additional data: fuel consumption, GNSS and GHG emissions at 2 sec. time intervals on a link by link basis.
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IST informed the FMT hardware/software vendor and requested the API software to interface directly with the CANbus of the vehicle via their fleet management unit. CTL agreed to purchase this API for a cost of $1000 (756 euro). In addition, CTL and IST are in negotiations with MTN – one of the cellular carriers operating in Cyprus to purchase an appropriate data plan for (up to 68) the CPO equipped vehicles and the five OSEL buses for a time period of six months. The cost for the data plan will be partially covered by CTL in order not to burden CPO with an additional cost ‐ their current data plan is at one minute time intervals, whereas we need data to be streamed every few seconds, with two seconds being the minimum plan that MTN can provide.
Planned second diagnostic test on the OSEL bus using the MyFI technology (Note: We wil revise this as soon as we manage to conduct the test by the end of August) (2013‐08‐10 to 31)
DDE has developed a connector that is believed to be compatible to the port of the fleet management system of the CITARO buses. This connector will be tested by CTL during the second week of August 2013. DDE will send this device to CTL during the first week of August.
If this second trial is positive then DDE will install the five MyFI devices at the OSEL Citaro buses. Otherwise, the back‐up option will be followed, by installing the devices at the CPO fleet vehicles.
In parallel, CTL at the request of DDE purchased at the end of July, 2013 a set of six antennas (cost, 505.2 euro) – MobileMark Mag Mount Antenna c/w 4.5m Cables & SMA, “MGW‐301‐C23‐C23‐B23‐4” with a range between 800MHz to 2700 MHz, which covers the cellular frequencies of MTN 900/1800 (2G) and 2100(3G) ‐ that will be needed to send and receive messages via the 5 MyFI devices via the MTN 3G wireless network.. In addition, these antennas will be used for the V2V and V2I communication test.
Diagnostic Test on the CPO delivery vehicles (we will revise this based on what we will be able to achieve by the end of August) 2013‐08‐01 to 30
The following actions will be undertaken during August 2013: 1) Purchase and install the API to interface the CPO fleet management system to the vehicle CANbus for real‐time data streaming; Finalize the data that will be retrieved from the CANbus; 2) Select and purchase the appropriate data plan from the MTN company (2, 5 or 10 second frequency); IST currently operates on the one‐minute frequency plan of MTN for the FMT of the CPO; 3) Conduct a set of tests on all delivery vehicles to verify that each vehicle is sending CANbus data to the server and are properly stored at the server, 4) Send a set of CANbus data to UTH and UHI for preliminary analysis.
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Existing driving pattern study ‐ Development of Driver fuel consumption driving profiles on a link by link basis without providing feedback to the drivers (2013‐09‐01 to 2013‐11‐09)
Development of fleet drivers’ (OSEL, CPO) fuel consumption profiles (2013‐09‐01 to 2013‐11‐09)
Under this eight week time period bus CANbus data will be retrieved via the utilization of five DDE MyFI devices that will be installed on five OSEL Citaro Mercedes‐Benz buses. During this period the profile of each driver using fuel consumption will be estimated.
The analysis of the eight week data will be conducted during this time period, which will define the drivers’ driving profiles for: vehicle speed, fuel consumption, and emissions GHG. Given these driving profiles efficient driving patterns will be identified based on the above parameters, which will then be used to develop driving guidelines that will be then presented to OSEL and its drivers.
Development of training guidelines (2013‐10‐21 – 2013‐11‐15).
During this period, a set of guidelines will be developed for each driver based on their fuel consumption driving profiles. The guidelines will be presented in the form of “association rules”, i.e., if head then body. For instance, if “sudden acceleration” then “a quantitative/qualitative description of consumption”. This form is easily understood, it is a quite generic way to present reason and result, and it does not constitute an offense to the driver. The REDUCTION partners will present these guidelines to the OSEL and CPO management for fine tuning and approval. Once the guidelines are finalized then the OSEL and CPO management will present them to their drivers and urge them to follow them for an eight week time period. This is perceived as a necessary and sufficient interval for the drivers to familialize themselves with the rules, and put them in practice for a quite long time. It is the time interval that the drivers think they get familiar with a specific vehicle’s behaviour, or with a specific route.
IST in cooperation with CTL will try and modify the default values of the CPO fleet management system if necessary and trigger an alarm if they exceed a certain threshold (e.g. sudden acceleration, excessive non‐fuel efficient speed, other) – this is optional as we may not be able to change the current default values within the software. It is noted that the CPO fleet management system itself produces a driving performance grade (divided in five grades from A to E (E being the worst performance)) for each driver based on their driving behavior using the data recorded on sudden acceleration and fuel consumption.
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The time period allocated for the development of these driving profiles maybe extended for an additional month based on the analysis that will be conducted continuously by the REDUCTION consortium.
Phase II – Proposed Driving Patterns Cyprus Fleet Field Trial (2013‐11‐18 – 2014‐02‐15) Proposed Driving Guidelines Study ‐ Data collection and analysis for the fleet driver behavior field study based on REDUCTION proposed driving guidelines (2013‐11‐18 – 2014‐01‐18).
During this eight‐week follow‐up time period, the OSEL and CPO drivers will be requested to follow the driving guidelines that will be produced during the first time period. The analysis of the data will be conducted continuously by the researchers of the REDUCTION consortium. The REDUCTION partners may opt to provide periodic guidelines to the drivers upon the approval of OSEL and CPO management if they deem it necessary based on new analysis data.
CTL and IST will provide weekly raw data to UTH and UHI throughout the field study (or as otherwise decided).
This eight week time period may be extended by one month if it is deemed necessary by the REDUCTION partners including OSEL, IST and CPO.
Cyprus fleet field trial of V2V and V2I communications (2014‐01‐15 – 2014‐02‐15)
Given that only the DDE MyFI devices have the capability to communicate in real time with each other, it is proposed that a controlled field study be conducted using five vehicles. Under this test various schemes of V2V communications may be designed and executed to test the capability of the MyFI devices to communicate with each other in real time. In parallel, the corresponding V2I communications will be tested – send and receive data from/to MyFi to/from server. The five drivers will be requested to perform specific driving tasks within a closed corridor such as headway between vehicles, vehicles operating at crossing roadways, vehicles operating at parallel roadways, impact of corner buildings.
Fleet Field Trial Feedback Questionnaire (2014‐01‐15 – 2014‐02‐15)
CTL will prepare a questionnaire for the main stakeholders of the field trial that will be based on the results of the field trial and the general concept of eco‐routing and eco‐driving: OSEL management, CPO management, IST system integrator, OSEL and CPO drivers and the Cyprus Public Works Department. CTL will summarize the results of the questionnaire in a Technical Memorandum.
2014‐01‐15 – 2014‐03‐15. Summary of the analysis and Final Report
A Final Report will be prepared and submitted to the REDUCTION consortium for review and approval.
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2014‐04 Dissemination Seminar to OSEL, CPO and the Cyprus Public Works Department
CTL in mid April, 2014 is expected to host the REDUCTION Consortium partners in Cyprus. During this meeting, a seminar is proposed to be conducted where the results of the fleet trial will be presented to the OSEL and CPO management and the Cyprus Public Works Department. The agenda for this seminar will be decided at a later stage of the project at one of the REDUCTION meetings.
5.4 Main Responsibilities of the Cyprus Fleet Field Trial • CTL Cyprus Transport Logistics Ltd (CTL) (Nicosia, Cyprus) o CTL will organize and administer the Nicosia field trial of OSEL and CPO o CTL will assist in the development of the drivers fuel consumption driving profiles o CTL with the assistance of the REDUCTION partners will prepare the Nicosia field trial reports • OSEL ‐ Transportation Organization of Nicosia District (Nicosia, Cyprus) OSEL is the main bus company that operates in Nicosia district. It operates 25 bus lines using brand new buses of type CITARO MERCEDES‐BENZ 12m with engine (KW) 260, and 345 HP. o OSEL will assist the REDUCTION partners in the field trial through the allocation of 5 Citaro Mercedes‐Benz buses o OSEL will assist the REDUCTION partners in carrying out the field trial o OSEL will provide feedback on the results of the field trial • Ministry of Communication and Works Public Works Department (MCW‐PWD) (Nicosia, Cyprus). The MCW‐PWD will assist OSEL and the REDUCTION partners in defining the Nicosia field trial and provide feedback on the potential benefits of the REDUCTION technologies for OSEL and for the Cyprus public. • Delphi Deutschland Delco Electronics GMBH (DDE) (Wuppertal, Germany) o DDE will supply five MyFI devices that will be installed at the OSEL buses or CPO fleet vehicles o DDE will prepare the interface software to read the CANbus vehicle data o DDE will assist in the installation of the MyFI devices in the buses and the CPO fleet vehicles (if necessary) o DDE will provide assistance throughout the trial o DDE will assist in the preparation of the final report of the Nicosia field trial
• Stiftung Universität Hildesheim (UHI) (Hildesheim, Germany) o UHI as the Project Manager of REDUCTION will oversee the Nicosia fleet operations field trial. o UHI will conduct a statistical analysis to analyze the driving patterns of the bus drivers and develop guidelines to reduce fuel consumption. • Panepistimio Thessalias (UTH) (University Of Thessaly) Volos, Greece o UTH is the leader of the WP5 and will oversee the preparation and execution of the Nicosia fleet operations field trial.
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o UTH will lead the execution of the V2V/V2I communications Cyprus field trial. • Istognosis Ltd. (IST) (Nicosia, Cyprus) new participant ‐ IST is the system integrator and operator of the Cyprus Papaellinas Organization (CPO) (CPO) fleet management system. o IST will assist CTL in the carrying out of the CPO fleet trial o IST will install the API software to extract data from the CPO delivery vehicles CANbus and send them to the IST server o IST will assist in the training of the CPO drivers to follow the fuel efficient guidelines that will be developed by the REDUCTION consortium • Cyprus Papaellinas Organization (CPO) (Nicosia, Cyprus), new participant: CPO operates a fleet of 120 delivery vehicles that serve to deliver its products throughout Cyprus. Recently through the aid of IST CAP has installed a fleet management system at 68 vehicles of its fleet with the aim to improve the driving record both in terms of safety and fuel efficiency. o CPO will provide up to 68 vehicles equipped with its fleet management system to be utilised for REDUCTION o CPO will assist the REDUCTION partners in carrying out the fleet trial via the IST system integrator o CPO will provide feedback on the results of the field trial.
5.5 Specifications of the Cyprus Fleet Trial Technologies
5.5.1 DELPHI MyFI V2X/CCU Technical Data Summary On board features:
• Industrial grade hardware compliancy • Standard x86 architecture INTEL® ATOM 1GHz with ext. temp. range ‐40°C to ~85°C • Onboard 1GB DDR2 RAM • Onboard 4GB Solid State Disk • 1x DSRC radio • 1x GPS (Fastrax) • antenna setup: 1.1 dual‐antenna support for DSRC 1.1.1 (can be reconfigured for standard 802.11a/b/g/n WLAN) 1.2 Enclosure prepared for multiple antennas (e.g. GPS, DSRC, 3G/4G/LTE) • wide‐range power supply (8 ~ 32V, 20W) • internal protection against wrong polarity on connector • customized enclosure for enhanced heat dissipation • automotive grade connectors • Operating System:
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1.3 customized & ruggedized DELPHI‐blend Linux 1.4 customized & ETSI compliant 11p stack and drivers Exposed interfaces (via automotive connector):
• 1x IEEE 802.3 ETHERNET (10/100 Mbit) • 1x USB2.0 • 1x CAN (High‐Speed) • power supply lines (PWR, GND, IGN)
5.5.2 DDE MyFI Installation Requirements
The DELPHI MyFI V2X/CCU devices will be connected to the CANbus of the Citaro Mercedes-Benz through the OBD port.
Hardware needs: • Five OSEL Citaro Mercedes‐Benz buses or • Five CPO delivery vehicles • Five DELPHI MyFI V2X/CCU devices • Five hard disks of 10 TB each (one for each bus) • One PC server to receive the wireless data from the five DELPHI MyFI V2X/CCU devices • Multi‐meter (voltage/amperes/resistance) • Oscilloscope (optional) • Soldering iron and solder • Pliers, knife, isolation tape • 0.8 sqr‐mm copper‐wire
Software installation needs: • DELPHI MyFI V2X/CCU device customized software to read the CANbus data from the Citaro Mercedes‐Benz buses and send them • Data plan with the MTN cellular telephony company operating in Cyprus
Personnel needs: • DDE: DELPHI MyFI V2X/CCU device expert • OSEL: five bus drivers, one manager • CTL: One manager, one IT expert • UTH: One Communications scientist • UHI: Project Manager, One data mining expert • MCW‐PWD: One official representative
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5.5.3 Costas Papaellinas Organization (CPO) fleet technical summary CPO Fleet Characteristics • Fleet: 120 Cars • Installed GPS with OBD Reader: 68 Cars • Delivery Cars: 8 CPO Fleet Management System • OBD Guard • OBD‐II Reader (Castel Group) • Optional GPS module (uBlox) • GSM/GPRS module (Telit)
Figure 12. CPO OBD Guard
• GSM/GPRS Specification • GSM module Telit • GSM/GPRS Quad band 850/900/1800/1900Mhz • Communication protocol Embedded TCP/IP protocol • GPS Specification • GPS chipset SIRF Star III u‐Blox • Channels 20 • Receiver frequency 1575.42MHz • Cold start approx 42s • Warm start approx. 38s • Hot start approx. 1s • Antenna Built‐in ceramic antenna • Protocol Supported for ODB‐II • J1850‐VPW, J1850‐PWM, KWP2000, ISO9141‐2, CAN‐BUS • Features • OBD‐II compliant.
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• Integrated GSM/GPRS, GPS optional. • Real‐time vehicle diagnostic for monitoring and reporting. • Integrated 300 hrs of data storage. Data is stored on remote servers. • Built‐in 3‐axis acceleration G‐Sensor module for towing alarm. • Alarms can be send via SMS. • User access via USB or via web platform. • Alarms • Speeding alarm setting • Hard acceleration alarm setting • Hard braking alarm setting • Temperature alarm setting • High RPM alarm setting • Low‐voltage alarm setting • Extended Engine Idling alarm setting • Quick change line alarm setting • Sharp turn alarm setting • PIDs • Engine Coolant Temperature( ) • Engine RPM(rpm) • Vehicle Speed Sensor(km/h) • Mass Air Flow Sensor(g/s) • Calculated Load Value(%) • Intake Manifold Absolute Pressure(kPa) • Intake Air Temperature( ) • OBD Require To Which Vehicle Designed • Distance Travelled While MIL Activated(km) • Fuel Rail Pressure(gauge)(10kPa) • Commanded EGR(%) • Fuel Level Input(%) • Barometric Pressure(Kpa) • Accelerator Pedal Position D(%) • Accelerator Pedal Position E(%) • Software • API to read CANbus data from the OBD port • Wireless Communication Data Plan • The MTN 3G network will be utilised to send the data from each fleet vehicle to the server
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• Frequency: from 2 to 10 seconds (the data plan will be decided by the end of August, 2013); currently the data frequency as set‐up by IST for the CPO is one minute. The same data plan will be utilised for both OSEL and CPO.
5.5.4 CPO Installation Requirements
The CPO fleet management devices have been installed independently from REDUCTION by IST on the CPO delivery vehicles during July, 2013. Under REDUCTION, IST will install an API to read the raw data from the vehicle CANbus using the OBD port. IST will purchase this API and CTL will reimburse them at a cost of $1000 (756 euro).
Hardware needs: • Up to 68 CPO fleet vehicles equipped with the fleet management system • Data storage server. The IST storage data server will be utilised, which is the same that it will be used for the CPO fleet management system. Software installation needs: • The API interface will be installed by IST to read and store the CANbus data for each fleet vehicle. • CTL wil assist IST thoughout the API installation and testing • UTH and UHI will assist in finalizing the data that will be gathered from the CANbus via the API software and the data storage Personnel needs: • DDE: DELPHI MyFI V2X/CCU device expert (if the CPO fleet is utilised instead of the OSEL buses) • CTL: One manager, one IT expert • IST: One IT expert with the CPO fleet management system • CPO: Up to 68 fleet drivers, one manager • UTH: One Communications scientist • UHI: Project Manager, One data mining expert • MCW‐PWD: One official representative.
CPO Fleet Coverage area: • The greater Nicosia network will be the primary focus of the field study • Additional routes for all delivery destinations covered by the 68 fleet management equipped vehicles may be added. The final list of the vehicles that will be covered for REDUCTION by the end of August, 2013; this list will determine also the coverage area. Data to be extracted from the OBD port: • GNSS data: vehicle location, speed; Frequency: every 10 sec (default); if feasible they will be gathered every 2 sec.
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• Fuel Consumption Frequency:every 2 sec (to be gathered by the API) • GHG emissions: GHG emissions are not readily available from the data list provided by the vendor; if feasible they will be included in the messages that will be retirevd from the CANbus.
Obstacles in running the field trial The Nicosia OSEL bus field trial has been delayed due to the following reasons: OSEL requested that REDUCTION should provide a guarantee that the installation of the DDE devices on the OSEL Citaro Mercedes‐Benz buses would not negate the Warranty of the buses. DDE explained that without the cooperation of Mercedes‐Benz it would have been difficult if not impossible to be able to read the CANbus data from the Citaro Mercedes‐Benz buses. By the end of October 2012 it was decided that such cooperation with Mercedes‐Benz is not possible. DDE then informed CTL that there was a possibility that the CANbus data could be read indirectly by “sniffing” them from the bus wiring system. It was therefore agreed to proceed with the test using OSEL buses if OSEL would agree to participate. CTL in November, 2012 asked OSEL whether they will participate in the field trial, based on the advice from DDE that the installation and testing of the devices will not be intrusive to their operation. After several discussion, CTL, DDE and OSEL agreed to conduct the “sniffing” diagnostic test in early March, 2013 – the time that all parties were available to do this test.
First OSEL Buses Diagnostic Test; 6th of March, 2013: DDE during this test with the cooperation of OSEL and CTL tried to gather data streams by sniffing data from the bus wiring system. This test was not successful.
Second OSEL Diagnostic Test: As mentioned earlier, the first diagnostic test conducted by DDE and CTL on two OSEL buses in Cyprus on the 6th of March, 2013 was not successful. However, it revealed that there was a possibility to read the data via the fleet management port of the Citaro Mercedes‐Benz buses. DDE therefore, suggested that it was worthwhile to try once more by developing a connector that could fit with the Citaro fleet management connector. This second test is expected to be conducted in August 2013.
Back‐up solution to the OSEL bus field trial, the use of CPO delivery company: Given this setback, in March 2013, CTL and DDE decided that we needed to find an alternative fleet to carry out the Nicosia fleet trial as a back‐up solution. CTL therefore conducted the following potential participants: 1) Travel Express (an intercity taxi/minivan) company. 2) the EMEL Limassol bus company; 3) The CPO company. Among them the CPO was selected as the best potential solution since it was ready to install its own fleet management system that included GNSS and fuel data.
IST informed CPO in May 2013 of the desire of CTL to utilise their fleet management system, once installed, for the purposes of REDUCTION and specifically for the development of bus
69 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] driver profiles using fuel consumption as the main parameter. IST during the months of May, June and July kept on updating CTL on the progress of the installation of the CPO fleet management system. IST also informed CTL in early July that CPO was looking favorably towards a potential cooperation with REDUCTION. Given this assurances, CTL informed the REDUCTION consortium at the Volos, Greece meeting (9‐10/7/2013) that an alternative company has been found which could be used for carrying out the fleet field trial regardless of the outcome of the second diagnostic test using the OSEL buses. CTL subsequently met with CPO and IST and received a verbal agreement from CPO at a meeting conducted at the offices of CPO in mid‐July, 2013. CTL proceeded to provide final confirmation to DDE that indeed we could utilise the CPO delivery company as a back‐up in case the second diagnostic test using OSEL Citaro Mercedes‐Benz buses fails.
Given these changes the Cyprus fleet field trial is expected to start in early August and be completed by Mid‐March, 2014.
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Tasks ‐ Cyprus Fleet Field Trial Plan – Phase I (Revised) Task 1. Secure the participation of OSEL, CPO and the Ministry of Communications and Works in the REDUCTION Nicosia Fleet Operations field study. (2012‐09‐01 – 2013‐04‐30)
Task 1.1 Prepare a detailed description of the REDUCTION field trial to OSEL and the MCW‐PWD.
• Description of the bus routes and bus types that will be used for REDUCTION. • Description of the technologies that will be installed in the buses. • Description of the communication system • Description of the installation and testing of the hardware and software in the buses • Description of the data gathering process and the data analysis models • Description of the bus field trial • Description of the bus drivers, OSEL executives and MCW‐PWD officials’ feedback on REDUCTION technologies questionnaire Task 1.2 Sign an agreement (if requested by OSEL) between OSEL, MCW‐PWD and REDUCTION to carry out the field trial based on the description of the field trial.
Task 1.3 CPO fleet field trial: Prepare a detailed description of the REDUCTION field trial.
• Description of the delivery vehicle types and routes that will be used for REDUCTION. • Description of the technologies that will be installed in the CPO vehicles. • Description of the communication system • Description of the installation and testing of the hardware and software in the CPO vehicles • Description of the data gathering process and the data analysis models • Description of the CPO fleet field trial • Description of the Feedback Questionnaire for CPO drivers, CPO management and IST on REDUCTION technologies Task 1.4 Sign an agreement (if requested by CPO) between CTL, DDE, CPO and REDUCTION to carry out the field trial.
Deliverables: • Final report on the description of the Nicosia OSEL bus field trial (CTL, 2013‐09‐30) o Current Status: will be completed pending on the outcome of the second diagnostic test that is expected to be carried out in August, 2013.
• Signed agreement (if requested by OSEL) between REDUCTION consortium, OSEL and MCW‐PWD on the Nicosia field trial (CTL, 2013‐09‐30) o Current Status: A verbal agreement has been reached between CTL, DDE, OSEL and MCW; pending on the outcome of the second diagnostic test in August, 2013.
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• Final report on the description of the Cyprus CPO fleet field trial (CTL, 2013‐09‐30) • Signed agreement (if requested by CPO) between CPO and REDUCTION to carry out the field trial. Task 2 Select, purchase and test the wireless data plan from MTN (2013‐08‐04 – 2013‐08‐19)
• CTL in coordination with OSEL, CPO/IST, DDE, UTH and UHI will decide and purchase the desired data plan from the MTN cellular carrier (2, 5, or 10 sec) • CTL in cooperation with IST will test the MTN data plan using the existing fleet management system of CPO Deliverables: • Technical memorandum on the MTN data plan Task 3. Develop interface software to retrieve data from the fleet vehicles’ CANbus (2013‐04‐01 – 2013‐09‐13)
Task 3.1 Develop interface software between the MyFi and the OSEL Bus CANbus (2013‐04‐ 01 – 2013‐09‐13) OSEL will provide to CTL and DDE the bus model specifications that will be used during the Nicosia field trial.
• DDE will develop the software to communicate between the REDUCTION hardware/software and the OSEL buses’ CANbus. • DDE and CTL will conduct diagnostic tests and send sample data collected via the MyFI interface software to DDE, UTH and UHI for preliminary analysis Task 3.2 Develop Interface software between the MyFi and the CPO fleet vehicles’ CANbus (Back‐up solution; will be implemented only if the OSEL field trial will not be carried out) (2013‐08‐19 – 2013‐09‐13)
• IST will provided to CTL and DDE the fleet vehicles’ model specifications that will be used during the Cyprus field trial. • DDE will develop the interface software to communicate between the REDUCTION hardware/software and the CPO vehicles’ CANbus. • IST and CTL will conduct diagnostic tests and send sample data collected via the MyFI interface software to DDE, UTH and UHI for preliminary analysis Task 3.3 Installation and testing of the API interface to gather data from the CPO vehicles’ CANbus (2013‐08‐07 – 2013‐08‐31)
• IST will purchase and install the API that communicates and gathers data from the fleet management device • CTL, DDE, UTH and UHI will prepare the list of the data needs that need to be gathered from the vehicles’ CANbus • IST and CTL will conduct diagnostic tests and send sample data collected via the API to DDE, UTH and UHI for preliminary analysis Deliverables:
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• Technical Memorandum of the functionality of the DELPHI MyFI V2X/CCU interface software (DDE, 2013‐09‐30). • Technical Memorandum on the functionality of the CPO fleet management system API interface software (IST, CTL, 2013‐09‐30) Task 4. Development of data analysis procedure by REDUCTION partners (2013‐08‐19 – 2103‐09‐ 30)
• Description of the data set gathered from the OSEL bus MyFI devices • Description of data sets gathered from the CPO fleet management system • Development of data analysis models and associated software used Deliverables: • Report on the data mining procedure to analyze the OSEL bus fuel consumption driving behavior (UHI, CTL 2013‐09‐30) • Report on the data mining procedure to analyze the CPO fleet fuel consumption driving behavior (UHI, CTL 2013‐09‐30) Task 5. Installation and testing of REDUCTION hardware and software for the fleet field trials
Task 5.1 Installation and testing of the DDE MyFI V2X/CCU devices (2013‐03‐05 – 2013‐09‐13)
Task 5.1.1 Diagnostic tests for the DDE MyFI devices
First Diagnostic Test 2013‐03‐06: The first diagnostic test was conducted to verify whether the device has the capability to read the following data from the bus CANbus: fuel consumption, GHG and vehicle location/speed. As mentioned earlier this test was not successful as we were not able to read the data by sniffing them from the wiring system.
Planned Second Diagnostic Test 2013‐08: A second test will be conducted in August 2013 where DDE and CTL will try and sniff out the data from the Citaro buses fleet management port. A special connector has been developed for this purpose by DDE. If the second filed test is successful then the following steps will be carried out to proceed with the OSEL field trial. Otherwise, we will utilise five CPO fleet vehicles to carry out the test.
• Step 1. Conduct a meeting with OSEL to outline the installation procedure of the MyFI to OSEL executives, and technical personnel • Step 2. Install the hardware and software at one OSEL bus • Step 3. Conduct a validation test to examine the functionality of the DELPHI MyFI device: Read, store at the MyFi and send wirelessly GNSS vehicle location and speed, fuel consumption, and GHG emissions data. Task 5.1.2 Installation and testing of the DDE MyFI devices on the five OSEL buses
• Conduct a workshop at OSEL offices to outline the installation procedure to OSEL executives, MCW‐PWD officials and technical personnel for the five OSEL Citaro Mercedes‐Benz buses (half day) • Identify the five buses that the DDE MyFI devices will be installed
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• Install the hardware and software on the five OSEL buses. • Conduct a validation test to fine tune the five DDE MyFI devices and ensure that they function as intended for each bus: Read, store at the MyFi and send wirelessly GNSS vehicle location and speed, fuel consumption, and GHG emissions data. • Train the OSEL manager and bus drivers on the use of the DDE MyFI devices Task 5.2 Installation and testing of the CPO fleet management and the DDE MyFI devices (2013‐07‐ 01 – 2013‐09‐13)
Task 5.2.1 Diagnostic tests of the CPO fleet management devices and their capabilities
CPO Fleet management System First Diagnostic Test 2013‐07‐17: The first diagnostic test was conducted to familiarize with the existing fleet management system and the automated reports that it generates. The system produces automated real time reports as follows: 1) Data from each vehicle are transmitted at one minute time intervals using the MTN wireless network from each vehicle to a central server (owned and operated by IST), 2) Vehicle trip fuel consumption, 3) Vehicle GNSS data (location and speed) recorded at 10 sec. time intervals, 4) Drivers’ sudden acceleration alarms above a certain default threshold (recorded per trip), 5) A five stage driving scoring system where A is the best and E is the worst (recorded per trip).
CTL requested from IST to gather also the following additional data: fuel consumption, GNSS and GHG emissions at 2 sec. time intervals on a link by link basis. IST informed CTL these data could only be produced using an API software that is sold separately by the FMT vendor from China. CTL agreed to purchase the API interface to gather CANbus data on a link by link basis.
CPO CANbus API Interface Diagnostic test, 2013‐08‐07‐22: The API will be installed at 8 delivery vehicles that will be used to test its capabilities of gathering the selected data from the vehicle’s CANbus and sending them to the IST server through the selected MTN data plan. The 8 delivery vehicles are chosen to be utilised first since they demonstrated a much larger activity than the other vehicles by covering much more Km per day. This diagnostic test will be used to test the following: MTN’s data plan communication system, the API interface gathering and sending data, and finalizing the data stream that will be needed to carry out the REDUCTION fleet field test.
Task 5.2.2 Diagnostic test of the DDE MyFI devices and their capabilities (if needed)
CPO DDE MyFI Diagnostic Test, 2013‐08‐18‐25: This test will be conducted only if the second OSEL diagnostic test fails to read the OSEL buses CANbus data.
One DDE MyFI device will be installed at one CPO fleet vehicle that will be designated by the CPO. The main steps that will fe followed to carry out these diagnostic tests are:
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• Step 1. Conduct a meeting with IST and CPO to outline the installation procedure of the MyFI to CPO executives and technical personnel • Step 2. Install the MyFI hardware and software at one CPO fleet vehicle based on its unique model specifications o DDE will develop the interface software to read and gather the data from the vehicle’s CANbus and transfer them to the MyFI. • Step 3. Conduct a validation test to examine the functionality of the DELPHI MyFI device: Read from CANbus, store at the MyFi and send wirelessly fuel consumption data, GHG emissions and GNSS vehicle location and speed data Task 5.3.1 Installation and testing of the CPO CANbus API at the selected set (up to 68) of CPO fleet vehicles (2013‐08‐07 – 2013‐08‐31)
• Conduct a workshop at the CPO offices to outline the installation procedure and expected outcomes of the field trial to IST, CPO executives, and technical personnel for the designated CPO fleet vehicles. • Identify the final list of the CPO fleet vehicles that will be used for REDUCTION and install the CANbus API interface. • Conduct a validation test to fine tune the API interface and the wireless MTN data communication and ensure that they function as intended for each vehicle: Read, store and send wirelessly fuel consumption, GHG emissions, GNSS vehicle location and speed data to the IST server. Task 5.3.2 (if needed) Installation and testing of the five DDE MyFI devices on the five CPO fleet vehicles
• Conduct a workshop at the CPO offices to outline the installation procedure to IST CPO executives, and technical personnel for the five CPO fleet vehicles. • Identify the five CPO fleet vehicles that will be utilised for REDUCTION and install the DDE MyFI devices and associated software. • Conduct a validation test to fine tune the five DDE MyFI devices and ensure that they function as intended for each vehicle: Read, store and send wirelessly fuel consumption, GHG emissions, GNSS vehicle location and speed data to the IST server. • Train the IST manager and CPO fleet drivers on the use of the DDE MyFI devices Deliverables: • Report on the installation and testing procedure for the API interface and the MyFI devices with the CPO fleet vehicles’ CANbus (CTL, DDE, and IST, 2013‐09‐30) • Report on the installation and validation phase (CTL, DDE, and IST 2013‐09‐30) • Report on the training of the CPO fleet drivers (CTL, DDE, and IST 2013‐09‐30) Task 6. Conduct the REDUCTION Cyprus Fleet Driver Driving Behavior Field Trial
The field trial will be conducted in two phases based on the REDUCTION initial plan: The first phase (Phase I) will last about eight weeks to monitor the driving behavior of the drivers based primarily on fuel efficiency. No advise will be provided to the drivers during
75 D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] this 8‐week time period. Instead the REDUCTION partners CTL, DDE, UTH and UHI will utilise the data gathered from this 8‐week time period to develop fuel consumption driving patterns per driver. Subsequently, the REDUCTION partners will develop a set of guidelines for each driver that will aim towards the reduction of fuel. In Phase II of the Cyprus field trial another 8‐week monitoring period will be allocated to gather and analyse the fuel consumption driving patterns for all drivers – the drivers will be instructed to follow the driving guidelines developed under REDUCTION. The results of Phase I and II will be summarized in a report as part of REUCTION and a workshop will be conducted with both OSEL, CPO and MCW to present the results of the study.
Task 6.1 Data collection for the existing fleet driver behavior field study (2013‐09‐01 – 2013‐11‐01) • The five OSEL buses equipped with the DDE MyFI devices will follow their normal bus routes. • The designated CPO fleet vehicles equipped with the fleet management system and the CANbus API interface will follow their normal routes. • The DDE MyFI devices will record data in real‐time from the buses’s CANbus, store them temporarily at the DDE MyFI SSD, and sent them wirelessly to a CTL server for storage via the MTN 3G network. The frequency of the data transmission will be decided during the diagnostic test. • The CPO fleet management system (FMT) CANbus API will record data in real‐time from the fleet vehicles’s CANbus, store them temporarily at the fleet management system’s me memory, and sent them wirelessly to the IST server for storage via the MTN 3G network. The frequency of the data transmission will be decided during the diagnostic test. • CTL will be sending the stored data to the REDUCTION partners UHI and UTH through a web service for both OSEL and CPO fleets on a weekly basis (or as decided during the course of the field trial).
Task 6.2 Analysis of the data and development of the OSEL and CPO fleet drivers fuel consumption and GHG emissions driving profiles (2013‐09‐08 – 2013‐11‐09) • UHI with the aid of CTL will develop the drivers’ fuel consumption and GHG emissions driving profiles. • DDE and UTH will analyze the data quality gathered by the MyFI devices and the V2I communication data from the DDE MyFI devices to the server. Task 6.3 Development of fuel efficiency guidelines per driver (2013‐10‐21 ‐ 2013‐11‐15) • CTL and UHI will present to OSEL, CPO and the fleet drivers the main findings from the driving field study. The driving patterns that produce more efficient fuel consumption will be identified and presented. • The REDUCTION partners will recommend a set of driving guidelines for each driver that will aim in the reduction of fuel consumption to OSEL and CPO management, respectively. Each driver will be requested to follow these recommendations during the second 8‐week phase of the project.
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• The REDUCTION partners will also present the corresponding GHG emissions driving profiles to CPO management such that they could use it in the future as necessary (e.g. if the laws in Cyprus change such that carbon credits are provided to fleet operators that reduce their GHG emissions). Task 6.4 Prepare Bi‐weekly progress reports for the fleet driver behavior field trial (2013‐08‐16 – 2013‐11‐15) • CTL will be reporting to the REDUCTION partners the status of the field trial on a bi‐ weekly basis from the 16th of August 2013 to the 15th of November, 2013 in the form of Technical Memorandums. Deliverables: • Draft Final Report on the results of the Fleet Field Study using existing driving behavior (2013‐12‐15) • Bi‐weekly field trial Technical Memorandums (2013‐08‐16 – 2013‐11‐15)
5.6 Tasks – Proposed Driving Behavior Fleet Field Trial‐ Phase II Phase II of the Nicosia fleet field trial will be carried out for both OSEL bus and CPO fleet drivers. It will be carried out during an 8‐week time period to determine whether the drivers followed the proposed fuel efficiency driving guidelines and whether these guidelines produced any significant benefit to the OSEL and CPO fleet operators using fuel consumption as the main parameter.
Task 7 Monitor and analyze the fleet driver behavior based on the proposed guidelines for an 8‐week time period
Task 7.1 Data collection for the fleet driver behavior field study based on proposed driving guidelines (2013‐11‐18 – 2014‐01‐18) • The fleet drivers of OSEL and CPO will be requested to follow the proposed driving guidelines that have been developed during Phase I of the field study. • The five OSEL buses equipped with the DDE MyFI devices will follow their normal bus routes. • The designated CPO fleet vehicles equipped with the fleet management system and the CANbus API will follow their normal routes. • The DDE MyFI devices will record data in real‐time from the buses’s CANbus, store them temporarily at the DDE MyFI SSD, and sent them wirelessly to a CTL server for storage via the MTN 3G network. The frequency of the data transmission will be decided during the diagnostic test. • The CPO fleet management system (FMT) CANbus API will record data in real‐time from the fleet vehicles’s CANbus, store them temporarily at the FMT memory, and sent them wirelessly to the IST server for storage via the MTN 3G network. The frequency of the data transmission will be decided during the diagnostic test. • CTL will be sending the stored data to the REDUCTION partners UHI and UTH through a web service for both OSEL and CPO fleets.
Task 7.2 Analysis of the data collected during Phase II of the field trial (2013‐11‐25 – 2014‐01‐31)
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• UHI and CTL will analyze the data and develop the OSEL and CPO fleet drivers’ driving profiles. • UHI and CTL will conduct a comparative analysis between the existing and the proposed fuel efficiency driving profiles for each driver. This analysis is expected to determine whether the drivers indeed changed their driving behavior and whether this change resulted in a reduction on fuel consumption and/or GHG. • UTH and DDE will analyze and summarize the V2I wireless communication data from the DDE MyFI V2X/CCU devices to the server. Task 7.3. Bi‐weekly reporting on the Fleet Driver Behaviour Field Trial (2013‐11‐18 – 2014‐02‐15) • CTL will be reporting to the REDUCTION partners the status of the field trial on a bi‐weekly basis from the 16th of August 2013 to the 31st of January, 2014 in the form of Technical Memorandums. Deliverables: • Draft report on the results of the Nicosia field trial (2014‐03‐15) • Bi‐weekly field trial Technical Memorandums (2013‐11‐18 – 2014‐02‐15) Task 8. User Benefits Questionnaire
• CTL will prepare and conduct a questionnaire to OSEL, CPO management and fleet drivers, and the MCW‐PWD to extract their experience on the user benefits they observed from the REDUCTION technologies. Deliverables: • Report on the User benefits (OSEL, CPO/IST and MCW‐PWD) questionnaire (2014‐ 03‐15)
Task 9. Communications V2V and V2I Fleet Field Trial
Task 9.1 Evaluate the V2I communication system throughout the fleet field trials of OSEL and CPO, respectively (2013‐09‐01 – 2014‐01‐18) • Evaluate the data gathering from the CANbus to the temporary storage and sending to a remote server in real‐time. Task 9.2 Conduct a controlled test to evaluate the V2V and V2I communications using the five MyFI devices (2014‐01‐15 – 2014‐02‐15) The DDE MyFI devices have the capability to communicate in real time with each other as they were designed to support V2V communications. Given that the number of devices is very small, it is proposed that a controlled field study be conducted using five vehicles. The main steps of this controlled experiment are:
• Select a corridor where the controlled test will be carried out. • Recruit five drivers to carry out the test • Install the five MyFI devices on the five test vehicles. • Design a set of tests that will evaluate the capability of the MyFI devices to send and receive real‐time messages at short range. This may include but not limited
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to: range between vehicles, vehicles running on parallel roads of close proximity, vehicles running in close proximity on crossing roadways near intersections, vehicles operating at various speed levels (but within legal limits), etc. • Conduct a set of training tests such that the drivers become familiar with the routes to be followed. • Conduct the V2V and V2I test. • Summarize the results in a report. Deliverables: • Report summarizing the results of the V2V and V2I communication test (2014‐02‐28) Task 10. Cyprus Fleet Field Trial Final Report (2014‐04‐30)
Deliverables: • The description of the Cyprus field trial and the associated results will be presented in a Final Report (2014‐04‐30) Cyprus Field Trial Schedule and Deliverables
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Table 12 Cyprus Fleet Driver Behavior Field Trial Schedule Month 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Task 1 x x xx x xxxxxxxx x x x Task 1.1 xxxx xxxx xxxx Task 1.2 xx xxxx Task 1.3 xxxx xxxx Task 1.4 xx xxxx Task 2 xxx Task 3 x x x xx xxxx xx Task 3.1 x x x xx xxxx xx Task 3.2 xx xx Task 3.3 xxx Task 4 xxxxxx Task 5 xxxx xxxx xx Task 5.1 x xx xx Task 5.2 xxxx xxxx xx Task 5.3 xxxx Task 6 xxxx xxxx xx Task 6.1 xxxx xxxx Task 6.2 xxx xxxx x Task 6.3 xx xx Task 6.4 x x x x x Task 7 xx xxxx xxxx xx Task 7.1 xx xxxx xx Task 7.2 x xxxx xxxx Task 7.3 x x x x x Task 8 xx xxxx xx Task 9 xxxx xxxx xxxx xxxx xxxx xxxx Task 9.1 xxxx xxxx xxxx xxxx xx Task 9.2 xx xx Task 10 xx xxxx xxxx xxxx
Table 13. Nicosia Fleet Driver Behavior Field Trial – Deliverables and Milestones Task Description/ Deliverables Authors Date 1 Finalize the plan for the Cyprus Fleet Field Trial and secure CTL, DDE, UTH, UHI, 2013‐09‐30 the participation of OSEL, CPO and the Ministry of OSEL, CPO, IST Communications D5.6.1 Final report on the description of the Nicosia OSEL bus field trial D5.6.2 Signed agreement (if requested by OSEL) between the REDUCTION consortium, OSEL and MCW‐PWD on the Nicosia field trial D5.6.3 Final report on the description of the Cyprus CPO fleet field trial D5.6.4 Signed agreement (if requested by CPO) between CPO and REDUCTION to carry out the field trial
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2 Select, purchase and test the wireless data plan from MTN CTL, IST 2013‐08‐31 D5.6.5 Technical memorandum on the MTN data plan 3 Develop interface software to retrieve data from the fleet CTL, DDE, IST 2013‐09‐30 vehicles’ CANbus D5.6.6 Technical Memorandum of the functionality of the DELPHI MyFI V2X/CCU interface software D5.6.7 Technical Memorandum on the functionality of the CPO fleet management system API interface software (IST, CTL, 2013‐09‐30) 4 Development of the data analysis procedure by REDUCTION DDE, CTL, UTH 2013‐11‐01 partners D5.6.8 Report on the data mining procedure to analyze the OSEL bus fuel consumption driving behavior D5.6.9 Report on the data mining procedure to analyze the CPO fleet fuel consumption driving behavior 5 Installation and testing of REDUCTION hardware and CTL, DDE, IST 2013‐09‐30 software for the fleet field trials (2013‐03‐05 – 2013‐09‐13) D5.6.10 Report on the installation and testing procedure for the API interface and the MyFI devices with the CPO fleet vehicles’ CANbus D5.6.11 Report on the installation and validation phase D5.6.12 Report on the training of the OSEL/ CPO fleet drivers 6 Conduct the REDUCTION Cyprus Fleet Driver Driving CTL, DDE, UTH, UHI, 2013‐12‐15 Behavior Field Trial ‐ Existing driving patterns IST, OSEL, CPO D5.6.13 Draft Final Report on the results of the Fleet Field Study using existing driving behavior D5.6.14 Bi‐weekly field trial Technical Memorandums 7 Monitor and analyze the fleet driver behavior based on the CTL, DDE, UTH, UHI, 2014‐03‐15 proposed guidelines for an 8‐week time period IST, OSEL, CPO D5.6.15 Draft report on the results of the Nicosia field trial D5.6.16 Bi‐weekly field trial Technical Memorandums 8 User Benefits Questionnaire (2014‐01‐21 ‐ 2014‐03‐15) CTL, IST, UTH, UHI 2013‐03‐15 D5.6.17 Report on the User benefits (OSEL, CPO/IST and MCW‐PWD) questionnaire (2014‐03‐15) 9 Communications V2V and V2I Fleet Field Trial UTH,DDE,UHI,CTL,I 2014‐02‐28 ST D5.6.18 Report summarizing the results of the V2V and V2I communication test 10 Cyprus Fleet Field Trial Final Report CTL,DDE,UTH,UHI 2014‐04‐30 D5.6.19 Cyprus Field Trial Final Report
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6. Trinité Amsterdam Field Trail: Phase‐1 The Food Centre Amsterdam is in the middle of the city. Every day transportation companies (freight) are visiting the Food Centre in order to unload their cargo. There are two main routes to reach the Food Centre from the city ring, the Jan van Galenstraat and the Haarlemmerweg. The Jan van Galenstraat is the main route from historic perspective and connects the city ring with the south entrance of the Food Centre. Since a couple of years a north entrance has been established, but it is hardly used by the transportation companies.
The problem that arises from this is that the CO2/NOx emission in the Jan van Galenstraat is too high. This is caused by queues of trucks that are waiting to use the south entrance for unloading their cargo at the Food Centre. Because there is no parking space, the truck drivers choose to drive circles in the city, visiting the south entrance until they are allowed to enter. A secondary problem that comes from this is that the truck drivers choose to take constantly right turns, in order to get back to the south entrance. If they choose to take left turns they have to cross tramways, which makes it more difficult. This “circle driving” with constant right turns cause a lot of accidents with bicycles that are in the blind spot of the truck.
Finally, for the transportation companies the travel time to reach their destination is too high. When there is a better schedule for unloading cargo, the circle driving would be decreased which contributes to the safety of bicycle drivers, the CO2/NOx emission is expected to be lower in the Jan van Galenstraat and the travel time (and therefore the CO2 emission and fuel consumption) of the trip will be decreased.
During the first meeting with the city of Amsterdam on 3 September 2013, the context of this problem is being discussed and the continuation of this pilot is being agreed upon. The next step would be to deliver a plan of action before a kick‐off meeting would be organised with all stakeholders present.
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Figure 13. Overview of the Amsterdam use‐case.
6.1 Goals of the field trail The Amsterdam field trail should deliver insight to several points of interest.
• Examine the possibility to minimise the air pollution and keep the route “green” by advising specific routes and departure times to drivers using the ATM (Area Traffic Manager) of the system architecture, described in work package 4 and the smartphone app.
• Examine the possibility of integrating the traffic data and CO2/NOx data from third party companies, in order to compare the baseline situation with the outcome of the field trail.
• Determine the possibility to minimise traffic accidents with bicycle drivers and the contribution to the safety in the environment.
• Determine the possibility to minimise the travel time for transportation companies and the contribution to decrease CO2/NOx emission and fuel consumption.
• Establish a clear and concrete view on how to deploy the system, with all obstacles identified and removed in order to successfully fulfil the pilot with the city of Amsterdam.
• Establish a system that is ready or nearly ready to be used in the pilot, before the field trails of the REDUCTION project finishes.
It is not a main goal of this pilot to be finished within the time constraints of the REDUCTION
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] project itself. We expect the pilot to be operational, even after the REDUCTION project will end. This is because this pilot is strongly related to two other pilots: the IJburg pilot and the ambulances pilot for the city of Amsterdam. These pilots together form one of four “Front Runner” projects (“boegbeeld” projects in Holland).
The Dutch ministry of Infrastructure and Environment, in person of minister Schultz van Haegen, has issued a government initiative “Better Informed on the road”. The goals of this initiative is to improve the service to travellers by stimulating innovative commercial companies and allow them to cooperate closely with local governments and road authorities. Trinité Automation signed in on this initiative with the “Digital Road Authority”, a concept in which the Area Traffic Management (ATM) plays a central role. The Digital Road Authority contains the three pilots as mentioned above.
The REDUCTION project and its state‐of‐the‐art technologies, research and consortium meetings have a large and positive contribution to the deployment of the system and the success of the pilots. All three pilots (the green route to the Food Centre, the IJburg pilot and the ambulances pilot) use the same concepts and technologies, only deploy them in a different context and for different goals. Trinité Automation chooses to add the green route pilot (the Amsterdam use‐case) to the REDUCTION project for the benefit of all partners, the pilot and the REDUCTION project itself. The other two pilots will not be described within this work package, but do have a close dependency with each other in the deployment of the system and management of the pilots.
6.2 Field trail Amsterdam The field trail in Amsterdam will cover 2 roads. The roads are selected in cooperation with the city authority (DIVV) of Amsterdam. The two routes are main routes to the centre of the city.
1) Haarlemmerweg
2) Jan van Galenstraat
The city of Amsterdam is very interested in reducing the air pollution created by the traffic on the two routes. The smartphone app will be used to guide road users trying to minimise the CO2/NOx emission. The ATM (Area Traffic Manager) provides the information used to guide the road user.
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Figure 14. Routes field trail Amsterdam.
The ATM monitors the performance of the network real time and can communicate through controlling SwitchPoints (AccessPoint are the entry and exit points of a SwitchPoint). Depending on the traffic the ATM can decrease the traffic inflow of a specific route. The ATM can use roadside equipment to regulate the flow but for this test especially the smartphone app will be used to advise users to reroute.
Secondly, the smartphone app will be used to communicate the desired arrival time of the transportation company at the Food Centre Amsterdam, to unload their cargo. The smartphone app will send the desired arrival time to the ATM that controls the traffic in the area and the ATM gives advise back to the driver on the optimal departure time. In order to determine the optimal departure time, the system has to be able to determine an optimal schedule for unload cargo, minimizing the waiting queues at the entrance of the Food Centre.
6.3 Use case 1 User wants to take a drive and enters his desired date and departure time in the agenda of his smartphone. The smartphone will read the agenda and communicates the requested arrival date and time with the ATM. The ATM will give back an optimal departure time.
2 The smartphone app calculates the most eco‐friendly route based on the historical data from the database and calculates the AccessPoints that are passed.
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3 The user starts driving and the smartphone app calculates if it is crossing an AccessPoint. When crossing a AccessPoint it notifies the ATM and sends its information (speed, acceleration, fuel consumption, CO2 emission and the next AccessPoint to pass)
4 The ATM is monitoring the network of Amsterdam and receives a notification from the smartphone app. With the AccessPoint information from the app it can check if the route passes one of the specified routes and decide to give a rerouting advise. (Push message to the smartphone app)
5 When the user arrives at his destination all information is stored and the driver will receive a ranking from the “Best driver contest”.
6.4 Best REDUCTION driver contest The best driver contest is a game that can be played by the users. Users can register through the app. Based on the fuel consumption, CO2 emission and following up the rerouting advise a ranking list is generated. This list is available on the driver contest web portal.
Rerouting advice: use the preferred route. BONUS: 10 extra points
The goal behind the contest is to stimulate users to carry out the advices given and make eco‐ driving attractive.
6.5 Status of the field trial The following steps need to be taken in order to complete the field trail:
1. A formal acknowledgement from the city of Amsterdam to participate and cooperate in this field trail.
2. A plan of action need to be written, that will describe the steps in detail that is necessary to be taken, the anticipated risks and possible control measures to eliminate the risks, the stakeholders and their role in the pilot, a timeline/planning.
3. A kick‐off meeting has to be organized with all stakeholders present. The plan of action will be discussed and their contribution to the plan will be incorporated.
4. A test group of drivers needs to be assigned.
5. Measurement data (traffic and CO2/NOx emission) must be made available to the
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system.
6. Design and development of the necessary software; the smartphone app, the ATM and the scheduling mechanism on the waiting queues.
7. Installing and testing the system for baseline measurements of CO2/NOx emission in the Jan van Galenstraat.
8. Installing and testing the system for the test group of drivers and the field trail.
9. Collecting and evaluation of the results.
The current status of the field trail is that step 1 and step 2 are completed. Design and developments of step 6 are in progress. A kick‐off meeting is not yet established, but expected to take place soon. The results of the kick‐off meeting are important for the necessary data collection and the formation of a test group of drivers (step 4 and 5).
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7. CTL Nicosia Simulation Field Trial: Phase‐1
7.1 Introduction This report presents the implementation of the Visual Interactive System for Transport Algorithms (VISTA) Dynamic Traffic Assignment (DTA) in Nicosia to produce estimates of traffic flow conditions, fuel consumption and GHG emissions as part of CTL’s Cyprus field trial. This report also includes an update to the original literature presented to date under REDUCTION to include environmental models and transportation models implemented to produce estimates of environmental Measures of Performance (MOPs) of Fuel Consumption (FC) and Greenhouse Gas (GHG) emissions.
The motivation for the utilization of Dynamic Traffic Assignment to evaluate the impact of eco‐ routing on network conditions stems from its versatility to be implemented on realistic networks while capturing the principal operational characteristics of a transportation network: 1) it reaches a DUE solution state, which is the basis for conducting comparative analyses – non‐ DUE models can only be employed for short‐term comparative studies, 2) it propagates traffic using the main principles of traffic flow theory through the use of a mesoscopic traffic simulator while modeling each vehicle, 3) it models pre‐timed signal timing, 4) it requires less expensive calibration versus microscopic traffic simulators. The utilization of microscopic traffic simulators, while capturing a more detailed and accurate representation of traffic flow conditions at sub‐second time steps, are applicable to rather small networks, hence impractical in capturing the impact of various projects or traveling policies for long paths. Their applicability for large networks deteriorates dramatically due to the computational burden and the calibration implementation costs, and almost impossible to reach a DUE state.
The principal characteristics of simulation‐based DTA models – the category of VISTA ‐ are: 1) A dynamic Origin‐Destination (OD) matrix is estimated using a combination of techniques (OD surveys, traffic counts, path trajectories via location estimation devices (GPS, wireless roadside vehicle readers)) ‐ the dynamic OD matrix is usually estimated at 15‐minute time intervals or less; 2) The model propagates the OD demand using a mesoscopic traffic simulator such as Daganzo’s cell transmission model at every few seconds (e.g., 6 seconds or less), vehicles move in packets from one cell to the next subject to the traffic flow theory laws of density, flow and speed ‐ where the number of vehicles moving is determined by the available capacity of the receiving cell at the specific time step; 3) Each vehicle moves along a time‐dependent shortest path (TDSP) that is determined at each iteration; 4) The model converges to a Dynamic User
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Equilibrium (DUE) – no user can unilaterally improves his/her travel time (cost) by changing his/her departure or desired arrival time and path within the assignment time interval – each path that has vehicles on it for a specific time interval will eventually have the same travel time (cost) as the other paths for each OD pair; 5) Simulation based DTA models converge to a local DUE and it is rather extremely difficult computationally to find a global DUE – as there is no equivalent mathematical programming model where global convergence can be proved.
The principal characteristics of VISTA are: 1) The travelers’ behavior is modeled using a Dynamic Traffic Assignment (DTA) model that reaches DUE; 2) it utilises a universal database model that is based on a spatial Geographic Information System (GIS) that can be easily interface with other databases; and 3) it is Internet and/or Intranet based, providing access to the various stakeholders to run the various algorithms, view the results of the models, query the database, change the database based on the authorization level of each. 4) it is suitable for modeling complex traffic flow on a large scale regional/sub‐regional network, 5) it propagates traffic based on the mesoscopic cell transmission model developed by Daganzo 1994 that follows the hydrodynamic theory of traffic flow at six‐second time intervals; at each six‐second time interval it moves some vehicles from one cell to the next, 6) it models motorways, weaving sections, ramps, signalized and unsignalized intersections, variable message signs, autos, buses, vehicle class restrictions and othe traffic control measures. The main output of VISTA is the vehicle trajectory from its Origin to its Destination at 6‐second time steps (based on the mesoscopic traffic simulator). These vehicle trajectories are used to produce estimates of link speeds, which is the main parameter employed of most environmental models. The implementation of the environmental models within the VISTA traffic simulator is described in the following sections.
The main objective of the REDUCTION Cyprus simulation field trial are:
Implement the VISTA DTA model to produce estimates of the time‐dependent shortest path (TDSP) trip based on: travel time, fuel consumption and air quality (GHG)
7.2 Supplemental Literature on DTA and Environmental Modeling This section provides an updated literature on DTA and environmental modeling.
7.2.1 User Equilibrium and System Optimal Traffic Assignment Environmental cost functions have been incorporated into traffic assignment components in a few past studies and this study relates to one of the broader sub‐categories. User equilibrium (UE) and system optimum (SO) assignment assume that users seek to minimize individual and
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] total travel costs, respectively. Following the initial work of Merchant and Nemhauser (with regard to the DTA modeling approach, classical path‐based system optimal DTA models assign trips to minimize the objective function. An assignment satisfies DUE when the dynamic travel costs between all origin and destination pairs are equal for all utilised paths, and no path has a smaller travel cost. Readers are directed to more detailed research by Ran et al., Bernstein et al., Friesz et al., and Wie et al., which conduct some of the pioneering work in terms of new formulations and novel solution approaches.
A summary of the main characteristics of DTA can be found in Peeta et.al. (2001). A more practical understanding on DTA and how to implement it can be found in the DTA primer developed by the Network Modeling Committee of the Transportation Research Board, Chiu, Y.C. et.al. (2011).
7.2.2 Environmental Objectives and Traffic Assignment One of the earliest studies to explicitly include environmental objectives in the form of emission factors in standard assignment practices was by Tzeng and Chen, who developed a multi‐ objective traffic assignment method using nonlinear programming techniques and produced various solutions that minimize carbon monoxide (CO) emissions, assuming fixed link‐specific emissions. Rilett and Benedek proposed an equitable traffic assignment with environmental cost functions and considered UE and SO traffic assignments . The same study was extended with an equitable strategy trying to minimize both CO emissions and travel time. Yin and Lawphongpanich use a bi‐objective function where objectives are reducing congestion and emissions through toll pricing. Sugawara and Niemeier developed an emission‐optimized assignment model and concluded that the emission‐optimized assignment is most effective under low to moderately congested conditions, saving 30% of CO emissions. Nagurney et al. developed a multi‐criteria network equilibrium model with environmental criteria and utilised a fixed amount of CO emission rate per traveler per link. In a separate study, Nagurney examines distinct paradoxical phenomena occurring in congested urban networks regarding total emissions and which demonstrates that so‐called network `improvementsʹ may actually result in higher emissions. More recently, Ahn and Rakha investigate the impacts of route choice decisions on vehicle energy consumption and emission rates for different vehicle types using microscopic and macroscopic emission estimation tools and report that the faster highway route is not always the best from an environmental perspective. Rakha et al. present the INTEGRATION microscopic framework and develops eco‐routing algorithms based on vehicle sub‐populations and individual agents, and notice a 15% savings in fuel‐consumption levels. Kolak et al. introduce sustainability measures based on emission amounts and conclude
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7.2.3 Eco‐Routing Based Navigation There have been some studies which explicitly model new strategies for finding eco‐routes. Ericsson et al. studied the tradeoff between a navigation system based on CO2 emissions rather than shortest distance or time. Their results indicate that these are not necessarily served by the same route and reductions of the order of 8% can be achieved when optimizing for fuel savings. Barth et al. and Minett et al. elaborate on energy and fuel efficiency by investigating the performance of energy optimized routes. Further, there have been studies that have conducted pollutant‐specific eco‐routing analysis, some of the ones being Frey et al. that show the effect of road grade on emissions and point out that best total fuel use and NOx emissions can dictate different eco‐routes. Ahn and Rakha and Bandeira et al. in separate studies show that different pollutants can have different best routes.
7.2.4 Environmental Models In principle, total emissions from fuel combustion are calculated by summing emissions from two different sources, namely the thermally stabilized engine operation (hot emissions), and the warming‐up phase (cold start emissions). Particulate emissions from resuspension, as well as evaporative emissions can also occur in a number of different ways. However, this literature focuses upon emissions from fuel combustion, since these are the main source of pollution.
All emission models take into account various factors affecting emissions, although the manner and detail in which they do so can differ substantially. Emission models can be split into two categories, i.e. Inventory (Fleet) emissions models and Vehicle emission models. Furthermore, a distinction can also be made between models which use continuous emission functions and models which use discrete emission values. In general, the emission calculation models could be categorized in 5 main categories:
1. Aggregated emission factor models, that operate on the simplest level, with a single emission factor being used to represent a particular type of vehicle and a general type of road: a) urban road, b) rural road and c) highway. 2. Average speed models, that are based upon the principle that the average emission factor for a certain pollutant and a given type of vehicle varies according to the average speed during a trip, and the emission factor is again usually stated in grams per
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travelled kilometre (g/km). 3. Traffic situation models, that are based on an approach for incorporating both speed and cycle dynamics into emission estimations involves “traffic situation” modelling, where cycle average emission rates are correlated with various driving cycle parameters 4. Multiple linear regression models, which employ a weighted‐least‐squares multiple regression approach to model emissions, based on tests on a large number of vehicles over more than 50 different driving cycles. 5. Modal models, factors are allocated to the specific modes of vehicle operation encountered during a trip. In the simplest type of modal model, vehicle operation is defined in terms of a relatively small number of modes. Several different terms (as well as modal) have been used to describe the more detailed type of model, including ‘instantaneous’ (which is the most well‐known and used one), ‘microscale’, ‘continuous’ and ‘on‐line’ (De Haan, et al., 2000). In ‘instantaneous’ models, the number of time steps increase dramatically (typically with one‐second intervals). Atjay et al. (2004) stated that the aim of instantaneous emission modelling is to map emission measurements from tests on a chassis dynamometer or an engine test bed in a neutral way. This review focuses on the following well known, used and cited environmental models:
MODEM (Modelling of emissions and consumption in urban areas): is an instantaneous emission model that produced during DRIVE (Dedicated Road Infrastructure for Vehicle safety in Europe) programme of the European Commission. The program is user friendly, with the only requirements being that the user specifies the driving pattern in the correct format, and identifies different directories for the input and output data. The model is capable of estimating fuel consumption and CO, HC, NOx and CO2 emissions on a second‐by‐second basis. Regarding model’s limitations, in MODEM, only the legislative class of vehicles EURO I is included. Moreover, MODEM is not capable of estimating cold start emissions, evaporative emissions, emissions from heavy duty vehicles (HDV) and motorcycles, or emissions of PM10, benzene and 1,3‐butadiene (Detr, et al., 2000).
PHEM (Passenger car and Heavy‐duty Emission Model): is an instantaneous emission model that estimates fuel consumption and emissions based on the instantaneous engine power demand and engine speed for a user‐specified driving pattern. It was developed in the framework of ARTEMIS project and the COST Action 346, and is a complete and accurate model to use, which takes into account the dynamics of driving cycles including driving resistances, the losses in the transmission system etc. Moreover, it includes HDV maps. In
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PHEM the assessment of the emission behaviour of engines meeting these standards is highly uncertain, as no production vehicles were available for measurement. Furthermore, the effects of the new technologies used to meet the type approval limits are difficult to predict.
VeTESS (Vehicle Transient Emissions Simulation Software): model is a computer simulation tool based on an instantaneous emission approach, being developed by the DECADE project. VeTESS calculates the emissions from a single vehicle during a “drive cycle”, i.e. a driving pattern defined by the model user. The driving pattern contains details of the speed of the vehicle and the road gradient. These data, when coupled with information on the vehicle form the basis of a series of calculations that derive the engine power required at every point on the route (MIRA, 2002). Using an innovative mathematical technique, VeTESS “looks up” each emission value from the corresponding engine map and then sums the interpolated values to provide a calculated figure for the emissions produced during that time increment. Each vehicle in VeTESS requires engine maps for each emission under examination, while within VeTESS, maps are contained for CO, CO2, NOx, HC, particulates as well as fuel consumption. Another pre‐requisite of the modelling technique is a detailed description of the journey to be simulated, in terms of vehicle speed and road gradient, and for some forms of analysis, such details may not be available. Although the software has been designed to allow the rapid simulation of many vehicles and many journeys, the model is better suited to the analysis of individual cases, and there are more suitable methods (employing emissions factors) for estimating emissions for large fleets and large geographical areas, particularly if precise journey details are not known (Boulter, et al., 2007).
CMEM (Comprehensive Modal Emissions Model): was created in 2001 under the guidance of the United Statesʹ National Cooperative Highway Research Program (NCHRP) to model light‐ duty vehicle (LDV) emissions as a function of the vehicleʹs operating mode. It is an instantaneous‐based stand‐alone nanosimulation and physical‐based emissions model, able to predict emissions for a wide variety of vehicles, and its main purpose is to predict vehicle exhaust emissions associated with different modes of vehicle operation such as idle, cruise, acceleration, and deceleration. Nevertheless, the model is highly complex and detailed, takes into account engine power, includes aspects of vehicle operation such as variable starting conditions (cold‐start, warm start) and off‐cycle driving, various states of condition (e.g., properly functioning, deteriorated, malfunctioning) etc., while operates on a temporal level which is similar to that of other instantaneous models. Moreover, as the CMEM model has been
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ADVISOR (Advanced vehicle simulator) ‐ was originally developed collaboratively by the US Department of Energy and the US National Renewable Energy Laboratory (NREL). In general, this instantaneous model requires a two steps input. Firstly, it requires a definition of the vehicle using measured or estimated component and overall vehicle data. Secondly, it requires a speed‐time trace, combined with road gradient, over a pre‐defined test route. ADVISOR was created in the MATLAB/Simulink environment. MATLAB provides an easy‐to‐use matrix‐based programming environment for performing calculations while Simulink can be used to represent complex systems graphically using block diagrams. ADVISOR employs a unique combination of backward and forward‐facing simulation attributes. A purely backward‐facing simulation propagates a high level requirement (e.g. change from X to Y speed in Z seconds) linearly backward through a series of systems (e.g. vehicle wheels transmission engine) (Markel, et al., 2002). Finally, it is noted that the ADVISOR model is usually used to simulate single‐vehicle responses, while focuses on light cars and does not contain maps for HDV.
MOVES ‐ MOtor Vehicle Emission Simulator (MOVES) ‐ is a U.S. Environmental Protection Agency’s (EPA’s) state‐of‐the‐art tool for estimating emissions from highway vehicles. The model is one of the most advanced multi‐scale emission models based primarily on a modal‐ approach and engine power demand, and on analyses of millions of emission test results and considerable advances in the EPA’s understanding of vehicle emissions. MOVES2010b, is EPA’s latest motor vehicle emissions model release, and in addition it can estimate volatile organic compounds (VOCs), nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), carbon monoxide (CO), and other precursors from cars, trucks, buses, and motorcycles. MOVES is a complete and accurate model to use. It also includes calculations for evaporative emissions, while although Heavy‐Duty Vehicles are examined, in comparison with the volumes of data available for light‐duty vehicles, the amount of data available for heavy‐duty vehicles is small. The main disadvantage of the model is that it has been developed in the United States and calibrated based on local environmental factors as well as vehicle fleet mixture. Its implementation in European countries would require similar calibration studies that capture the environmental and vehicular realities in each locality. Yet it provides a rather good framework for a similar model to be developed and implemented in Europe.
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EMPA ‐ is an instantaneous model that was derived from the German‐Swiss‐Austrian‐Dutch‐ Swedish cooperation for the “Handbook Emission Factors for Road Transport” (HBEFA) and the former European ARTEMIS and PARTICULATES projects. Using advanced measurement and modelling techniques, which are reliant upon knowledge of a number of test parameters, high‐frequency measurements, and the solving of a series of differential equations, it is possible to estimate emissions from individual vehicles over short time scales. Weilenmann et al. (2001) have developed EMPA, a mathematical model of the measurement system which can then be ‘inverted’ or solved in order to reconstruct the original emission signal in the exhaust pipe from the one measured at the analyzer. This process increases in complexity with the level of exhaust dilution used. In essence, with EMPA an attempt has been made to correct the emissions signal for light‐duty vehicles. EMPA model is quiet detailed one, but requires the dimensions of the exhaust gas system of the tested cars and measured modal data on the exhaust gas volume flow. Furthermore, as these models do not include the dynamic behavior of the engine, a dynamic emission model is needed. Another element, which is of great importance in terms of the emissions level, is catalyst behavior, and the catalyst has its own dynamics (i.e. oxygen storage). Thus, besides the engine model, a model for oxygen storage in the catalyst has to be added. The inclusion of a dynamic variable and/or a catalyst model should lead to improvements in the existing model (Atjay, et al., 2005). Finally, the main disadvantage of the model is that it has been developed only for light‐duty vehicles.
TEE (Traffic Energy and Emissions) ‐ is an adjusted average speed emission model that developed by ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), offers a variety of alternatives to the ‘classic’ average speed approach for the estimate of vehicles emissions as a function of their kinematics. It offers a variety of alternatives to the ‘classic’ average speed approach for the estimate of vehicles emissions as a function of their kinematics. Such alternatives include (i) a detailed ‘speed cycle’ Model; (ii) a reconstructed ‘simplified cycle’ Model; (iii) an ‘intermediate’ approach called ‘Corrected Average Speed’ (CAS). The limitations of Detailed Speed Cycle model are in the difficulties of obtaining data on speed profiles of vehicles and in the accuracy of the instantaneous emission functions. Real speed profiles can be obtained through time consuming and relatively expensive field measurement campaigns by using instrumented vehicles for the recording of speed and acceleration – yet this limitation is temporary as in the near future a large number of vehicles will be equipped with GPS equipment thus providing accurate location and speed data. The modelling alternative is the use of traffic simulators capable of microscopically representing the
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] behaviour of single vehicles (or small classes of vehicles) thus allowing the estimation of ‘likely’ speed profiles along the links of a network (Negrenti, 1996). Moreover, the main limitation of the Simplified Cycle Model is that the time spent during acceleration and deceleration is turned into a number of acceleration episodes on the basis of a driver behaviour hypothesis and acceleration rates derived from experimental data. The Corrected Average Speed (CAS) model seems to be the possible optimal ‘compromise’ between the need of having a ‘simple’ model (not requiring too complicated inputs) and the necessity of not losing accuracy in dealing with delicate problems as those encountered in the impacts assessment of measures on traffic. However, the model’s main limitation is that it is not up to date with the most recent EU standards.
COPERT 4: is an average speed model which estimates emissions of all major air pollutants produced by different vehicle categories, including heavy duty trucks. COPERT has been developed in the framework of several scientific projects, including: CORINAIR, COST 319 action, PARTICULATES, MEET, ARTEMIS, etc. COPERT 4 estimates emissions of all major air pollutants (CO, NOx, VOC, PM, NH3, SO2, heavy metals) as well as greenhouse gas emissions (CO2, N2O, CH4) produced by different vehicle categories, including passenger cars, light commercial vehicles, heavy duty trucks, busses, motorcycles, and mopeds. It also provides speciation for NO/NO2, elemental carbon and organic matter of PM and non‐methane VOCs, including PAHs and POPs. In addition, non‐exhaust PM emissions (tire, break and road wear) have been lately included. Furthermore, COPERT 4 introduces 6 main vehicle categories: Passenger cars, Light‐duty vehicles, Heavy‐duty vehicles (HDV), Buses, Mopeds, Motorcycles. For each category, one or more types of vehicles are defined, based on the engine size (e.g. engine <1.4 l, engine 1.4 to 2.0 l and engine > 2.0l). For each vehicle category and each type a technology/legislation is associated (pre‐Euro, conventional, Euro 1 etc.). The main advantage of COPERT 4 is that its methodology balances the need for detailed emission calculations on one hand and use of few input data on the other. In that way the methodology can also be used with a sufficient degree of certainty at a higher resolution too, i.e. for the compilation of urban emission inventories with a spatial resolution of 1×1 km2 and a temporal resolution of 1 hour. Furthermore, COPERT is continually updated, includes all major pollutants from exhaust emissions, as well as non‐exhaust PM emissions (tire, break and road wear), and has been selected by EEA as a base for the EMEP/EEA air pollutant emission inventory guidebook (http://www.eea.europa.eu/publications/emep‐eea‐emission‐inventory‐ guidebook‐2009). One of the main disadvantages of COPERT is that it does not include
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] parameters such as mean positive acceleration, or speed variation in the estimation of an emission factor. Furthermore, there is still an issue regarding emissions from light duty trucks, as the ARTEMIS failed to develop reliable emission factors for these vehicles. As the ARTEMIS database is quite extended in the coverage of technologies, it is expected that this can be corrected by rechecking the methodology. However, there is a handicap now as there is little information for upcoming vehicle emission standards, such as the new vehicle technologies at Euro V for passenger cars and Euro V, VI for heavy duty vehicles. The emission factors now included in COPERT for these technologies are based on emission reductions over Euro IV, based on the differences in the emission limits between the two technologies. However, this is only a best‐guess approach as the electronic control of recent vehicle technologies makes it difficult to estimate the actual emission performance, without actually testing each specific technology implemented in new vehicles.
VERSIT+ ‐ that was developed by TNO is used to predict emission and energy use factors that are representative for vehicle fleets in different countries. Emission factors are differentiated for various vehicle types and traffic situations, and take into account real‐world driving conditions. The model originally predicted vehicle emissions as a function of propulsion energy using Euro test emissions data (as a multiple linear regression model). However, it has evolved and the new versions are based on average speed algorithms. This suite of models is capable of predicting emission factors and energy use factors that are representative for vehicle fleets in different countries. Emission factors are differentiated for various vehicle types and traffic situations, and take into account real‐world driving conditions. As a consequence, it requires a relatively detailed driving pattern, and it could be very complex. Finally, if used in conjunction with microscopic traffic simulation models, the quality of the generated driving patterns could be an issue.
Two of the most frequently used types are average speed models and instantaneous models. However, modal models are in general hardly more precise than the average speed models and have the disadvantage of being much more complex to develop. Indeed, validation at high temporal and spatial resolution (e.g. second by second), which is the resolution at which the most complex models operate, often leads to larger deviations when compared with more aggregate results (Robin, et al., 2010). Moreover, a review of the available validation studies showed that usually approaches are restricted to specific models. Two of the models that appear more frequently are MOVES (18%) and COPERT (16%), which reflects the common use
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7.3 Nature of the trial This is a simulation field trial that is based on the mesoscopic VISTA DTA model. Due to limited budget limitations no extensive calibration will be conducted on the network due to limited resources to conduct a detailed data collection system for the roadways of the network. The aim is to produce a framework to analyze transport networks where multiple traveler route choice objectives are implemented, e.g., travel time, fuel consumption, air quality, and generalized travel cost. This study aims to produce some initial insights of the impact of short events where a percentage (%) of travelers will choose to change their routes based on fuel consumption while the remaining travelers will stay at their original routes – as though they are not aware that some travelers have switched routes.
A set of parametric experiments are presented on two transportation networks: the greater Nicosia, Cyprus network and the downtown Austin, Texas (TX), USA network. The Austin, TX network implementation is included here such that a comparative analysis can be made on two diverse networks. The implementation of these models using VISTA is presented in subsequent sections.
7.3.1 Geography of the Nicosia Simulation Field Trial The VISTA DTA model covers the same area with the VISUM transportation planning model developed by the MCW‐PWD for the year 2010. The greater Nicosia network as implemented in VISTA is depicted in Figure 15. The Nicosia model features 26,443 OD pairs, 25,029 links and 58,678 trips during the AM peak‐hour.
In addition, this study included the implementation of the enhanced VISTA simulator for the downtown Austin, TX, USA network for comparative analysis. The downtown Austin network features 3,109 OD pairs, 1,574 links and 62,836 trips during the 2‐hour AM peak‐period that represents a sub‐area of the regional planning model maintained by the Capital Area Metropolitan Planning Organization (CAMPO). However, the presented number of trips and network properties do not necessarily reflect the MPO’s current demand estimations and assumptions.
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(a) Downtown Austin, TX. (a) (b) Nicosia, Cyprus
Figure 15 Nicosia, Cyprus and Austin TX, USA Networks implemented
Even though both considered networks have a comparable number of trips, Nicosia covers a larger geographic area, and the topology is such that more alternative routes are present.
7.3.2 Time Schedule of the Nicosia Simulation Field Study The first phase (Phase I) of the simulation study was completed by the end of July, 2013. The implementation of VISTA in Nicosia and Austin,TX started at the end of Januray, 2013 when the Cyprus Public Works Department provided to CTL the data from their updated transportation planning model VISUM that included: an updated GIS and roadway geometry, bus route and schedules, and an updated OD matrix ‐ The greater Nicosia VISUM model for the AM peak period developed for the year 2010 by the MCW‐PWD has been provided to CTL as part of its participation in the REDUCTION project on 2013‐01‐28. The second phase (Phase II) of the simulation study will be completed by June, 2014.
7.4 Hardware and Software Characteristics of the Simulation Environment • Operating System: The VISTA software is operating in a Linux environment. The VISTA Transport Group Inc. has a license agreement with CTL to provide support and access to the VISTA software. • Access to VISTA: Based on a client‐server. Each user has an account and password. • Server: SGI Altix 4700 with 40 processors. • Database: Postgres • Input Data Formats: GIS, .csv, Access database format
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• Output data format: GIS, .csv, HTML • VISTA Web Interface: Used to access VISTA, input and output data in table format (.csv, HTML). It is also used to execute various modules of the VISTA software and search the input and ouput databases using SQL queries. • VISTA Java GIS interface: Used to access VISTA, input data via the GIS editor and produce output in a GIS spatial representation and animate the traffic flow propagation based on the mesoscopic traffic simulator. The Java GIS interface (also referred to as the Editor) is accessed through the web interface.
7.5 Description of the input The main data needs and implementation steps to implement VISTA under REDUCTION are outlined next:
7.5.1 GIS and Roadway Geometry • The transport network is digitally represented through GIS geospatial software that is periodically updated by the MCW‐PWD. VISTA utilises its own GIS model that interfaces well with commercial packages such as ARCGIS. • Roadway geometry: Number of lanes, capacity, grade, curve characteristics • The GIS data and roadway geometry will be imported into VISTA from the updated 2010 Nicosia VISUM model – a transportation planning software ‐ that was recently developed by the MCW‐PWD. The GIS and roadway geometry data were imported directly from the MCW‐PWD VISUM transportation planning model for Nicosia in February, 2013.
7.5.2 Bus Data • Bus Routes: bus route number, consecutive roadway links that comprise the bus route • Bus Schedules: Bus route start time and end time (s), bus frequency (s) • Bus stops: bus stop number, roadway link number, dwelling time (s), bus‐stop geolocation mapped to the roadway link via map matching. The bus routes and schedules were imported into VISTA from the MCW‐PWD VISUM model that was provided to CTL on the 28th of January, 2013.
7.5.3 Traffic control Data • Signal timing: Red, Yellow, Green intervals in (s). • Stop sign: whether the non‐signalized intersection is operated via a Stop sign or not • Yield sign: whether the non‐signalized intersection is operated via a Yield sign or not.
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• Speed limit: the speed limit in (Km/h). • Lane Movement Designation: The vehicle allowable movements as designated per lane. • Lane Vehicle class prohibitions: Utilization of the roadway lanes per vehicle category (auto, bus, truck). The traffic control data were input manually from the VISTA model of 2010 that was developed for the Nicosia Integrated Mobility Master Plan Study. It is noted that VISUM does not model traffic signals explicitly.
7.5.4 Historical Traffic Flow data • Static OD Matrix: The MCW‐PWD produced a new OD matrix in 2012 based on 300 traffic zones for personal auto trips and bus trips. This OD matrix is hourly based on a 24‐hour basis. • Dynamic OD Matrix: CTL produced an estimate of the dynamic OD matrix using the Nicosia VISUM static AM peak period OD and historical 15‐minute traffic counts. • Historical Traffic Counts: MCW‐PWD conducted 15‐minute traffic count studies at various locations for the Nicosia IMMP study and the follow‐up study. These traffic counts were used to develop an estimate of a typical AM peak 15‐minute time interval Dynamic OD matrix.
7.6 VISTA Software Update with Environmental Modeling Capability The VISTA DTA simulator was necessary to be updated in order to be able to integrate the use of environmental models. The following environmental models were employed to be interfaced with VISTA. • An energy consumption function based on a speed-emission curve fits from the MOVES- based data, the US-EPA approved mobile-source emissions modeling package • Parametric Modeling of Energy Consumption in Road Vehicles (PAMVEC) model • A vehicle specific power (VSP) based fuel-rate model
MOVES Curve Fit‐based Fuel Consumption Model The implementation of MOVES using the VISTA DTA model was accomplished through the use of a set of non-linear energy-speed calibrated curves. The energy-speed functions employed in this study have been borrowed from – they were been calibrated for the Austin, TX network but not for the Nicosia model. These fuel consumption functions employed in this study were estimated through speed-energy consumption curve fitting and regression analysis using data extracted from the MOVES model and its default US national database. The energy consumption models as functions of average link speed s are:
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where,
TEC = Total energy consumption (kW hr/mi) ICEV = Internal combustion engine vehicle s = average link travel speed (mi/hr)
Such curves could be generated also in Nicosia and any other City in Cyprus using the local environmental and vehicular data.
The PAMVEC Model The Parametric Modeling of Energy Consumption in Road Vehicles (PAMVEC) approach to modeling vehicle energy consumption is based on the road load equation, and can be described as follows:
The basic road load equation is,
Its components are defined as (assuming a horizontal road and hence a 0 % gradient),
Further, engine efficiency losses are accounted for by,
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The notations introduced and used in the above relations are as follows, along with their values used,
= road load power (W) = power loss due to aerodynamic drag = power loss due to rolling resistance = power required in acceleration = power loss due to road gradient = power required at the wheels = braking power loss = drive train power loss = engine power requirement = engine losses = total power requirement of the ICEV = accessory power = 1 kW
= gravitational acceleration = 9.81 m/s2
= vehicle mass = 1640 kg
= density of air = 1.2 kg/m3
= engine efficiency = 0.17
= transmission efficiency = 0.9
= frontal area = 2.5 m2
= rolling resistance coefficient = 0.01 = aerodynamic drag coefficient = 0.32
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Calculation of braking loss requires the average rate of energy storage within the vehicle inertia which is matched by an equal and opposite rate of energy dissipation and/or recapture, which can essentially be an expression for the average rate of energy storage, the form for which is similar to Paccel in the road load equation.
The VSP‐based Model This model uses an approach to capture additional characteristics of the traffic such as its dynamic nature and fuel efficiency of vehicles, and uses the VSP bins in the form of bin distribution to represent fuel consumption per unit of time. VSP (kW/ton) is defined as the instantaneous tractive power per unit of vehicle mass. For a typical light‐duty vehicle, VSP for a constant horizontal road surface (zero grade) can be calculated by using the relation,
where,
v = vehicle speed (m/s) a = vehicle acceleration (m/s2)
From the VSP values, the normalized fuel consumption rate (NFR), the average fuel consumption rates divided by the idling rate (rate corresponding to the VSP bin of 0) can be derived as follows,
The fuel consumption rates (g/s) for the idling mode (FRidle) were estimated from the plots provided for the average fuel rates provided in the same study.
Ultimately, the total fuel consumption rate is derived from the above two consumption rates as follows,
This model considers the VSP, which has been regarded as a realistic metric for estimating fuel consumption and emission of vehicles based on the gross power per mass of the vehicle. Energy consumption was converted to petrol using a factor of and a density of
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Acceleration Computations The principal parameters used to produce estimates of energy consumption and emissions are link (or sub‐link) average speed and average acceleration. Since the VISTA DTA produces estimates only of the average link speed it was necessary to develop a technique such that the acceleration can be estimated from the characteristics of the Cell Transmission Mesoscopic (CTM) traffic simulator by monitoring the corresponding individual vehicle trajectories at every time step. As a first order model, CTM does not output acceleration, and the specific VISTA CTM simulator provides only link travel times per vehicle, which can yield the average speed experienced by the vehicle on the link. Although some traffic assignment models may output speeds at smaller spacing intervals, others, such as the link transmission model, do not contain enough information to do so. This method approximates speed changes on a link using the individual vehicle speed data the start and end of each link of the network.
The individual vehicle speeds were averaged to calculate the average speed of the link at every time step of seconds. Acceleration was calculated based on changes in average speed on links and between links at intersections. Formally, let be the path of vehicle . For every link , let be the length of , the gradient angle of , the time arrives on , and the time exits . Then vehicle has an average speed on , , of
The average link speed at some time is a function of the average individual vehicle speeds of vehicles on the link at . First define the set of vehicles on the link at :
Then the average link speed at , , is the average of the individual vehicle speeds of vehicles in :
The corresponding energy consumption for was calculated as
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where is the ICEV power consumption for a speed , an acceleration , and a gradient angle , where is the link following on . This model assumes that in the last time step spent on , accelerates to . A more realistic acceleration is difficult to determine from the first order flow model output of CTM. Time spent idling at intersections is included as the difference between and , and thus reduces the average link speed for the vehicle. For the purposes of emissions, average speeds less than , which might occur due to high congestion, were considered to belong to the idling state.
In the future, the CTM above methodology could be modified to estimate the acceleration by dividing the link at smaller segments (or embedding “location” sensors) such that the corresponding vehicle speeds are estimated at a less disaggregated level. This requires a major revision of the VISTA software and it is one of the methodologies that will be attempted for the second phase of the project.
7.6.1 Implementation methodology of the VISTA DTA with environmental modeling in Nicosia, Cyprus and Austin, TX, USA
The main steps followed for implementing the VISTA model with environmental modeling capabilities are: Step 1. Identify a set of environmental models that could be interface with VISTA; three initial models were identified and interfaced with VISTA as outlined above. Step 2. Estimate the DUE traffic flow state – representing a typical day under normal (recurring) traffic flow conditions – by implementing VISTA for the Nicosia and Austin networks. Step 3. Embed into VISTA the three environmental models PAMVEC, MOVES and VSP, independently from each other. Step 4. Conduct a set of parametric analyses using fuel consumption as the main parameter: 1) Given the DUE state from Step 2, produce an estimate of the time‐dependent shortets path for each OD pair based on fuel consumption using each of the environmental models chosen; the
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] fuel‐consumption TDSP is estimated separately for each model; 2) Assign a percentage of the demand for each OD pair based on an eco‐friendly Market Penetration (MP) rate of 5, 10, 15, 20, 25 %, respectively, to eco‐prone drivers (assuming that all will follow this potentially fuel efficient path); the remaining demand for each OD pair are assumed to stay at their original paths as determined by the base DUE assignment. Step 5. Execute the CTL simulator of VISTA for each eco‐friendly MP rate; this method is myopic as the fuel efficient path is expected to experience deteriorating traffic flow and fuel consumption conditions as the % of each eco‐friendly demand rises. The main objective of this exercise is to observe the impact of various levels of MP rates on traffic flow and fuel consumption conditions. During the second phase of the project (Phase II) this procedure will be modified such that the eco‐prone drivers are distributed at a set of fuel‐efficient paths per OD instead of only one path; this step will require a change in the enhanced VISTA software with environmental modeling. Step 6. Summarize the traffic flow and fuel consumption results in a report format. The methodology followed is illustrated in Figure 16.
Figure 16. VISTA framework for modeling eco‐routing impacts
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7.7 Description of the output The results of the implementation of VISTA with environmental modeling capabilities as applied to the Nicosia and the Austin, TX networks are summarized in this section.
The impact of the proposed eco‐routing strategies is analyzed following a three‐step process, and the corresponding results are presented in the following sections: In the first step, the Dynamic User Equilibrium (DUE) state is estimated for each of the considered networks, the Nicosia, Cyprus and the Austin, TX, USA, respectively. This DUE state reflects recurrent traffic conditions of a typical weekday ‐ The main output produced is the set of DUE TDSP paths for each OD pair at six‐second time steps. Given, the trajectory of each vehicle then the corresponding link average speed and acceleration is then estimated at 15‐minute time intervals. Subsequently, the corresponding energy consumption is estimated on a link by link basis for each of the environmental models embedded into VISTA. The second step involves the utilization of the VISTA TDSP to estimate the eco‐route for each OD pair per 15‐minute time period of the day for each MP rate and assigning the corresponding eco‐demand to the least energy path (see Figure 15). In the third step, the VISTA CTM traffic simulator is executed based on the new assignment to evaluate the impact of each MP scenario on traffic flow and environmental conditions. Link travel times are computed based on the departure time of each eco‐prone (eco‐routed) vehicle and assumed to remain constant during its trip.
A set of various MP rate levels were conducted from 0% to 100% per OD pair. It was observed that after a certain threshold is reached, after which each pf the two networks tested becomes very congested. Given these tested scenarios, the Nicosia, Cyprus network becomes overcongested at an MP rate of 25% and the Austin, TX network at an MP rate of 20%. In terms of VISTA performance each of these thresholds demonstrates that a lot of vehicles get stuck in the network for several hours and they do not complete their trip during the allocated assignment time period – they will finally reach their destination of we increase the simulation time accordingly; yet since this is an unrealistic scenario there was no need to do so. This implies that if an eco‐routing scheme is implemented then a set of eco‐paths would need to be estimated per OD utilizing a more efficient distribution of eco‐prone vehicles to these paths. This latter procedure is expected to be developed and embedded into VISTA during the second phase of the REDUCTION project. Next we present the main results of the parametric analysis for each network.
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Nicosia, Cyprus Network Results Using PAMVEC
Table 14. PAMVEC, Nicosia, Cyprus ‐ Network Results (15‐min assignment)
Avg. Eco- Netk VKT Netk Netk Energy/Ve Avg. MVs Speed route Trips (veh- TT Energy MVs SE h. TT/Veh. VKT SE/veh (km/veh (%) (vehs) km) (hrs) (kWh) (kWh) (kWh/veh) (h/veh) (km/Veh) (kWh/veh) ) 0 58678 372972 14218 284295 0 4.84 0.242 6.356 0.000 26.2 5 58678 373042 14010 285802 -451 4.87 0.239 6.357 -0.154 26.6 10 58678 372995 14736 288819 -258 4.92 0.251 6.357 -0.044 25.3 15 58678 372832 15713 293753 415 5.01 0.268 6.354 0.047 23.7 20 58678 372907 77329 295827 1032 5.04 1.318 6.355 0.088 4.8 Netk: Network, MVs: Moved Vehicles, SE: Saved Energy, VKT: Vehicle Km Travelled, TT: travel time, kWh: kilowatt‐hours, veh‐km: vehicle‐kilometers, km: kilometer, MP: Market Penetration Table 15. PAMVEC, Austin, TX ‐ Network Results (15‐min assignment)
Eco- Netk VKT Netk Netk Energy/Ve AVG. MVs Avg. route Trips (Veh- TT Energy MVs SE h. TT/Veh. VKT SE/veh Speed (%) (vehs) km) (hrs) (kWh) (kWh) (kWh/veh) (h/veh) (km/veh) (kWh/veh) (km/h) 16893 0 62836 9 10273 145645 0 2.32 0.163 2.689 0.000 16.4 16897 5 62836 6 9632 145181 -375 2.31 0.153 2.689 -0.119 17.5 16899 10 62836 0 10994 146993 -392 2.34 0.175 2.689 -0.062 15.4 16903 15 62836 3 13282 153526 545 2.44 0.211 2.690 0.058 12.7 16911 20 62836 7 17821 162686 2478 2.59 0.284 2.691 0.197 9.5 16919 25 62836 2 23279 169587 5294 2.70 0.370 2.693 0.337 7.3
Network Travel Time Impact of Eco‐routing
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Figure 17. Travel Time/veh (h/veh) for Nicosia, Cyprus
Figure 18. Travel Time/veh (h/veh) for Austin, TX, USA
The network‐wide travel time is depicted in Tables 12 and 13 for the Nicosia and Austin, TX networks, Respectively while Figures 16 and 17 depict the corresponding network average travel time per vehicle for each network, respectively. It is observed that at low (up to 15%) eco‐ routing MPs, the network travel time and average travel time remains relatively constant and increases dramatically at higher levels. The higher levels cause each network to become overcongested. While the average travel time in the Austin network increases gradually as a function of the MP, it remains fairly stable for MPs of up to 15% in the Nicosia network. The
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Energy Consumption at the Network Level
Figure 19. Nicosia ‐ Energy Consumption/veh (kWh/veh)
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Figure 20. Austin, TX, USA ‐ Energy Consumption/veh (kWh/veh)
Figures 18 and 19 present the corresponding network energy consumption per eco‐routed MP for the Nicosia and Austin, TX, USA networks, respectively. It is noted that for the Austin network a small reduction is observed when 5% of the demand is diverted to the eco‐paths using fuel consumption as a parameter. However, as more eco‐prone vehicles are assigned to the initially estimated least energy path per OD pair, the energy consumption increases at the network level. The Nicosia network experienced a higher energy consumption at all eco‐routed MPs. The results are somewhat intuitive: 1) Some of the links of the network are expected to become oversaturated as a result of assigning eco‐prone vehicles on least energy paths when a threshold is exceeded, resulting in higher energy levels for the network, and 2) The Austin, TX result at the 5% level indicates that some modest energy savings may be observed – this means that some networks may benefit from eco‐routing below a certain eco‐routing MP percentage.
It is also important to observe that the proposed modeling approach reflects short‐term impacts. In the long term, both eco‐prone and non‐eco‐prone drivers are expected to adjust to the resulting system conditions by finding new routes that better meet their objectives. The latter suggests the value of implementing new modeling approaches that incorporate energy consumption as a component of the cost considered by drivers.
Energy Savings for Eco‐prone Vehicles
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Figure 21. Nicosia – Energy Saved/Eco‐routed veh. (kWh/veh)
Figures 20 and 21 depict the potential energy savings for eco‐prone vehicles for the Nicosia and Austin, TX networks, respectively. The Nicosia and Austin, TX results suggest that eco‐prone vehicles assigned to least energy paths, may observe lower energy consumption levels up around 12‐13%, whereas at higher MPs they will experience higher energy consumption levels. These results are somewhat different from what is observed at the network level where especially in the Nicosia case, the corresponding network energy level increases at all eco‐ routing MPs. This may lead us to conclude that transportation policies maybe set such that they strike a balance between benefiting the eco‐prone travelers versus system‐wide impacts. It further points to the need for more setting up a comprehensive eco‐routing strategy prior to any real‐world implementation.
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Figure 22. Austin, TX, USA – Energy Saved/Eco‐routed veh. (kWh/veh)
Impact of Eco‐routing on Average Distance Travelled
Figure 23. Nicosia – Average Distance Travelled (km/veh)
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Figure 24. Austin TX, USA – Average Distance Travelled (km/veh)
OD path length Analysis ‐ In order to analyze energy savings as a function of OD path length, the OD pairs are clustered in categories based on the average path length; the corresponding energy savings are then estimated for each category. Figures 24 and 25 present the energy savings per OD distance for the Nicosia and Austin, TX networks, respectively. In both networks OD pairs that are in relatively close proximity – based on the average OD path lenth ‐ experience the least savings under all eco‐route MP scenarios. In the Austin network, the furthest apart OD pairs benefit the most, most likely due to the presence of more alternative routes that are not congested. OD pairs that are separated by path‐lengths of 2.4 – 4.8 km are the ones experiencing the highest energy consumption under the higher MP scenarios, possibly because of their central location in the network where the network is most congested. In contrast, the best energy savings are observed for the category with the longest paths‐lengths (7.2 – 9.7 km) under all MP scenarios.
A similar trend is observed in Nicosia – the two longest path‐length categories experience most of the energy savings ( 24.1 ‐ 33.8 Km). All other categories experience the highest energy consumption – albeit only very modest under all MP scenarios – with the 15% noticeably worst
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Figure 25. Energy savings vs. OD path‐length(distance) Nicosia, Cyprus
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Figure 26. Energy savings vs. OD path‐length (distance), Austin, TX, USA
Eco‐path length analysis – Figures 26 and 27 present a comparative analysis of the eco‐path length (the one estimated per OD pair using energy) versus the OD DUE path‐length (representing the followed paths under recurring traffic conditions). The latter reflects the average distance traveled by vehicles, and may be considered as an estimate of the OD distance. Figures 26 and 27 present the change in Eco‐path length as a percentage of OD distance computed as follows:
where is the OD distance, and defines the eco‐path length.
A negative value implies that the eco‐path is shorter than the OD distance under recurrent conditions. In both networks, eco‐paths are longer for short‐distance OD pairs. This is likely a result of the availability of uncongested alternatives which, although longer, do not significantly increase travel time. For OD pairs with longer distance, the corresponding eco‐ paths are comparatively shorter, which is consistent with the higher energy savings described earlier. However, Figures 26 and 27 indicate that, in general, eco paths may be shorter or longer
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Figure 27. Change in eco path length as a percentage of OD distance vs. OD distance, Nicosia, Cyprus
Figure 28. Change in eco path length as a percentage of OD distance vs. OD distance (a) Austin,
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TX, (b) Nicosia, Cyprus
7.8 Conclusions and Future Work We presented a modeling framework to assess the network‐level impacts of eco‐routing policies through the use of the VISTA Dynamic Traffic Assignment (DTA) model/software. A set of parametric analyses is used to estimate the network‐level impacts of eco‐routing policies, including traffic flow patterns, travel times and fuel consumption. The eco‐route computation is based on the PAMVEC fuel‐consumption model. The proposed framework was implemented on two transportation networks: the greater Nicosia (Cyprus) region (58,678 trips during the peak period hour and 25,029 links) and the downtown Austin, TX (62,836 trips during the 2 hour AM peak period and 1,574 links).
The methodology executes first the VISTA DTA software to estimate recurrent traffic conditions. Second it utilises customized software tools to compute the shortest eco‐routes and implement an eco‐routing policy within VISTA. Given this framework, a set of parametric analyses are conducted by altering the percentage of eco‐prone vehicles ranging from 5 to 25% ‐ although higher percentages were executed they are not reported here since they lead to non‐ realistic congestion levels. The parametric analysis consists of assigning a fraction of all vehicles departing within pre‐defined 15‐minute intervals to the shortest eco‐route (energy consumption). The software’s mesoscopic CTM‐based traffic simulator is used to evaluate traffic conditions under each proposed eco‐policy – representing short term demand changes; and assuming that the non‐eco‐prone vehicles are not aware of the route choices of the eco‐ prone vehicles. Although the scope of this work is limited to the analysis of short‐term impacts through simulation, the use of an equilibrium‐based DTA framework sets the basis for further research focused on characterizing an equilibrium solution accounting for both, eco‐prone and non‐eco prone drivers – a rather challenging task that it is worthwhile exploring in the future.
The parametric analysis suggests that both, system‐level and individual energy consumption, are sensitive to the number of eco‐prone vehicles. In most cases, system‐level energy savings only occur at very low MP levels, while individual level savings are possible for MP levels of 5% to 15%. The observed behavior is consistent with typical “selfish” routing strategies, where users select a path that optimizes an individual goal while disregarding the impact that their choice has on other drivers. From a system level perspective, the former can clearly lead to sub‐
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] optimal performance, and negatively affect all drivers. These results suggest that successful eco‐ routing policies should encourage the distribution of eco‐prone drivers among a number of eco‐ friendly routes – this strategy will be explored during the second phase of the Nicosia simulation project.
The strategy followed is to assign all eco‐prone vehicles to the least energy path for each OD pair while changing the percentage of eco‐prone vehicles. Regarding the characteristics of such eco‐routes, the numerical experiments suggest that the proposed strategy selects paths with higher speeds and lower travel times, searching to reduce the fuel consumption rates. The former takes the system out of its equilibrium state; in the long term, non‐eco prone drivers may alter their path choices accordingly, eventually leading to a different system performance. This phenomenon should not be ignored if such eco‐policy is implemented as it may yield in networks that become highly unbalanced when travelers become aware of other travelers’ route choices.
Although eco‐routes were not found to be consistently shorter than the paths chosen under recurrent conditions, OD pairs further away exhibited higher savings in terms of energy consumption for both analyzed networks. This is most likely related to the availability of uncongested alternative routes for these paths, and suggests that the overall impacts of eco‐ routing policies are likely to be network‐dependent.
There are a number of reasons that may explain why the energy savings observed across experiments are relatively minor. From a methodological standpoint, a possible factor is the use of instantaneous average link travel time for the eco‐route computation. As described earlier, this means that link travel times are computed based on the departure time of each eco‐prone vehicle and assumed to remain constant during its trip. Given that travel times in the simulation change along the trip, it is possible that individual drivers experience longer travel times than expected. It is important to notice that most real‐time applications will suffer from a similar limitation, which suggests that incorporating a measure of travel time reliability in the selection of the eco‐route may be desirable. Another modeling limitation is given by the use of the same eco‐route for all vehicles departing during the same DTA assignment interval (15 minutes).
Preliminary experiments conducted by finding eco routes every 5 minutes lead to slightly better results, in which eco‐prone drivers experienced savings for up to a 25% MP level. The former is
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Finally, it is important to notice that the long‐term impact of an eco‐routing policy will be given by a new equilibrium solution. The characteristics of such solution deserve further study, given that they involve finding multi‐criteria DUE conditions. However, the fact that energy consumption is closely correlated with total travel time suggests that the long term system state when eco‐prone vehicles are present may not differ much from the case in which all drives are non‐eco prone; possibly resulting in a different assignment anyway. The former can be addressed by developing and enforcing policies that lead to a system‐optimum solution, in which some drivers are willing to accept slightly longer travel times in order to reduce congestion throughout the network; and hence aid in the establishment of a carbn credit system as long as there is a robust system in place that can produce true estimates of each vehicle’s carbon footprint.
7.9 Obstacles in running the field trial The Nicosia simulation field trial was delayed due to the unavailability of the data from the VISUM model for Nicosia. This was accomplished with a few months delay. The MCW‐PWD provided this data to CTL on 2013‐01‐28. CTL decided to concentrate its efforts – utilizing additional personnel and committing much more time ‐ such that the simulation field trial be completed within the original schedule. As stated the Phase I of the field study was completed by the end of July, 2013.
7.10 Planning for Phase‐2 of the Field Trial The second phase of the Nicosia simulation field study will examine the following:
7.10.1 Nicosia VISTA‐DTA Simulation Field Trial Task Plan Task 4 [Continued from Phase I]. Conduct of parametric test runs [2013‐09‐01 – 2013‐12‐31] • Task 4.1. Complete the set of parametric analysis using the MOVES and VSP environmental models; summarize the results for fuel consumption and GHG emissions. • Task 4.2. Complete a set of results utilizing a 5‐minute time interval assignment analysis using the PAMVEC model • Task 4.3. Explore the potential of incorporating the COPERT 4 anvironmental model. The COPERT 4 model relies mostly on the link average speed, which is a direct input of VISTA DTA.
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Deliverables: • Report on the results of the VISTA DTA parametric analyses on Fuel Consumption and GHG emissions routing
Task 5. Conduct various environmental related scenarios [2103‐09‐01 – 2104‐06‐30] • Task 5.1 Develop a DTA model that can be based on a generalized cost function. • Task 5.2 Enhance the VISTA software to be able to execute a fuel consumption DTA DUE • Task 5.3 Execute the new VISTA DTA with fuel consumption using one of the environmental models presented. • Task 5.4 Conduct a comparative analysis between DTA assignments based on travel time, fuel consumption and GHG emissions Conduct DTA assignments using 5 minute time intervals and compare them to the 15‐minute assignments. • Task 5.5 Conduct DTA assignment using various conditions and potential policies: eco‐ routing under Incident conditions, designate a set of routes to be used only by eco‐prone vehicles, prohibiting other vehicles of using it; assign eco‐prone vehicles along asset of eco‐routes per OD pair; explore the impact of electric vehicles, fuel cells and natural gas; impact of mode changes by assigning travelers to the Nicosia bus system.
Deliverables: • Report on the results of the VISTA DTA environmental related analyses
Task 6. Present the VISTA DTA model for Nicosia to the REDUCTION consortium including the MCW‐PWD • The results from Phase I will be presented to MCW by the end of September, 2013 • The results from Task 4 will be presented to MCW by the end of December, 2013. • The preliminary final results of Task 5 will be presented in April, 2014 during the REDUCTION consortium meeting that will take place in Cyprus. • The final results will be presented in June, 2014 to MCW.
Deliverables: • Presentations to MCW, 2013‐09, 2013‐12, 20014/04, 2014/06 Task 7. Summarize the results in a Report [2014‐04‐01 – 2014‐06‐30] Deliverables: • Final Report
Cyprus Field Trial Schedule and Deliverables
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Table 16. VISTA simulation field trial schedule for phase II.
Month 1 2 3 4 5 6 7 8 9 10 Task 4 xxxx xxxx xxxx xxxx Task 4.1 xxxx xx Task 4.2 xxxx Task 4.3 xx xxxx xxxx Task 5 xx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx Task 5.1 xxxx xxxx xxxx Task 5.2 xxxx xxxx Task 5.3 xxxx xx Task 5.4 xxxx xxxx Task 5.5 xxxx xxxx xxxx xxxx xxxx xxxx Task 6 x xx x Task 7 xxxx xxxx xxxx
Table 17. Nicosia simulation field study‐ Phase II eliverables and milestones
Deliverables Authors Date Task 4 D.5.6.1 Report on the results of the VISTA DTA parametric CTL, UTH 2013-01-31 analyses on Fuel Consumption and GHG emissions routing using the PAVMEC, MOVES, VSP and COPERT 4 environmental models Task 5 D5.6.2 Report on the results of the VISTA DTA environmental CTL, UTH 2014-06-30 related analyses Task 6 D5.6.3 Presentations to MCW CTL, UTH 2013-09 2013-12 2014-04 2014-06 Task 7 D5.6.4 Final Report CTL, UTH 2014-06
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8. UTH’s Simulated Trial: Phase‐1
8.1 Introduction This simulated trial is an essential part of CTL’s field trial aiming at investigating and measuring the impact of V2V communications in the total emissions of CO2, i.e., whether this mode of communication can indeed offer any advantages in reducing CO2. It is widely known and solidly validated, that V2V mode of communication is very successful in terms of achieving driver and vehicle safety. There also studies [3] that have promoted this type of communication for reducing traffic congestion, but there is a significant gap in the literature concerning the impact of V2V on eco‐friendliness [11]. Since, CTL’s field trial can only deploy a limited number and type (only buses) of vehicles, it was mandatory to complete this field trial with an extensively studied simulated scenario, where we will have the opportunity to experiment with various parameters (across a range of values) affecting the transportation efficiency in terms of CO2 reduction. In what follows, we describe in details the simulation configuration, and give a representative set of plots depicting the obtained results.
8.2 Nature of the trial We developed our study using the hybrid simulation framework Veins (Vehicles in Network Simulation) [7],[9], which is the composition of the network simulator OMNET++ and the open source road traffic simulator SUMO. Our study is based on simulating an accident in a short distance from the vehicles destination, providing one possible exit. The reason for selecting the vehicles’ destinations close to the accident is because we are interested in quantifying the impact of V2V communications on CO2 emissions independently of the travel time. Had we selected very distant (from the accident site) destinations, then it would be apparent that the emitted CO2 from the detouring vehicles would be dominated by the emissions due to the long travel. We consider our measurements using three possible scenarios:
(A) Enabled IVC (IVC), where the first vehicle that identifies the accident sends a message to the other vehicles after waiting for 15 seconds, and those who receive the message change their route if possible.
(B) Disabled IVC (NIVC), where vehicles with access to the exit, after waiting for 3 minutes change their route as illustrated in Figure 322. The rest of the vehicles are trapped behind the
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(C) All vehicles are blocked (BA), and queued behind the accident, as shown in Figure 311.
The trapped vehicles in all competitors are expected to wait for 2 minutes, 5 minutes or 10 minutes before they shut their engine down. So, we can model the situations where the drivers are quickly convinced that they are indeed trapped (2 minutes before they shut the vehicle engine down), or when they expect that the road will be freed in a reasonable time (5 minutes or 10 minutes). We understand that, the BA option with the vehicles shutting the engine down in 2 minutes is expected to exhibit the least CO2 emissions, because the vehicles are standing still (least petrol consumption) and fast enough they turn the engine off.
Our experimentation takes place in a part of the city of Erlangen as shown in Figure 299. The reason for that is there are available maps for that city that are appropriate for our simulation, i.e., not too many options for detouring, and also we can have a means for comparison with earlier results that used that map. Nevertheless, the location of experimentation is not an essential issue, and it leaves the obtained results unaffected.
The yellow arrow indicates the area where the accident occurred, and Figure 3030 shows the exit path that vehicles follow after becoming aware of the incident.
Our experimentation set of vehicles consists of light cars of 1,300kg, and heavy cars (buses/trucks/…) of 10.000kg. As emission models, we considered only the not aggregated ones that are described in [1], and in particular only the EMIT and SIDRA‐Inst (we will denote it by SIDRA) models since they produce meaningful results measured in g/sec and ml/sec, respectively. For the EMIT model we calculate CO2 emission parameters according to [1]. More specifically, we use the Category 9 vehicle, e.g., a ʹ94 Dodge Spirit, (for a description of categories see [1]) for our light vehicles, and make the assumption that the emission of heavy vehicles, is 1.5 times the emission of the light vehicles. For the SIDRA‐Inst model as described in
[2] and its references, we calculate Rt (where Rt= 0.333 + 0.00108∙v2t+ 1.2∙at+ 0.1177∙θt is the total tractive force required to drive the vehicle), for 1,200kg vehicles and 10,000kg vehicles to differentiate light from heavy vehicles emissions. Our experimentation is based only in instantaneous models and on the 2 aforementioned ones which are closest to our needs. The other models mentioned in [2][1] are not appropriate, because the SP (Specific Power) model for instance instead of estimating emission rates or fuel consumption directly, is defined as the instantaneous power per unit mass of a vehicle. The Joumard model does not specify the unit of
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We need to emphasize here that – based on earlier studies and results – we consider the EMIT model the most appropriate and realistic for our evaluations.
Table 18: Simulation parameters.
Independent parameter Range of values Default value Model EMIT, SIDRA‐Inst EMIT Velocity (Km/h) 50, 70 50 Number of vehicles 40, 100, 200 100 Engine Stop After (minutes) 2, 5, 10 5 Percentage of heavy and light (30%,70%), (50%,50%), (70%,30%) (30%,70%) vehicles (heavy, light) Scenario IVC, NIVC, BA IVC
Figure 29. The arrow indicates the area where the accident occurred.
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Figure 30. The re‐route followed by vehicles after becoming aware of the incident.
Figure 31. A screenshot of the All Blocked scenario with 200 vehicles.
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Figure 32. Vehicles with access to the exit reroute after waiting for 3 minutes.
8.3 Description of the input The input to the simulation are various values for the independent parameters described in Table 18, and also a map of the city. These inputs are fed into VEINS.
8.4 Description of the output VEINS produces the values of various output (dependent) parameters such channel busy time, number of rebroadcasts, CO2 emission, distance traveled, maximum/minimum speed, maximum/minimum acceleration, packets lost, and so on. In our investigation, we are interested in the total CO2 emission for the EMIT (grams/sec), and the SIDRA‐Inst (mL/sec) models, and also the totalCO2 emission per meter covered by vehicles.
8.5 Obstacles in running the field trial Being a simulation study, it did not present significant challenges or obstacles for its completion. The only concern was to select appropriate value for the input parameters so as to guarantee that they reflect a configuration observed in the real world. Even though the literature on this topic is poor, we used as our guide a couple of articles such as [9] and [10] that have described a solid methodology.
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8.6 Obtained Results This section presents a comprehensive subset of the obtained results, and provides explanations and intuitions of the observed patterns of behaviour. It consists of four subsections: the first illustrates the total average (for all vehicles) CO2 emission per meter covered in the simulation: in grams/sec for the EMIT, and in mL/sec for the SIDRA‐Inst; the second subsection contains the results for a moderate number of vehicles, i.e., 100 vehicles, the third for only 40 vehicles, and finally in the fourth subsection, we give plots that concern a relatively large number of vehicles (200 vehicles).
8.6.1 Total average CO2 emissions per meter traveled
In this section, we give the average CO2 emission per traveled meter, irrespectively of the vehicle type (lightweight or bus) and irrespectively of the “status” of the vehicle, i.e., blocked, waiting before detouring, immediately detouring. We realize and admit that the averages can destroy the patterns of behaviour when there are outliers in the distribution. Nevertheless, they do reveal some interesting patterns. In the next three subsections, we provide the CO2 emissions for each vehicle so as to draw safer conclusions. The unit of measure is as described for the EMIT and SIDRA‐Inst in the previous paragraph.
For the interpretation of curves, we have used the notation model‐vehicles‐heavyVehicles‐ speed, e.g., ivc‐100‐30‐70 means that we are evaluating the use of IVC for 100 vehicles, 30% of which are trucks/buses, with maximum speed equal to 70 Km/h. Apparently, the curves can be compared in triads, i.e., ivc‐100‐30‐70, ninc‐100‐30‐70, and ba‐100‐30‐70, because they present the performance of the three competitors for a specific configuration.
Figure 33 illustrates the results for the EMIT model. For any triad that we examine, we observe that IVC is the top performing option. Moreover, the performance of BA deteriorates exponentially with increasing time of being engine on, whereas the CO2 emissions of IVC increase only linearly.
Figure 34 illustrates the results for the SIDRA emission model. As far as the SIDRA model is concerned, we observe that BA is a better choice, because each vehicle consumes more petrol and thus emits more CO2 when travels than when standing still and keeping its engine on. IVC is the second best alternative. The two options become equivalent for the case when keeping the engine on for 10 minutes. All competitors exhibit linear CO2 emissions growth with increasing time of the engine being on.
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0,9
0,85 ba-100-30-50 nivc-100-30-50 0,8 ivc-100-30-50 ba-100-30-70 nivc-100-30-70 0,75 ivc-100-30-70 ba-100-50-50 nivc-100-50-50 0,7 ivc-100-50-50 ba-100-70-50 0,65 nivc-100-70-50 ivc-100-70-50 ba-100-50-70 0,6 nivc-100-50-70 ivc-100-50-70 ba-100-70-70 0,55 nivc-100-70-70 ivc-100-70-70
0,5
0,45 2min 5min 10min
Figure 33. Average CO2 per meter traveled for the EMIT emission model.
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1,6 ba-100-30-50 nivc-100-30-50 1,5 ivc-100-30-50 ba-100-30-70 nivc-100-30-70 1,4 ivc-100-30-70 ba-100-50-50 nivc-100-50-50 1,3 ivc-100-50-50 ba-100-70-50 nivc-100-70-50 1,2 ivc-100-70-50 ba-100-50-70 1,1 nivc-100-50-70 ivc-100-50-70 ba-100-70-70 1 nivc-100-70-70 ivc-100-70-70
0,9
0,8 2min 5min 10min
Figure 34. Average CO2 per meter traveled for the SIDRA emission model.
8.6.2 Moderate vehicle density [100 vehicles] For this configuration, with 100 vehicles being involved in the simulation, we examine the impact of various independent parameters. We note here that we have collected the simulated results for the Cartesian product of the values of the independent parameters, but here we record only the most representative.
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Impact of truck percentage
BA
NIVC
IVC
Table 19. Comparison of BA, NIVC, IVC for 100 vehicles, 30% trucks, 50 Km/h and 5 minutes engine on.
Apparently (Table 19), IVC has better performance because less vehicles are trapped. BA is comparable to IVC even though we expected it to be better, since they are stopped (low emissions) and do not travel the distance that the IVC vehicles do.
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BA
NIVC
IVC
Table 20. Comparison of BA, NIVC, IVC for 100 vehicles, 70% trucks, 50 Km/h and 5 minutes engine is on.
Comparing Table 19 to Table 20, we see that increasing the percentage of heavy vehicles the performance gap between BA and IVC widens, because BA’s vehicles do not move at all, but the latter maintains its emissions less than 2000, whereas NIVC more than 2000.
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BA
NIVC
IVC
Table 21. Comparison of BA, NIVC, IVC for 100 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on.
When we have equal percentage of light vehicles and trucks (Table 21), IVC is the clear winner compared to NIVC, and performs equally well to BA.
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Impact of speed
BA
NIVC
IVC
Table 22. Comparison of BA, NIVC, IVC for 100 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on.
In the next experiment whose results are illustrated in Table 22 and Table 23, we compare the three methods with respect to the average velocity of the vehicles. We observe that the relative performance of the competirors is maintained with what we saw in Table 19, with the exception that the CO2 emissions are higher.
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IVC
50Km/ h
IVC
70Km/ h
Table 23. Comparison of IVC for 100 vehicles, 30% trucks, 5 minutes engine on and speed 50Km/h vs. 70Km/h.
Comparing Table 22 to Table 23 in order to understand the impact of speed on the IVC’s emissions, we confirm a rational results, i.e., that lower speed results in less CO2 emissions.
Impact of time the engine is on
IVC 2min
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IVC 5min
IVC 10mi n
Table 24. Comparison of IVC for 100 vehicles, 30% trucks, 50Km/h, and engine on 2min, 5 min and 10 min.
Table 24 presents the results of the investigation of the impact of `engine on’ the emissions. It is understood that only the trapped vehicles are affected. A longer period of ‘engine on’ will certainly result in slightly more emissions. We make analogous observations for the other two competitors, i.e., NIVC and BA.
Impact of CO2 emissions model
IVC EMIT
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IVC SIDRA
Table 25. Comparison of IVC for 100 vehicles, 30% trucks, 50Km/h, 5 minutes engine on: Emission models EMIT vs. SIDRA.
Finally, we need to quantify the impact of the emissions model selection (Table 25). It is clear – confirming the result in [2] – that under the SIDRA model the emissions are higher, almost double for the non‐blocked vehicles.
8.6.3 Small vehicle density [40 vehicles] For this configuration, with only 40 vehicles being involved in the simulation, we examine the impact of various independent parameters. We note here that we have collected the simulated results for the Cartesian product of the values of the independent parameters, but here we record only the most representative. For the simulations with 40 and 200 vehicles, we include only the IVC and NIVC competitors.
Impact of truck percentage
NIVC
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IVC
Table 26. Comparison of NIVC and IVC for 40 vehicles, 30% trucks, 50 Km/h and 5 minutes engine is on.
Table 26 versus Table 27 and Table 28 shows that IVC is clearly more beneficial to emissions reduction than NIVC as the percentage of heavy vehicles increases, since these vehicles emit more GHG, especially as they need to accelerate in order to follow the detour.
NIVC
IVC
Table 27. Comparison of NIVC and IVC for 40 vehicles, 70% trucks, 50 Km/h and 5 minutes engine is on.
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NIVC
IVC
Table 28. Comparison of NIVC and IVC for 40 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on.
Impact of speed
NIV C
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IVC
Table 29. Comparison of NIVC and IVC for 40 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on.
IVC 50Km/ h
IVC 70Km/ h
Table 30. Comparison of IVC for 40 vehicles, 30% trucks, 5 minutes engine is on and speed 50Km/h vs. 70Km/h.
Contrasting Table 29 to Table 30, we observe that higher speed will result in higher emissions
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Impact of time engine is on
IVC 2min
IVC 5min
IVC 10mi n
Table 31. Comparison of IVC for 40 vehicles, 30% trucks, 50Km/h, and engine is on for 2min, 5 min and 10 min.
Examining Table 31 in order to figure out the impact of ‘engine on’ on emissions for the case of IVC, we do not observe significant differences among the three time intervals investigated, because very few vehicles are trapped, and therefore this interval does play role in increasing the emissions.
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Impact of CO2 emissions model
IVC EMIT
IVC SIDRA
Table 32. Comparison of IVC for 40 vehicles, 30% trucks, 50Km/h, 5 minutes engine is on: Emission models EMIT vs. SIDRA.
Consistenly with what we observed in Table 25, Table 32 shows that the SIDRA model results in higher emissions.
8.6.4 Large vehicle density [200 vehicles] For this configuration with 200 vehicles being involved in the simulation, we examine the impact of various independent parameters. We note here that we have collected the simulated results for the Cartesian product of the values of the independent parameters, but here we record only the most representative.
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Impact of truck percentage
NIVC
IVC
Table 33. Comparison of NIVC and IVC for 200 vehicles, 30% trucks, 50 Km/h and 5 minutes engine is on.
NIVC
IVC
Table 34. Comparison of NIVC and IVC for200 vehicles, 70% trucks, 50 Km/h and 5 minutes engine is on.
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NIVC
IVC
Table 35. Comparison of NIVC and IVC for 200 vehicles, 50% trucks, 50 Km/h and 5 minutes engine is on.
Table 33, Table 34 and Table 35 present the results of the investigation of heavy vehicles percentage on emissions. Since the total number of vehicles is quite large (i.e., 200), the differences among the three competitors become now quite significant.
Impact of speed
NIVC
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IVC
Table 36. Comparison of NIVC and IVC for 200 vehicles, 30% trucks, 70 Km/h and 5 minutes engine is on.
IVC 50Km/ h
IVC 70Km/ h
Table 37. Comparison of IVC for 200 vehicles, 30% trucks, 5 minutes engine is on and speed 50Km/h vs. 70Km/h.
Table 36 and Table 37 confirm that the vehicles’ speed is a high‐impact factor on emissions. A 20Km/h increase in velocity might result in a gross 20% increase in emissions.
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Impact of time engine is on
IVC 2min
IVC 5min
IVC 10mi n
Table 38. Comparison of IVC for 200 vehicles, 30% trucks, 50Km/h, and engine is on for 2min, 5 min and 10 min.
Similarly to what we observed for smaller numbers of vehicles, Table 38 confirms that IVC is not impacted significantly by the time the `engine is on’.
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Impact of CO2 emissions model
IVC EMIT
IVC SIDRA
Table 39. Comparison of IVC for 200 vehicles, 30% trucks, 50Km/h, 5 minutes engine is on: Emission models EMIT vs. SIDRA.
Finally, for this large number of roaming vehicles, the SIDRA model results in almost double the amount of emissions with respect to EMIT’s emissions.
8.7 Trustworthiness of obtained results The academic community is aware of the somehow limited (w.r.t. the real executions) validity of the results obtained via simulations. No matter what the fidelity of the simulations is, there will always be some parameters of the physical world that the simulator did/can not manage to perfectly model. But, simulations are an indispensable part of systems’ evaluation, because they provide to the designer the tools and flexibility to experiment with various configurations of the system’s components.
REDUCTION partners did not have to develop custom simulators for investigating CO2 emissions, because there are quite a lot of academic and industrial‐strength simulators. Fortunately, the simulators used by the communications/networking communities, such as ns‐2 [6] and its successor ns‐3 [7], OMNET++, VEINS [9] and so on, are quite popular and established tools of networking research.
We only had to verify that our results match those that come from similar studies.
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Unfortunately for this purpose – but fortunately for the REDUCTION’s originality in investigations – there are less that a handful of articles investigating the impact of V2V communications in CO2 reduction.
Indeed, we trust our results since we did extensive simulations, and the obtained results match those that come for earlier studies, e.g., [10].
8.8 Planning for Phase‐2 of the Field Trial For the second phase of the simulated field trial, we have made preparations for the investigation of the impact of specific networking protocols (e.g., [4] and [5]) on CO2 emissions, and also for investigation of the impact of V2V communications on CO2 emissions in a distributed traffic regulation scenario, instead of the accident scenario that we have considered in the first phase.
8.9 Suggested Changes in REDUCTION’s architecture and methods We did not detect any problems with the architecture, communication protocols, and networking algorithms of the REDUCTION that need to be reworked before running Phase‐2.
8.10 Conclusions We examined the impact of Intelligent Transportation Systems (intervehicle communications) on CO2 emissions. We considered the GHG emissions as a first class citizen in the measurement tasks for transportation scenarios. We conducted a series of simulations using the VEINS simulation framework. We concluded that the IVC method, under a realistic emissions model (i.e., EMIT), outperforms its no‐IVC competitor, and even proves to be a better option than that of keeping the vehicle (voluntarily) trapped.
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9. TrainOSE Field Trial: Phase‐1
9.1 Introduction This field trial aims to multimodal transportation options for company customers. One of these options is the minimum CO2 routes where customer can choose the route with the minimum
CO2 emissions. Given the fact that: 1. Rail network doesn’t cover sufficiently the Greek territory, 2. the wide coverage of bus transportation systems lines, multimodal transportation options should include these two (bus, train) main modes of transportation. Input for this trail phase is the following set.
1. Transportation mode (train, bus, taxi etc.).
2. Detailed schedules (time, frequency etc.).
3. CO2 emissions for every possible trip combination.
4. Transportation cost.
The expected goal of this trial phase it to provide to the users a way to identify how to move around Greek territory with the most eco‐way among others modes of transportation
9.2 Nature of the trial The nature of the first trial is to provide an internet service for users who want to move over the Greek territory by visiting REDUCTION web site. Users – the pool of these users can be the original TrainOSE web site – visit REDUCTION Site and can get fully travel information for the entire Greek Territory.
First users’ wave came after a small promotion campaign in order to promote the multimodal Web Application. The primary action of our campaign was to send e‐mails (users who book train tickets online enforced to put their mail address) to invite users for trying our application. For these users we have some initial findings that could help us to sketch their profiles. Log file date : 15 Oct 2013 Number of Users:103 The following figures describe our users in terms of their relation with eco multimodal traveling.
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Figure 35. Age distribution.
Figure 36. Public transportation usage.
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Figure 37. Eco‐driving.
During users’ visit to our Web site, we asked them few more questions about their perception for various ECO multimodal traveling features.
1st question: What is the maximum affordable transfer time between different transportation means.
Figure 38. Answers chart to 1st question.
2nd question: What is the maximum affordable tickets price increase for ECO multimodal
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Figure 39. Answers chart to 2nd question.
3rd question: What is the maximum affordable additional travel time for ECO multimodal travel.
Figure 40. Answers chart to 3rd question.
4th question: What type of trip lengths, users are willing to use ECO multimodal transportation.
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Figure 41. Answers chart to 4th question.
We need larger sample to analyze it more in order to find hidden relations. Since the process is ongoing, it remains to be seen if the initial findings hold. However this small sample “says”:
• Users accept the idea of Multimodal ECO driving. • Users’ majority are willing to afford small transfer delays ‐ close to 0‐10 mins ‐ for more ECO traveling. • Users’ majority are willing to afford small additional ticket cost ‐ close to 0‐10%‐ for more ECO traveling. • Users’ majority are willing to afford small additional travel time ‐close to 0‐10%‐ for more ECO traveling. Users’ Perception is, that ECO Multimodal Traveling can fit to Medium – Long trip distances.
This field trial is a real study, because users select their way of multimodal moving. It is possible in the future to use historical data (storing procedure is ongoing) for simulation
The majority of Greek territory has been covered. Every city above 10000 inhabitants included in the system. The next phase of this trial is to include all Greek villages and small cities who served by some type of transportation system.
Following figures describe the coverage of Greek territory in terms of transportation means included in this field trial.
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Figure 42. Full coverage of Greek territory.
Website will be available during the entire program duration and it will be under continue
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TRAINOSE participates actively to the project, however other transportations organizations such as BUS organizations participate passively to the project by not providing information concerning their transportation means and schedules.
9.3 Description of the input For this trial phase we need a sequence of inputs. Since the objective is to provide users multimodal transportation via a web service we need algorithms for multimodal transportation and data. Minimum data requirements are:
Nodes. Nodes represent Geo locations for transportation points.
Links. Links represent all arcs between nodes and all possible schedules between these arcs. Each arch comes with its own features such as travel distance, travel time, fuel consumption etc.
CO2 emissions for every transportation mean per person/km. CO2 emissions calculation for trains it is a difficult task since we need data for all type of locomotives, for all train lines, for all recorded percentage number of travelers in comparison to train capacities.
Following paragraphs describe in much greater detail the necessary input for our trial field
1. Geo Locations of all transportation nodes.
Geolocations for all TrainOSE stations came through a standard procedure. Bus stations came through exhaustive data search over their websites and phone catalogs if these sites existed. See Appendix A for a detailed description of these nodes.
2. Links and travel schedules for all transportations means
From TrainOSE, schedules for trains came through an automated procedure. However, bus schedules had to be determined though manual procedure due to absence of any kind of formatted information. Other features such distance between tow network nodes, ticket prices, fuel consumption was gathered thought exhaustive manual procedures as well. See Appendix A for a detailed description of these links.
3. CO2 emissions for each transportation vehicle per traveler.
CO2 emissions was a difficult task to be calculated for trains. Since the trains in Greece use a
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combination of diesel locomotives and electric powered locomotives. Calculations for CO2 emissions for trains based on worst case scenarios. For buses, calculations based on data concerning the average fuel consumption of bus diesel engines and the average travelers per trip.
Calculated CO2 emissions for Trains and Buses ‐ Field Trial
Transportation Type CO2 gr per KM per Person Bus (Λεωφορείο ΚΤΕΛ) 50 Train (Τρένο) 22 Mini-Bus 80 Ταξί - Taxi 150
9.4 Description of the output The outputs of this field trial are the routes from one point to another with all travel features such as travel time, travel cost and travel cost in CO2. Users input their origin and destination and the way of traveling and gets back: 1. complete schedule, 2. Total Travel time, 3. Total travel cost, 4. Total CO2 emissions. Table 40 presents the above mentioned output for a customer who wants to travel from the city of Volos (central Greece) to the city of Kalamata (south Greece). Customers start their travel by walking from the origin address to the nearest station (bus, train, etc). Figure 43 presents the walking distance.
Figure 43. From the original address to the closest departure point.
Then, the customer uses Table 40 in order to reach the destination city. This table contains the following information:
From Point: This column describes trip starting point (global or local).
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To Point: This column describes tip ending point (global or local).
With Vehicle: This column describes the vehicle type (e.g., bus, train, ferry, metro, local bus).
Distance: This column describes distance of the specific trip branch.
Travel: This column describes travel time of the specific trip branch.
CO2 Kilos: This column describes GHG emissions of the specific trip branch.
From Ticket Travel To Point: With Vehicle: Distance(KMs) CO2 Kilos Point: Cost Time
3,7 CO2 1. ΤΡΑΙΝΟ ΤΡΑΙΝΟ Λάρισα Train(Τρένο) 74 3,6 48 Mins Βόλος Kilos
http://tickets.trainos e.gr/dromologia/#apo www.trainos =ΒΟΛΟ;pros=ΛΑΡΙ;date www.trainose.gr e.gr =2014-01- 28;rtn_date=2014-01- 28;trip=1
14,95CO2 2. ΤΡΑΙΝΟ ΤΡΑΙΝΟ Αθήνα Train(Τρένο) 299 17,8 258Mins Λάρισα Kilos
http://tickets.trainos e.gr/dromologia/#apo www.trainos =ΛΑΡΙ;pros=ΑΘΗΝ;date www.trainose.gr e.gr =2014-01- 28;rtn_date=2014-01- 28;trip=1
ΚΤΕΛ Αθηνών - 0,05CO2 3. ΤΡΑΙΝΟ Μετρό - Metro 5 1 25 Mins Αθήνα Λιοσίων Kilos
No Avail Info for www.trainos http://www.ametro.g ΚΤΕΛ Αθηνών - e.gr r Λιοσίων
4. ΚΤΕΛ KTEL-Bus(Λεωφορείο 124 CO2 ΚΤΕΛ Καλαμάτας 248 23 180Mins Αθηνών - ΚΤΕΛ) Kilos Λιοσίων
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No Avail Info for ΚΤΕΛ http://www.ktel http://www.ktelmessi
Αθηνών - messinias.gr nias.gr, 2721022851 Λιοσίων
Table 40. Traveling from Volos to Kalamata.
Example explanation:
1. Customer first takes TRAIN from Volos to Larissa 2. Train from Larissa to Athens 3. Metro from Train station in Athens to Bus station. 4. Intercity bus from Athans to the city of Kalamata.
Following figure presents the walking distance from bus station in the city of Kalamata to his/her destination address.
Figure 44. From arrival point to desired address.
9.5 Obstacles in running the field trial This trial field had no major problems to deal with. However few minor problems can be mentioned:
‐ Problematic CO2 emission calculation due to lack of detailed information for fuel
consumption of Diesel Locomotives and the exact representation of CO2 emissions per KWH in case of electric powered locomotives
‐ The absence of formatted inputs for bus schedules. Bus transportation systems in Greece are complete operated by the private sector. As a result of this, all bus schedules had to
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be estimated and the imported manually to the system.
9.6 Obtained Results The most profound result is the Web site itself and all data information that have been collected through a time consulting procedure. The following figures present the multimodal Web site for the needs of this first field trial.
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Selection of search type
Selection of Travel Type (CO2 included)
If selections done then press the search button
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If calculation is successful then this success window pop up
And the results can be displayed
CO2 emissions These are the results
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9.7 Trustworthiness of obtained results The software is up and running, and provides real‐time services to the users.
9.8 Planning for Phase‐2 of the Field Trial For the phase‐2 of field trial, we will:
1. Prepare a new service called TRAIN‐TAXI in order to offer multimodality as an extension to train transportation services. Service can be described as follows: Every train passenger can book a ticket for multimodal transportation consisted of train and a fleet of taxis – capacity varies from 4 to 7 persons. This service will be offered from origin address to the closest train departure point and from the train arrival point to destination address. The basic idea is to make train passengers to share taxis and minivans in comparison to one‐person taxi transportation. The next step of this phase is to use dial a ride algorithms to improve much more the number of dead KMs of taxis and minivans fleet. Before the real appliance of dial‐a‐ride algorithms we will run some simulations given the fact that all transportation data will be recorded. Based on these results, fleet size and other transportation features will be under adjustment as well. 2. Study locomotive drivers behavior in order to define which of them, drive in a non eco way. In this phase we have some initial results that came out from a basic analysis of electric power consumption metrics. However these results can be presented, due to its importance and some very bold findings of this very first look. The following figures present an initial introduction of these findings and provide feed for the next phase. In Figure 45 we see the distribution of energy consumption which is clrealy inefficient. Also, in Figure 46 and Figure 47, we observe the non‐uniform driving pattern that causes significant variations in energy consumption.
Figure 45. Energy –frequency distribution for electrified locomotives. (Domokos – Thessaloniki).
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Figure 46. Energy – energy consumption differences between (same type) locomotives. (Domokos – Thessaloniki).
Figure 47. Impact of driving behaviour in energy consumption.
In the second phase, these data will be analyzed in more detail, and we hope that eventually we
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9.9 Suggested Changes in REDUCTION’s architecture and methods No issues were detected that demanded changes in REDUCTION’s methods, algorithms and techniques.
9.10 Conclusions In the first trial, TrainOSE prepared software tools to offer multimodal transportation services.
These multimodal services include the dimension of eco‐traveling since the option of less CO2 emissions offered to users. These services will be extended in the future with a new service, of combination of trains, taxis and minivans and the usage of dial‐a‐ride algorithms. The ultimate goal is to reduce the total amount of CO2 emissions without to decrease the offered quality of travel to our passengers
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10. Risk analysis and lessons learned Following the best practises, REDUCTION has designed four field trials. One is run by TrainOSE to develop, test and enhance multimodal services in Greece by combining train services, with taxi and bus services. For the TRAINOSE pilot, risk analysis raises the issue of bus schedules. As mentioned before, these schedules handled manual and it is necessary to define an updating procedure. During the program operation, updating is part of the pilot itself, however we have to think possible ways of updating in the future after the program finish. From the technical point of view, the application itself is well prepared for the updates via Web APIs. These Web APIs came in the simple form of http://sitename?linkid=xxx@cost=xxxx@traveltime=xxxx. The most profound solution is to ask KTEL‐Companies to define an updating methodology. We have to make contacts with each Web administrator for each KTEL‐company in order to define updating procedures. Right now we can’t predict which of these web‐administrators are willing to provide these updates since they are not obliged to do it. It remains to be seen what could be their response. In case of negative results then TRAINOSE has to undertake the updating responsibility every 6 months.
The other field trial run by Bektra/FlexDanmark deploys a large taxi fleet in order to develop techniques for the reduction of the GHG emissions from vehicles, establishing environmental profiles of vehicle types, and also estimate GHG emissions based on GNSS measurements. Finally, the third field trial run by CTL (and its complementary simulation‐based trial run by UTH), aims at collecting real data for drive behaviour analysis, for hardware and communications/networking protocols testing.
For the Bektra/FlexDanmark, there are no risks associated with the second phase, and all those risks and open issues described in D3.1, D3.3 and D5.1 are appropriately addressed. Similarly, the simulated field trial by UTH recognized no issues that will affect the execution of the second phase of the trial.
As far as the TrainOSE’s trial is concerned, in phase II we finalized the multimodal scenarios and offered these scenarios to our customers. The finalization process depends on two main actions. The first one is the implementation of a native multimodal algorithm, enhanced, in terms of time windows usage. The second one is the precision enhancement over various cost metrics. For the first action the risk on non‐completion is low since it depends on the time we will spend, internally, on algorithms development. Given the fact of our previous experience in
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D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1] development process, we consider the risk for uncompleted multimodal algorithms as very low. For the second one, concerning various metrics, risk comes as result of the possibility of low metrics accuracy. For train lines, cost metrics can be defined in a precise way. We know precisely the oil consumption per KM per Locomotive and the electric power consumption. Tickets price is well know as well. This can be achieved due to unlimited access we have to all company internal resources (human and information). However a problematic situation can arise in case of non‐accurate information for KTEL buses. Right now this information came through an extensive search over web sites. Most of these sites are not well formatted in terms of information organization and the updating process isn’t established in a standard way. When we proceed to the second phase, we have to upgrade significantly the precision for all trip features such as oil consumption, tickets pricing and average number of customers per bus line. It is possible not to be in position to get precise information about these metrics from KTEL private entities. KTEL private entities might deny this information due to:
¾ Privacy misconception. They could think that these statistics can be used against them form other transportation companies.
¾ Competition misconception. They could think that there is not profit for them, to establish synergies with trains, since most of their lines apply to same customer pool.
Based on the above mentioned reasoning, the risk to not accomplish the task of enhancing the accuracy for cost metrics (for all transportation means and for all lines) can be considered as medium.
Trinite Automation’ field trial faces the danger of slow procedures run by municipalities and ministries. The development of the software and set up of necessary the hardware are not considered significant factors of failures, since Trinite Authomation has a long experience on the set up and running of systems.
Finally, the CTL’s field trial due to the delays caused by external “actors” is the most vulnerable part of REDUCTION. To avoid any future problems, the consortium has taken specific actions concerning the monitoring of activities and designed a very detailed plan of actions so as to guaranteed day‐by‐day progress of the field trial.
Table summarizes the identified risks, the actions taken to mitigate them and the implications for the project. Overall, we feel confident that there will be no events that will alter the scope of
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Risks Counter‐measures Implications FlexDanmark trial: no risks or N/A N/A addressed earlier TrainOSE trial: a) It is beyond TrainOSE’s a) volatile bus schedules control. Low risk: cooperation a) insignificant (we can resort with KTELs on API to manual entering the data) development b) algorithms for multimodal b) low risks, because software scheduling development is in‐house (we b) severe can always use more programmers) Amsterdam trial: a) slow execution due to It is beyond Trinite’s control severe ‐ no results obtained bureaucracy CTL trial: hardware not DELPHI’s personnel visits operating Cyprus many times. Close severe ‐ no results obtained monitoring of the progress UTH (simulated) trial: no N/A N/A risks
Table 41. A summary of risks for the field trials.
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11. Conclusion
Reduction of fuel consumption and CO2 emission in land transportation systems, which will have immediate positive economical and environmental impact, has become an important part of green technologies. Intelligent transportation systems, which aim to use information and communication technology in the transportation systems, are considered to be a major enabler for the future green ITS. This deliverable presented the results of the first phase of four real field trials and two simulated ones. Figure 48 (from [11]) depicts the set of approaches which have been internationally followed for ITS fuel efficiency and CO2 emission reduction.
ITS’s fuel efficiency and CO2 reduction approaches
Traffic reduction Fuel consumption and CO2 emission reduction
City plane; walking Improvement based Improvement based Improvement based distance on driver’s bahavior on infrastructure and on communication vehicle development technology
Public transportation Fuel‐efficient driving Electronics vehicle Cruise control improvement: Multi‐ promotion (DSRC in buses by modality by TrainOSE (FlexDanmark, TRI) DELPHI‐CTL)
Driving behaviour Road improvement; V2V communication promotion; car slope elimination (simulated by UTH) pooling
Platooning
Traffic signal management
Figure 48. Approaches for ITS fuel efficiency and CO2 emission reduction.
The figure highlights (in yellow background) which of them have been followed by REDUCTION’s field trials. The results described earlier confirmed that we can achieve this goal
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All three real field trials have managed to achieve the goals they have set initially by following – whenever possible – best practices adopted by earlier projects, or they have developed novel methodologies to address the specific needs raised by REDUCTION objectives. Despite some delays that will inevitably appear in any large‐scale project, these delays have been delt with in such a way that the project is still ‘in track’. The REDUCTION partners have also increased the breadth of investigation by conducting simulation studies to explore parameters which are not easy to investigare in details in real settings. Overall, the planning, and execution of the first phase of each trial promises that the second phases will also run smoothly and successfully.
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[35] Frey, H. C., Zhang, K., and Rouphail, N. M. 2008. Fuel Use and Emissions Comparisons for Alternative Routes, Time of Day, Road Grade, and Vehicles Based on In-Use Measurements. Environmental Science & Technology, 42 (7), pp. 2483-2489, 2008 [36] Barth M., Boriboonsomsin K., Vu A., Environmentally-Friendly Navigation, Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA, Sept. 30 - Oct. 3, pp. 684-689, 2007 [37] Bandeira, J. M., Carvalho, D. O., Khattak, A., Rouphail, N. M., and Coelho, M. C. 2012. A comparative empirical analysis of eco-friendly routes for peak hours. In Proceedings of Transportation Research Board Meeting, Washington DC, USA, January 2012 [38] Ziliaskopoulos, A. and S.T. Waller. “An Internet‐based geographic information system that integrates data, models and users for transportation applications.” Transportation Research Part C: Emerging Technologies 8.1 (2000): 427‐444. [39] Gardner, L. M., Duell M., and Waller S.T.. ʺA framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability.ʺ Transportation Research Part A: Policy and Practice 49 (2013): 76‐90. [40] U.S. Environmental Protection Agency. Motor Vehicle Emission Simulator (MOVES) 2010 User Guide. EPA420‐B‐09‐041. Office of Transportation and Air Quality, Dec. 2009. [41] Simpson A.G., Parametric Modeling Of Energy Consumption In Road Vehicles, Thesis. Submitted to the School of Information Technology and Electrical Engineering, The University of Queensland, February 2005 [42] Song, G., and Lei Y. ʺEstimation of fuel efficiency of road traffic by characterization of vehicle‐specific power and speed based on floating car data.ʺ Transportation Research Record: Journal of the Transportation Research Board 2139.1 (2009): 11‐20. [43] Daganzo, C. F. ʺThe cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory.ʺ Transportation Research Part B: Methodological 28.4 (1994): 269‐287. [44] “Fuel Properties Comparison.” Alternative Fuels Data Center. U.S. Department of Energy, Web, Accessed 6/2013.
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346.htm. [51] 2001Aspects of instantaneous emission measurementInternational Journal of Vehicle DesignVol. 27 (1‐4), pp.94‐104 [52] Atjay, D and Weilenmann, M. 2004. Compensation of the exhaust gas transport dynamics for accurate instantaneous emission measurement. Environmental Science and Technology. 2004, Vol 38 (19), pp. 5141‐5148. [53] Atjay, D, Weilenmann, M and Soltic, P. 2005. Towards accurate instantaneous emission models. Atmospheric Environment. 2005, Vol 39 (13). pp. 2443‐2449. [54] Barlow, T. J. 1997. The development of high speed emission factors. Transport Research Laboratory,. 1997. TRL Report PR/SE/333/97 (unpublished). [55] Barth, M., et al. 2001. Comprehensive modal emissions model (CMEM). version 2.02: User’s Guide. s.l. : University of California Riverside Center for Environmental Research and Technology, 2001. [56] Boulter, P G. 2001. The impacts of traffic calming measures on vehicle exhaust emissions. University of Middlesex. 2001. PhD Thesis. [57] Boulter, P G, McCrae, I S and Barlow, T J. 2007. A review of instantaneous emission models for road vehicles. Transport Research Laboratory. 2007, 267. [58] Boulter, P.G., McCrae, I. S. and Barlow, T. J. 2007. A review of instantaneous emission models for road vehicles. Transport Research Laboratory. 2007, 267. [59] De Haan, P and Keller , M. 2000. Emission factors for passenger cars: application of instantaneous emission modelling. Atmospheric Environment. 2000, 34, pp. 4629‐4638. [60] DETR, Dept. of the Environment, Transport and the Region, et al. 2000. The air quality strategy for England, Scotland, Wales, and Northern Ireland. London : s.n., 2000. Cm4548. [61] 2004Development of a Simulation Tool to Calculate Fuel Consumption and Emissions of Vehicles Operating in Dynamic ConditionsSAE Technical Paper 2004‐01‐1873, doi:10.4271/2004‐01‐1873 [62] EC. 2012. Cordis. [Online] 2012. http://cordis.europa.eu/telematics/tap_transport/research/16.html. [63] 2012. Climate Action. http://ec.europa.eu/clima/policies/transport/vehicles/index_en.htm. 2012. [64] Hassel, D., et al. 1987. Das Abgas‐Emissionsverhalten von Personenkraftwagen in der Bundesrepublik Deutschland im Bezugsjahr 1985. UBA Bericht 7/87. 1987. [65] Hauger, A. and Joumard, R. 1991. LPG pollutant emissions. Use of Compressed Natural Gas (CNG), Liquefied Natural Gas (LNG) and Liquefied Petroleum Gas (LPG) as fuel for internal combustion engines. Kiev, Ukraine : UN‐ECE Symposium, 1991. [66] Hausberger, S. 1998. Planung und Koordination zur Aktualisierung der Emissionsfaktoren fur Schwere Nutzfahrzeuge. s.l., Austria : Institut fur Verbrennungskraftmaschinen und Thermodynamik derTechnical University‐Graz, 1998. [67] 2003. Simulation of Real World Vehicle Exhaust Emissions. VKM‐THD Mitteilungen. s.l. : Verlag der Technischen, 2003, Vol. Heft/Volume 82.
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[68] Hausberger, S., et al. 2009. Emission Factors from the Model PHEM for the HBEFA Version 3. Graz University of Technology. s.l. : Report Nr. I‐20a/2009 Haus‐Em 33a/08/679, 2009. [69] Hausberger, Stefan. 2008. The Modell PHEM:Structure and Applicatons. Institute for Internal Combustion Engines and Thermodynamics, University of Technology Graz : JRC, 2008. [70] INRETS. 2012. [Online] 2012. http://www.inrets.fr. [71] Jileh, P. 1991. Data of the Ministry of the Environment of the Czech Republic supplied to Mr. Bouscaren (Citepa). 1991. [72] Jost, p, et al. 1992. Emission and fuel consumption modelling based on continuous measurements. Deliverable No. 7, DRIVE Project V1053. TUV Rhineland, Cologne : s.n., 1992. [73] Joumard, R, Jost, P and Hickman, J. 1995. Influence of instantaneous speed and acceleration on hot passenger car emissions and fuel consumption”. SAE Paper 950928, Society of Automotive Engineers. 1995. [74] Markel, T, et al. 2002. ADVISOR: a systems analysis tool for advanced vehicle modeling. Journal of Power Sources. 2002, 110 (2002) 255–266. [75] MEET. 1999. Methodology for calculating transport emissions and energy consumption, DG VII. 1999, p. 362. [76] MIRA. 2002. VeTESS simulation procedure. Nuneaton, Warwickshire. : MIRA, 2002. [77] Negrenti, E. 1998. The ‘corrected average speed’ approach in ENEAʹs TEE model: an innovative solution for the evaluation of the energetic and environmental impacts of urban transport policies. 1998. [78] Negrenti, Emanuele. 1996. TEE: The ENEA traffic emissions and energetics model micro‐ scale applications. Science of The Total Environment. 1996, Volumes 189–190, 28 October 1996, Pages 167–174. [79] 1999. The ‘Corrected Average Speed’ approach in ENEA’s TEE model: an innovative solution for the evaluation of the energetic and environmental impacts of urban transport policies. Science of The Total Environment. 1999, Volume 235, Issues 1–3, 1 September 1999, Pages 411–413. [80] Pattas, K. and Kyriakis, N. 1983. xhaust Gas Emission Study of Current Vehicle Fleet in Athens (Phase I). Thessaloniki, Greece : s.n., 1983. Final report to PERPA/ EEC. [81] Pattas, K., Kyriakis, N. and Samaras, Z. 1985. Exhaust Gas Emission Study of Current Vehicle Fleet in Athens (PHASE II). Thessaloniki, Greece : s.n., 1985. Vols. I,II,III. [82] Paulina, C. M. and Schwarz , J. F. 1994. Performance Evaluation of Electric Dynamometers. SAE Technical Paper, 940485, 1994. 1994. [83] Rexeis , M., et al. 2005. “Heavy‐duty vehicle emissions”. s.l. : Graz University of Technology, 2005. Final Report of WP 400 in ARTEMIS (Assessment and Reliability of Transport Emission Models and Inventory Systems), DGTREN Contract 1999‐RD.10429, report no. : I02/2005/Hb 20/2000 I680. [84] Rexeis, M. 2009. Ascertainment of Real World Emissions of Heavy Duty Vehicles. Graz University of Technology. 2009. Dissertation at the Institute for Internal Combustion
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Engines and Thermodynamics. [85] Rexeis, M., et al. 2006. Emissions and Fuel Consumption from Heavy Duty Vehicles. Graz University of Technology. 2006. COST 346, WG A: Vehicle Model ‐Final Report. [86] Rijkeboer , R. C., Van der Haagen , M. F. and Van Sloten , P. 1990. Results of Project on In‐ use Compliance Testing of Vehicles. Delft,the Netherlands : s.n., 1990. TNO report 733039000. [87] Rijkeboer, R. C., Van Sloten, P. and Schmal, P. 1989. Steekproef‐controleprogramma, onderzoek naar luchtverontreininging door voertuigen in het verkeer. Jaarrapport 1988/89. No Lucht 87, IWT‐TNO. Delft, the Netherlands : s.n., 1989. [88] Robin, S, Ntziachristos, L and Boulter, P. 2010. Validation of road vehicle and traffic emission models ‐ A review and meta‐analysis. Atmospheric Environment . 2010, 44 (2010) 2943‐2953. [89] SAE. 1991. Road Load Measurement and Dynamometer Simulation Using Coastdown Techniques. SAE Procedure No. J1263. 1991. [90] Smit, R, Dia, H and Morawska, L. 2009. Road traffic emission and fuel consumption modelling: trends, new developments and future challenges. In: Traffic Related Air Pollution and Internal Combustion Engines. Nova Publishers, U.S.A., 2009, https://www.novapublishers.com/catalog/product_info.php?products_id¼9546. [91] Smit, R, Smokers, R and Rabe, E. 2007. A new modelling approach for road traffic emissions: VERSIT+. Transportation Research Part D. 2007, 12 (2007) 414–422. [92] Smit, R, Smokers, R and Schoen, E. 2005. Development of a new emission factor model for passenger cars linking real‐world emissions to driving cycle characteristics. 2005. [93] USEPA. 2012. Development of Emission Rates for Heavy‐Duty Vehicles in the Motor Vehicle Emissions Simulator MOVES2010. Final Report, 2012. [94] VOEMLow: Emission and Energy Measurement System as Development Tool for Clean Engines, Aftertreatment Systems and Powertrains. Debal, P., Lenaers, G. and Verhaeven, V. 2002. Detroit,USA : s.n., 2002. Proc. Global Powertrain Congress 2002. pp. 24‐26. September 2002 (available on CD). [95] Weilenmann, M, Soltic, P and Atjay, D. 2003. Describing and compensating gas transport dynamics for accurate instantaneous emission measurement. Atmospheric Environment. 2003, Vol 37, pp. 5137‐5145. [96] Instantaneous emission modelling. Atmospheric Environment. 2000, 34, pp. 4629‐4638. [97] EC. 2012. Climate Action. http://ec.europa.eu/clima/policies/transport/vehicles/index_en.htm. 2012.
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12. Appendix
Network Nodes (Bus and Train Stations) ‐ Field Trial Nod Nod e / e / Name- Lat Lon EN Address Site of transport. company 39.3 22.9 KTEL- Αρ. Ζάχου, Αρ. Ζάχου, NULL 6524 3363 Volos Βόλος, 38333 Βόλος, 38333 Ktel- Γιαννιτσών Γιαννιτσών 40.6 22.9 2310595411,http://www.ktel- Thessal 236,Μενεμένη,5 236,Μενεμένη,5 5341 0502 thes.gr/ oniki 4628 4628 Τρικάλων- Τρικάλων- 39.5 21.7 Ktel- Πατουλιάς,Τρίκ Πατουλιάς,Τρίκ NULL 4109 9642 Trikala αλα,42100 αλα,42100 1ο χλμ. εθνικής 1ο χλμ. εθνικής 38.5 21.4 Ktel- οδού Αγρίνιου- οδού Αγρίνιου- http://www.ktel-aitol.gr/ 9964 1711 Agrinio Αντιρρίου,Αγρίν Αντιρρίου,Αγρίν ιο,30100 ιο,30100 Μητροπολίτου Μητροπολίτου 40.3 21.7 Ktel- Ι. Αποστολίδη Ι. Αποστολίδη 2461034454,http://www.ktelkoz 0006 9641 Kozani 1- 1- anis.gr/Home.aspx 9,Κοζάνη,50100 9,Κοζάνη,50100 Λεωφόρος Λεωφόρος 39.6 20.8 Ktel- Γεωργίου Γεωργίου NULL 7461 4719 Ioannina Παπανδρέου,Ιω Παπανδρέου,Ιω άννινα,45444 άννινα,45444 38.8 22.4 Ktel- Ταϋγέτου Ταϋγέτου http://www.ktelfthiotidos.gr, 8818 475 Lamia ,Λαμία,35100 ,Λαμία,35100 2231051345 Κύπρου Κύπρου 39.6 22.4 Ktel- 25,Λάρισα,4122 25,Λάρισα,4122 NULL 3906 1909 Larisa 2 2 Καψάλη 9- Καψάλη 9- 38.2 21.7 Ktel- 21,Πάτρα,2622 21,Πάτρα,2622 NULL 5128 3721 Patras 3 3
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Λιοσίων Λιοσίων 38.0 23.7 Ktel- 260,Αθήνα,104 260,Αθήνα,104 NULL 0939 2223 Athens 45 45 Λεωφόρος Λεωφόρος 40.7 21.4 Ktel- Μακεδονομάχω Μακεδονομάχω 23850-28350, 210-5130427 8245 0893 Florinas ν 10, Φλώρινα, ν 10, Φλώρινα, 53100 53100 Τέρμα Τέρμα Ktel- 37.0 22.1 Αρτέμιδος, Αρτέμιδος, http://www.ktelmessinias.gr, Kalamat 404 0965 Καλαμάτα,2410 Καλαμάτα,2410 2721022851 as 0 0 Ktel- Μαμέλη 1, Μαμέλη 1, 41.1 25.4 2531022912,http://www.ktelrod Komotini Κομοτηνή, Κομοτηνή, 1413 0081 opis.gr/ s 69100 69100 37.5 22.8 Ktel- Συγγρού 8, Συγγρού 8, 2752027423,http://www.ktel- 653 0043 Nafplio Ναύπλιο 21100 Ναύπλιο 21100 argolidas.gr Ήρας 28, Ήρας 28, 40.5 22.2 Ktel- Βέροια 59100, Βέροια 59100, 2331022342 2669 0551 Verias Ελλάς Ελλάς 41.0 23.5 Ktel- Μεραρχίας 44, Μεραρχίας 44, 2321022727 8658 4675 Serres Σέρρες 62125 Σέρρες 62125 Λεωφόρος Λεωφόρος 37.0 22.4 Ktel- Λυκούργου 23, Λυκούργου 23, http://ktel- 7684 3732 Sparta Σπάρτη 23100, Σπάρτη 23100, lakonias.gr,2731026441 Ελλάς Ελλάς Λαδά 2, Λαδά 2, 40.0 21.4 Ktel- Γρεβενά 51100, Γρεβενά 51100, 2462022242 8359 255 Grevena Ελλάς Ελλάς 41.1 24.1 Ktel- Βίτσι 1, Δράμα Βίτσι 1, Δράμα 2521032421 485 4492 Drama 66100, Ελλάς 66100, Ελλάς Στύρων 1, Στύρων 1, 38.4 23.6 Ktel- http://www.ktelevias.gr/, Χαλκίδα , Χαλκίδα , 6185 1217 Chalkida 2221020400 34100 34100 Χαριλάου Χαριλάου Ktel- 38.9 21.7 Τρικούπη 8, Τρικούπη 8, Karpenis 2237080013 1175 949 Καρπενήσι , Καρπενήσι , i 36100 36100 Ερυθρού Ερυθρού 37.6 21.4 Ktel- Σταυρού 6, Σταυρού 6, 2621020600 754 3046 Pyrgos Πύργος Ηλείας , Πύργος Ηλείας , 27100 27100 Διάκου Διάκου Ktel- 40.5 21.2 Αθανάσιου 14, Αθανάσιου 14, Kastoria 2467083455 2153 6019 Καστοριά , Καστοριά , s 52100 52100
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Μητροπολίτου Μητροπολίτου 40.9 24.4 Ktel- Χρυσοστόμου, Χρυσοστόμου, 2510222294 3607 0812 Kavala Καβάλα 65403, Καβάλα 65403, Ελλάς Ελλάς Λαρίσης 200, Λαρίσης 200, 39.5 21.7 Ktel- 2441021001,http://ktel- Καρδίτσα, Καρδίτσα, 5575 9516 Karditsa karditsas.blogspot.com/ 43100 43100 Εστίας 10, Εστίας 10, 38.3 23.3 Ktel- Θήβα 32200, Θήβα 32200, 2262027512 1687 2075 Thiva Ελλάς Ελλάς 38.6 24.1 ktel- Κύμη, 34003, Κύμη, 34003, 2222022257 3259 0366 Kymi Ελλάς Ελλάς Ktel- Γ. Κλαυδιανού Γ. Κλαυδιανού 37.7 20.8 Zakynth 1, Ζάκυνθος 1, Ζάκυνθος 2695042656 8594 9837 os 29100, Ελλάς 29100, Ελλάς Λ. Θεοτόκη, Λ. Θεοτόκη, 39.6 19.9 Ktel- Κέρκυρα , Κέρκυρα , 2661028925 2181 1899 Kerkyra 49100 49100 35.2 26.1 Ktel- Σητεία 72300, Σητεία 72300, NULL 078 0636 Sitia Ελλάς Ελλάς Λεωφόρος Λεωφόρος 38.9 20.7 Ktel- Ιωαννίνων, Ιωαννίνων, 2682022213 789 4517 Preveza Πρέβεζα 48100, Πρέβεζα 48100, Ελλάς Ελλάς Ktel- Λιμάνι Λιμάνι 35.3 25.1 Heraklio Ηρακλείου, Ηρακλείου, 2810346645 4334 4306 n 71110 71110 Ασημάκη Ασημάκη 38.2 22.0 Ktel- Φωτήλα 2, Αίγιο Φωτήλα 2, Αίγιο 2691062452 5043 894 Aigio 25100, Ελλάς 25100, Ελλάς Βασιλέως Βασιλέως 40.6 22.0 Ktel- Φιλίππου 3, Φιλίππου 3, 2332027685 3049 701 Naousa Νάουσα 59200, Νάουσα 59200, Ελλάς Ελλάς Εθνικής Εθνικής Ktel- 40.6 22.4 Αντιστάσεως Αντιστάσεως Alexandr 2333023312 2648 3724 20, Αλεξάνδρεια 20, Αλεξάνδρεια ia 59300, Ελλάς 59300, Ελλάς Καπετάν Καπετάν Ktel- 40.5 21.6 Φούφα 12, Φούφα 12, Ptolemai 2463020750 1408 764 Πτολεμαΐδα Πτολεμαΐδα da 50200, Ελλάς 50200, Ελλάς Κριάρη 40, Κριάρη 40, 35.5 24.0 Ktel- Χανιά 73100, Χανιά 73100, 2821027044 1334 1801 Chania Ελλάς Ελλάς
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Παύλου Μελά, Παύλου Μελά, 40.7 22.0 Ktel- Έδεσσα 58200, Έδεσσα 58200, 2381022298 9892 5027 Edesa Ελλάς Ελλάς Βόλου 22, Βόλου 22, 39.1 22.7 Ktel- Αλμυρός Αλμυρός 2422023733 8557 584 Almyros 37100, Ελλάς 37100, Ελλάς Νίκου Νίκου Ktel- 35.0 25.7 Καζαντζάκη 2, Καζαντζάκη 2, Ierapetr 2842089760 1233 419 Ιεράπετρα Ιεράπετρα a 72200, Ελλάς 72200, Ελλάς Ktel- Δεληγιάννη, Δεληγιάννη, 37.7 21.3 Amaliad Αμαλιάδα Αμαλιάδα 2622028892 9727 513 a 27200, Ελλάς 27200, Ελλάς ktel- Αδριανουπόλεω Αδριανουπόλεω 41.5 26.5 Orestiad ς, Ορεστιάδα ς, Ορεστιάδα 2552022550 0328 3027 a 68200, Ελλάς 68200, Ελλάς Λαμίας 1, Λαμίας 1, 38.9 22.6 ktel- Στυλίδα 35300, Στυλίδα 35300, 2238022111 1435 1576 Stylida Ελλάς Ελλάς Ktel- Μανασή 16, Μανασή 16, 38.3 21.8 Nafpakt Ναύπακτος Ναύπακτος 2634028351 9421 3427 os 30300, Ελλάς 30300, Ελλάς Απιδανού, Απιδανού, 39.2 22.3 Ktel- Φάρσαλα Φάρσαλα 2491022262 9775 8011 Farsala 40300, Ελλάς 40300, Ελλάς Παναγιώτη Παναγιώτη Ktel- 38.3 21.4 Σταυρόπουλου Σταυρόπουλου Mesolog 2631022217 6774 3154 7, Μεσολόγγι 7, Μεσολόγγι i 30200, Ελλάς 30200, Ελλάς Στράτου Στράτου Ktel- 38.8 21.1 Γεωργίου, Γεωργίου, Amfiloch 2642022225 6444 6914 Αμφιλοχία Αμφιλοχία ia 30500, Ελλάς 30500, Ελλάς Καποδιστρίου Καποδιστρίου 37.6 22.7 Ktel- http://www.ktel- 6, Άργος 6, Άργος 3403 2664 Argos argolidas.gr,2751067324 21200, Ελλάς 21200, Ελλάς Ναυπλίου 50, Ναυπλίου 50, 37.5 22.3 Ktel- http://www.ktelarkadias.gr,2710 Τρίπολη 22100, Τρίπολη 22100, 0774 8827 Tripoli 224314 Ελλάς Ελλάς Θεοδώρου Θεοδώρου Ktel- 37.4 22.1 Τουρλεντέ 27, Τουρλεντέ 27, Megalop 2791022238 0181 3324 Μεγαλόπολη Μεγαλόπολη oli 22200, Ελλάς 22200, Ελλάς Ktel- Πικπα 4, Πικπα 4, 40.7 22.4 Giannits Γιαννιτσά Γιαννιτσά 2382022317 9128 1282 a 58100, Ελλάς 58100, Ελλάς
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Ελευθερίου Ελευθερίου Ktel- 40.8 25.8 Βενιζέλου 36, Βενιζέλου 36, Alexandr 2551026133 4606 7529 Αλεξανδρούπολ Αλεξανδρούπολ oupoli η 68100, Ελλάς η 68100, Ελλάς Δημητράκου 3, Δημητράκου 3, 40.9 22.8 Ktel- Κιλκίς 61100, Κιλκίς 61100, 2341022311 9307 7366 Kilkis Ελλάς Ελλάς Παπακωνσταντί Παπακωνσταντί 37.8 22.6 Ktel- νου 51Α, Νεμέα νου 51Α, Νεμέα 2746022214 2061 6158 Nemea , 20500 , 20500 Ηρακλέους 2, Ηρακλέους 2, 36.7 22.5 Ktel- Γύθειο 23200, Γύθειο 23200, 2733022228 5988 6526 Gythio Ελλάς Ελλάς Ktel- Ερμού 4, Ερμού 4, 37.0 21.6 Gargalia Γαργαλιάνοι Γαργαλιάνοι 2763022223 6582 3791 noi 24400, Ελλάς 24400, Ελλάς Αγίου Αγίου 40.1 22.5 Ktel- Νικολάου, Νικολάου, 2352081271 1707 2454 Litohoro Λιτόχωρο Λιτόχωρο 60200, Ελλάς 60200, Ελλάς Στυλιανού Στυλιανού 41.1 23.2 ktel- Γονατά, Γονατά, 2325022211 8276 8136 Heraklia Ηράκλεια Ηράκλεια 62400, Ελλάς 62400, Ελλάς Ktel- Ε. Καλαντζή 14, Ε. Καλαντζή 14, 38.9 22.1 Makrako Μακρακώμη Μακρακώμη 2236022553 4021 1583 mi 35011, Ελλάς 35011, Ελλάς Λεωφόρος Λεωφόρος Κωνσταντίνου Κωνσταντίνου 38.5 22.3 Ktel- Καραμανλή 25, Καραμανλή 25, 2265028226 2673 8387 Amfisa Άμφισσα Άμφισσα 33100, Ελλάς 33100, Ελλάς Νικηφόρου Νικηφόρου Ktel- 40.3 23.4 Φωκά, Φωκά, Polygyro 2371022909 7808 3971 Πολύγυρος Πολύγυρος s 63100, Ελλάς 63100, Ελλάς Ktel- Φιλίππου, Φιλίππου, 41.2 23.3 Sidiroka Σιδηρόκαστρο Σιδηρόκαστρο 2323022221 3784 9308 stro 62300, Ελλάς 62300, Ελλάς 41.1 24.8 Ktel- Δημοκρίτου 6, Δημοκρίτου 6, NULL 3335 9325 Xanthi Ξάνθη 67100 Ξάνθη 67100 Μανεγά Μανεγά 39.1 20.9 KTEL- 1,Αρτα,47100,Ε 1,Αρτα,47100,Ε http://www.ktelartas.gr/ 6433 8722 Artas λλάς λλάς
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Χαιρώνειας, Χαιρώνειας, 38.4 22.8 Ktel- Λιβαδειά 32100, Λιβαδειά 32100, 2261028336 3123 7509 Livadia Βοιωτία Βοιωτία Λεωφόρος Λεωφόρος 38.4 22.4 Ktel-Itea Ηρώων 1, Ιτέα Ηρώων 1, Ιτέα 1 3164 237 33200, Ελλάς 33200, Ελλάς Εθνικής Εθνικής ΚΤΕΛ 26510 39.5 20.2 Αντιστάσεως, Αντιστάσεως, Ηγουμεν 25014,http://www.ktelioannina.g 0587 6253 Ηγουμενίτσα Ηγουμενίτσα ίτσας r/ 46100, Ελλάς 46100, Ελλάς Ktel- 39.7 22.2 Τύρναβος Τύρναβος http://www.ktellarisas.gr / 2410 Tyrnavo 3885 8819 40100, Ελλάς 40100, Ελλάς 567.600 s Ktel- 35.3 25.1 Ηράκλειο, Heraklion, http://www.ktelherlas.gr,2810 Heraklio 2916 3853 Κρήτη, Ελλάς Crete, Hellas 246534 n Βόνιτσα, Βόνιτσα, 38.9 20.8 Ktel- http://www.ktel-aitol.gr, 26410- Ανακτορίου Ανακτορίου 224 8979 Vonitsa 54444 30002, Ελλάς 30002, Ελλάς Αντωνίου Αντωνίου 38.8 20.7 Ktel- Τζεβελέκη, Τζεβελέκη, http://www.ktel- 279 086 Lefkada Λευκάδα Λευκάδα lefkadas.gr,26450 22364 31100, Ελλάς 31100, Ελλάς 38.4 21.3 Ktel- http://www.ktel-aitol.gr, 26410- Αιτωλικό, Ελλάς Αιτωλικό, Ελλάς 3808 5365 Aitoliko 54444 Ktel- 38.8 22.7 Ράχες, Ράχες, http://www.ktelfthiotidos.gr, Lamia- 906 8103 Φθιώτιδα 35300 Φθιώτιδα 35300 2231051345 Raxes 38.6 22.9 Ktel- Αταλάντη 352 Αταλάντη 352 http://www.ktelfthiotidos.gr, 7771 7579 Atalantis 00, Ελλάδα 00, Ελλάδα 2231051345 Ktel- 37.9 22.9 Κόρινθος - http://193.92.78.218:10022/ykte Korithno Korinthos - Port 4137 3495 Λιμάνι l_korinthias/Default.aspx,0 s Κέντρο - Κίατο, Κέντρο - Κίατο, 38.0 22.7 Ktel- http://193.92.78.218:10022/ykte Κορινθίας, Κορινθίας, 1265 5017 Kiato l_korinthias/Default.aspx,0 Ελλάς Ελλάς TRAIN Άγιος Agios 38.3 21.8 Agios Βασίλειος,Ελλά Vassileios,Gree www.trainose.gr 1453 2059 Vassilei ς ce os 39.0 22.1 TRAIN Αγγείαι,Ελλάς Aggeie,Greece www.trainose.gr 9057 9054 Aggeie
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TRAIN Agios Agios Georgios 39.3 22.7 Αγ. Γεώργιος Georgio of www.trainose.gr 853 926 Φερρών,Ελλάς s of Ferres,Greece Ferres TRAIN 37.3 21.6 Αγιαννάκης,Ελλ Agiannakis,Gre Agianna www.trainose.gr 5001 9646 άς ece kis TRAIN Άγρα Agra 40.8 21.9 Agra Μεγαπλατάνου, Megaplatanou, www.trainose.gr 0977 8073 Megapla Ελλάς Greece tanou 40.6 22.6 TRAIN Adendro,Greec Άδενδρο,Ελλάς www.trainose.gr 7432 027 Adendro e TRAIN 37.7 21.3 Αγ. Agios Agios www.trainose.gr 189 1913 Ηλίας,Ελλάς Ilias,Greece Ilias 37.9 23.7 TRAIN Αθήνα,Ελλάς Athens,Greece www.trainose.gr 9261 2084 Athens TRAIN 38.2 23.6 Αγ. Agios Agios www.trainose.gr 7739 6994 Θωμάς,Ελλάς Thomas,Greece Thomas 38.1 22.3 TRAIN Αίγειρα,Ελλάς Aigira,Greece www.trainose.gr 4782 5522 Aigira 38.2 22.0 TRAIN Αίγιο,Ελλάς Aegion,Greece www.trainose.gr 5497 9035 Aegion 40.5 22.5 TRAIN Αιγίνιο,Ελλάς Aiginio,Greece www.trainose.gr 1166 4072 Aiginio TRAIN 41.2 23.1 Ακριτοχώριον,Ε Akritochorion,G Akritoch www.trainose.gr 6238 7986 λλάς reece orion TRAIN 41.0 22.7 Ακρολίμνι,Ελλά Akrolimni,Greec Akrolimn www.trainose.gr 906 8337 ς e i TRAIN 38.4 22.9 Αλαλκομεναί,Ελ Alalkomenai,Gr Alalkom www.trainose.gr 0829 8217 λάς eece enai TRAIN 40.6 22.4 Αλεξάνδρεια,Ελ Alexandreia,Gr Alexandr www.trainose.gr 2052 4292 λάς eece eia TRAIN 40.8 25.8 Αλεξανδρούπολ Alexandroupoli, Alexandr www.trainose.gr 45 7842 η,Ελλάς Greece oupoli 38.3 23.1 TRAIN Αλίαρτος,Ελλάς Aliartos,Greece www.trainose.gr 7961 1185 Aliartos
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38.1 21.5 TRAIN Άλυσσος,Ελλάς Alyssos,Greece www.trainose.gr 4271 9548 Alyssos 37.6 21.5 TRAIN Αλφειός,Ελλάς Alfeios,Greece www.trainose.gr 5357 038 Alfeios TRAIN 37.7 21.3 Αμαλιάδα,Ελλά Amaliada,Greec Amaliad www.trainose.gr 95 4629 ς e a TRAIN 38.8 22.5 Αγ. Agia Agia www.trainose.gr 9556 8199 Μαρίνα,Ελλάς Marina,Greece Marina 41.0 22.7 TRAIN Αμόριο,Ελλάς Amorio,Greece www.trainose.gr 906 8337 Amorio TRAIN 37.8 21.3 Αμπελόκαμπος, Ampelokampos, Ampelok www.trainose.gr 2649 0693 Ελλάς Greece ampos TRAIN 40.6 21.6 Amyntaio,Greec Amyntai Αμύνταιο,Ελλάς www.trainose.gr 909 863 e o TRAIN 38.6 22.5 Αμφίκλεια,Ελλά Amfikleia,Greec Amfiklei www.trainose.gr 5818 9465 ς e a TRAIN Αγ. Agioi 38.0 23.7 Agioi Ανάργυροι,Ελλ Anargyroi,Gree www.trainose.gr 2901 1774 Anargyr άς ce oi TRAIN 37.9 21.2 Άνδραβίδα,Ελλ Andravida,Gree Andravid www.trainose.gr 0707 6729 άς ce a TRAIN Agios 38.2 21.7 Αγ. Agios Andreas,Greec www.trainose.gr 3909 2731 Ανδρέας,Ελλάς Andreas e TRAIN Ano 39.3 23.0 Ano Άνω Lekhonia,Greec www.trainose.gr 2479 5466 Lekhoni Λεχώνια,Ελλάς e a TRAIN Ano 39.3 22.0 Ano Άνω Lekhonia,Greec www.trainose.gr 4465 5196 Lekhoni Λεχώνια,Ελλάς e a TRAIN 37.5 21.5 Άνω Ano Ano www.trainose.gr 7126 7146 Σαμικό,Ελλάς Samiko,Greece Samiko 37.6 22.7 TRAIN Αργος,Ελλάς Argos,Greece www.trainose.gr 3145 3622 Argos 39.3 23.1 TRAIN Αργυραίικα,Ελλ Argireika,Greec www.trainose.gr 1411 3265 Argireika άς e
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37.0 22.0 TRAIN Άρις,Ελλάς Aris,Greece www.trainose.gr 965 0153 Aris 39.4 22.6 TRAIN Armenio,Greec Αρμένιο,Ελλάς www.trainose.gr 843 9526 Armenio e 40.7 21.8 TRAIN Άρνισσα,Ελλάς Arnissa,Greece www.trainose.gr 9805 3505 Arnissa TRAIN 37.0 22.0 Ασπρόχωμα,Ελ Asprochoma,Gr Asproch www.trainose.gr 3906 8779 λάς eece oma TRAIN Αγ. Agios 38.1 23.8 Agios Στέφανος,Ελλά Stefanos,Greec www.trainose.gr 4034 594 Stefanos ς e 38.4 23.6 TRAIN Αυλίδα,Ελλάς Avlida,Greece www.trainose.gr 0469 0359 Avlida 38.2 23.6 TRAIN Αυλώνα,Ελλάς Avlona,Greece www.trainose.gr 4998 9562 Avlona 38.1 23.8 TRAIN Αφίδναι,Ελλάς Afidnai,Greece www.trainose.gr 8769 4441 Afidnai 38.1 21.5 TRAIN Αχαΐα,Ελλάς Achaia,Greece www.trainose.gr 4596 612 Achaia TRAIN 38.0 23.7 Acharnes,Gree Acharne Αχαρνές,Ελλάς www.trainose.gr 8032 4391 ce s TRAIN 37.5 22.5 Αχλαδόκαμπος, Achladokambos Achlado www.trainose.gr 0652 7994 Ελλάς ,Greece kambos 37.1 21.9 TRAIN Βαλύρα,Ελλάς Valyra,Greece www.trainose.gr 6528 855 Valyra 38.0 21.3 TRAIN Βάρδα,Ελλάς Varda,Greece www.trainose.gr 3254 6354 Varda TRAIN 37.2 21.8 Βασιλικόν,Ελλά Vassilikon,Gree Vassiliko www.trainose.gr 6734 9489 ς ce n 38.9 22.6 TRAIN Βασιλική,Ελλάς Vassiliki,Greece www.trainose.gr 0905 0131 Vassiliki TRAIN 39.3 22.7 Βελεστίνο,Ελλά Velestino,Greec Velestin www.trainose.gr 8181 4983 ς e o 41.0 22.7 TRAIN Βέννα,Ελλάς Venna,Greece www.trainose.gr 906 8337 Venna 40.5 22.2 TRAIN Βέροια,Ελλάς Veroia,Greece www.trainose.gr 3939 1359 Veroia 40.7 21.5 TRAIN Βεύη,Ελλάς Vevi,Greece www.trainose.gr 741 6734 Vevi 39.3 22.9 TRAIN Βόλος,Ελλάς Volos,Greece www.trainose.gr 6469 3644 Volos
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TRAIN 38.1 21.6 Βραχνέικα,Ελλά Vrachneika,Gre Vrachnei www.trainose.gr 6423 6851 ς ece ka 41.2 23.2 TRAIN Βυρώνεια,Ελλά Vyronia,Greece www.trainose.gr 6223 5351 Vyronia ς 41.0 23.7 TRAIN Gazoros,Greec Γάζωρος,Ελλάς www.trainose.gr 1669 6592 Gazoros e 40.8 22.8 TRAIN Γαλλικός,Ελλάς Gallikos,Greece www.trainose.gr 602 8621 Gallikos TRAIN 37.8 21.2 Γαστούνη,Ελλά Gastouni,Greec Gastoun www.trainose.gr 5361 6097 ς e i 39.3 23.0 TRAIN Γατζέα,Ελλάς Gatzea,Greece www.trainose.gr 2193 9468 Gatzea TRAIN 37.3 21.6 Γιαννιτσοχώρι,Ε Giannitsochori, Giannits www.trainose.gr 919 8929 λλάς Greece ochori TRAIN 37.2 21.7 Γλυκορίζιον,Ελλ Glykorizion,Gre Glykorizi www.trainose.gr 8522 6805 άς ece on TRAIN General Γεν. General 37.0 22.0 Hospital Νοσοκομείο Hospital of www.trainose.gr 6852 5509 of Καλαμάτας,Ελλ Kalamata,Gree Kalamat άς ce a TRAIN 38.8 22.3 Γοργοπόταμος, Gorgopotamos, Gorgopo www.trainose.gr 3428 9184 Ελλάς Greece tamos TRAIN 38.2 21.7 Διγενή Digeni Digeni www.trainose.gr 827 6552 Ακρίτα,Ελλάς Akrita,Greece Akrita 38.5 22.8 TRAIN Δαυλεία,Ελλάς Davleia,Greece www.trainose.gr 3444 1229 Davleia 38.0 23.7 TRAIN Δεκέλεια,Ελλάς Dekelia,Greece www.trainose.gr 9924 7903 Dekelia TRAIN 37.7 21.3 Douneika,Gree Douneik Δουνέικα,Ελλάς www.trainose.gr 4484 1887 ce a 38.1 22.4 TRAIN Δερβένι,Ελλάς Derveni,Greece www.trainose.gr 3025 106 Derveni TRAIN 37.2 21.9 Διαβολίτσι,Ελλά Diavolitsi,Greec Diavolits www.trainose.gr 9814 6718 ς e i 38.1 22.1 TRAIN Diakofto,Greec Διακοφτό,Ελλάς www.trainose.gr 9175 9798 Diakofto e
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TRAIN 41.3 26.5 Διδυμότειχο,Ελ Didymoteichon, Didymot www.trainose.gr 5291 1297 λάς Greece eichon 41.0 22.7 TRAIN Δίλοφος,Ελλάς Dilofos,Greece www.trainose.gr 906 8337 Dilofos 41.7 26.3 TRAIN Δίκαια,Ελλάς Dikaia,Greece www.trainose.gr 0665 0255 Dikaia 41.1 22.7 TRAIN Δοϊράνη,Ελλάς Doirani,Greece www.trainose.gr 7277 7207 Doirani TRAIN 39.1 22.2 Domokos,Gree Domoko Δομοκός,Ελλάς www.trainose.gr 8041 8598 ce s 39.4 22.2 TRAIN Doxaras,Greec Δοξαράς,Ελλάς www.trainose.gr 5803 7362 Doxaras e 41.1 24.1 TRAIN Δράμα,Ελλάς Drama,Greece www.trainose.gr 4035 4701 Drama 37.2 21.8 TRAIN Δώριον,Ελλάς Dorion,Greece www.trainose.gr 8743 5562 Dorion 40.8 22.0 TRAIN Εδεσσα,Ελλάς Edessa,Greece www.trainose.gr 0896 5124 Edessa 41.1 22.5 TRAIN Ειδομένη,Ελλάς Idomeni,Greece www.trainose.gr 2075 1837 Idomeni TRAIN 37.4 22.5 Ελαιοχώρι,Ελλά Elaiochori,Gree Elaiocho www.trainose.gr 5829 6814 ς ce ri 38.3 23.4 TRAIN Ελαιώνας,Ελλά Elaionas,Greec www.trainose.gr 7485 3915 Elaionas ς e 37.3 21.6 TRAIN Ελιά,Ελλάς Elia,Greece www.trainose.gr 7487 9025 Elia TRAIN Episkopi of 40.6 22.1 Episkopi Επισκοπή Naoussa,Greec www.trainose.gr 8636 4088 of Νάουσας,Ελλάς e Naoussa 37.6 21.4 TRAIN Επιτάλιο,Ελλάς Epitalio,Greece www.trainose.gr 2504 9291 Epitalio TRAIN 39.8 22.5 Evangeli Ευαγγελισμός Evangelismos www.trainose.gr 2914 1558 smos Τεμπών,Ελλάς (Tembi),Greece (Tembi) 37.4 21.6 TRAIN Zacharo,Greec Ζαχάρω,Ελλάς www.trainose.gr 7873 365 Zacharo e TRAIN 37.2 21.9 Ζευγολάτειο,Ελ Zevgolateio,Gre Zevgolat www.trainose.gr 5124 6523 λάς ece eio
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TRAIN 39.1 22.2 Θαυμακός,Ελλά Thavmakos,Gre Thavma www.trainose.gr 5113 8426 ς ece kos 39.1 22.1 TRAIN Thermes,Greec Θέρμες,Ελλάς www.trainose.gr 2333 629 Thermes e TRAIN 40.6 22.9 Θεσσαλονίκη,Ε Thessaloniki,Gr Thessal www.trainose.gr 4441 2978 λλάς eece oniki 38.3 23.3 TRAIN Θήβα,Ελλάς Thiva,Greece www.trainose.gr 2974 1872 Thiva 37.0 22.0 TRAIN Θουρία,Ελλάς Thouria,Greece www.trainose.gr 7863 4699 Thouria 41.0 22.7 TRAIN Thourion,Greec Θούριο,Ελλάς www.trainose.gr 906 8337 Thourion e 41.1 25.1 TRAIN Ίασμος,Ελλάς Iasmos,Greece www.trainose.gr 2593 862 Iasmos TRAIN 37.8 21.2 Καβασίλας,Ελλ Kavasilas,Gree Kavasila www.trainose.gr 7557 6668 άς ce s 41.0 22.7 TRAIN Καβύλλη,Ελλάς Kavylli,Greece www.trainose.gr 906 8337 Kavylli 37.5 21.5 TRAIN Καϊάφας,Ελλάς Kaiafas,Greece www.trainose.gr 1199 9626 Kaiafas TRAIN 37.4 21.6 Κακόβατος,Ελλ Kakovatos,Gree Kakovat www.trainose.gr 5678 4709 άς ce os TRAIN 39.7 21.6 Καλαμπάκα,Ελλ Kalambaka,Gre Kalamba www.trainose.gr 0306 2511 άς ece ka TRAIN 38.0 22.1 Καλάβρυτα,Ελλ Kalavryta,Greec Kalavryt www.trainose.gr 335 0999 άς e a 41.0 22.7 TRAIN Καλίνδοια,Ελλά Kallindia,Greec www.trainose.gr 906 8337 Kallindia ς e TRAIN 37.2 21.6 Καλό Kalo Kalo www.trainose.gr 9746 99 Νερό,Ελλάς Nero,Greece Nero 38.3 23.5 TRAIN Kalochori Pantichi Καλοχώρι 8781 9408 Παντείχι 37.2 21.9 TRAIN Καλλιρόη,Ελλάς Kalliroi,Greece www.trainose.gr 5589 3415 Kalliroi TRAIN 37.0 22.1 Καλαμάτα,Ελλά Kalamata,Gree Kalamat www.trainose.gr 3747 0904 ς ce a
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TRAIN 41.0 22.7 Καλοχώρι Kalochori Kalochor www.trainose.gr 906 8337 Σερρών,Ελλάς Serron,Greece i Serron 39.3 21.9 TRAIN Karditsa,Greec Καρδίτσα,Ελλάς www.trainose.gr 5403 1497 Karditsa e TRAIN 37.7 21.3 Kardam Καρδαμά (Άγιος Kardama (Agios www.trainose.gr 6906 3228 a (Agios Ηλίας),Ελλάς Elias),Greece Elias) TRAIN 38.2 21.7 Castellokampou Castello Patras,Ελλάς www.trainose.gr 8991 4241 ,Greece kampou TRAIN 40.8 22.6 Καστανάς,Ελλά Kastanas,Greec Kastana www.trainose.gr 2735 5711 ς e s TRAIN 41.2 22.8 Καστανούσσα, Kastanoussa,Gr Kastano www.trainose.gr 7609 9591 Ελλάς eece ussa TRAIN 41.2 22.8 Kastano Καστανούσσα Kastanoussa www.trainose.gr 7665 957 ussa Σερρών,Ελλάς Serron,Greece Serron TRAIN 41.0 22.7 Καστανέαι,Ελλά Kastanee,Gree Kastane www.trainose.gr 906 8337 ς ce e 37.6 21.3 TRAIN Κατάκωλο,Ελλά Katakolo,Greec www.trainose.gr 5359 1628 Katakolo ς e 40.2 22.5 TRAIN Κατερίνη,Ελλάς Katerini,Greece www.trainose.gr 69 3105 Katerini 38.0 22.1 TRAIN Κερπίνη,Ελλάς Kerpini,Greece www.trainose.gr 2901 0634 Kerpini TRAIN Kefalochori 40.5 22.3 Kefaloch Κεφαλοχώρι Imathias,Greec www.trainose.gr 7879 6481 ori Ημαθίας,Ελλάς e Imathias 38.0 23.7 TRAIN Κηφισσός,Ελλά Kifissos,Greece www.trainose.gr 1461 1871 Kifissos ς 40.9 22.8 TRAIN Κιλκίς,Ελλάς Kilkis,Greece www.trainose.gr 5835 571 Kilkis 40.9 25.7 TRAIN Κίρκη,Ελλάς Kirki,Greece www.trainose.gr 7557 9768 Kirki 40.7 21.6 TRAIN Κλειδί,Ελλάς Kleidi,Greece www.trainose.gr 433 33 Kleidi 40.2 21.7 TRAIN Κοζάνη,Ελλάς Kozani,Greece www.trainose.gr 9465 9264 Kozani
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TRAIN 40.5 22.3 Κουλούρα,Ελλά Kouloura,Greec Koulour www.trainose.gr 4899 1413 ς e a TRAIN 40.4 21.7 Komanos,Gree Komano Κόμανος,Ελλάς www.trainose.gr 6254 5465 ce s 41.1 25.3 TRAIN Κομοτηνή,Ελλά Komotini,Greec www.trainose.gr 1004 94 Komotini ς e TRAIN 37.2 21.8 Κοπανάκι,Ελλά Kopanaki,Greec Kopanak www.trainose.gr 8905 1831 ς e i TRAIN 41.1 26.3 Κορνοφωλιά,Ελ Kornofolia,Gree Kornofoli www.trainose.gr 5769 0128 λάς ce a 40.3 22.5 TRAIN Κορινός,Ελλάς Korinos,Greece www.trainose.gr 1615 7754 Korinos 41.3 23.3 TRAIN Κουλάτα,Ελλάς Koulata,Greece www.trainose.gr 9187 6172 Koulata TRAIN 37.9 21.3 Κουρτέσιο,Ελλά Kourtesion,Gre Kourtesi www.trainose.gr 7907 177 ς ece on 38.1 22.3 TRAIN Akrata Krathion Ακράτα 5385 4545 Κράθιο TRAIN 37.5 21.5 Κρέστενα,Ελλά Krestena,Greec Kresten www.trainose.gr 9634 4224 ς e a TRAIN 37.2 21.6 Κυπαρίσσια,Ελ Kyparissia,Gree Kypariss www.trainose.gr 5227 7014 λάς ce ia 39.1 22.1 TRAIN Κυφαιρά,Ελλάς Kyfaira,Greece www.trainose.gr 4056 9625 Kyfaira TRAIN Κυψέλη Kypseli 39.5 22.6 Kypseli Λάρισσας,Ελλά (@Larissa),Gre www.trainose.gr 2265 5334 (@Laris ς ece sa) 41.0 22.7 TRAIN Λάβαρα,Ελλάς Lavara,Greece www.trainose.gr 906 8337 Lavara 41.0 22.7 TRAIN Λαγυνά,Ελλάς Lagina,Greece www.trainose.gr 906 8337 Lagina 38.8 22.4 TRAIN Λαμία,Ελλάς Lamia,Greece www.trainose.gr 9603 3492 Lamia 38.0 21.4 TRAIN Λάππα,Ελλάς Lappa,Greece www.trainose.gr 9879 182 Lappa 39.6 22.4 TRAIN Λάρισα,Ελλάς Larissa,Greece www.trainose.gr 2951 2272 Larissa
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37.6 21.3 TRAIN Lasteika,Greec Λαστέικα,Ελλάς www.trainose.gr 9009 9815 Lasteika e TRAIN Latomei Λατομείο Latomeio 39.3 22.8 o (@Μελισσιάτικα (@Melissiatika), www.trainose.gr 8422 4736 (@Melis ),Ελλάς Greece siatika) 38.4 22.9 TRAIN Λειβαδειά,Ελλά Levadia,Greece www.trainose.gr 7057 2664 Levadia ς TRAIN 38.8 22.3 Λειανοκλάδι,Ελ Lianokladi,Gree Lianokla www.trainose.gr 9096 7211 λάς ce di TRAIN 40.0 22.5 Λεπτοκαρυά,Ελ Leptokarya,Gre Leptokar www.trainose.gr 5863 6555 λάς ece ya TRAIN Λευκοθέα Lefkothea 41.0 23.9 Lefkothe Αλιστράτης,Ελλ Alistratis,Greec www.trainose.gr 124 3401 a άς e Alistratis TRAIN 37.9 21.2 Lechaina,Greec Lechain Λεχαινά,Ελλάς www.trainose.gr 3849 674 e a TRAIN 40.6 22.5 Λιανοβέργιον,Ε Lianovergion,Gr Lianover www.trainose.gr 3018 0436 λλάς eece gion TRAIN 41.2 23.0 Λεβαδειά Livadia Livadia www.trainose.gr 5495 7103 Κερκίνης,Ελλάς Kerkinis,Greece Kerkinis 41.1 24.6 TRAIN Λιβέρα,Ελλάς Livera,Greece www.trainose.gr 2409 9182 Livera 38.6 22.5 TRAIN Λιλαία,Ελλάς Lilaia,Greece www.trainose.gr 6991 4427 Lilaia TRAIN 40.1 22.5 Λιτόχωρο,Ελλά Litochoro,Greec Litochor www.trainose.gr 2531 4991 ς e o 40.5 22.3 TRAIN Λουτρός,Ελλάς Loutros,Greece www.trainose.gr 9798 9769 Loutros TRAIN Μαγούλα Magoula 37.2 21.9 Magoula Μεσσήνης,Ελλά Messinis,Greec www.trainose.gr 4079 6407 Messinis ς e TRAIN Μαγούλα Magoula 39.4 21.7 Magoula Καρδίτσης,Ελλά Karditsis,Greec www.trainose.gr 6237 8838 Karditsis ς e TRAIN 41.2 23.1 Μανδράκι,Ελλά Mandraki,Greec Mandrak www.trainose.gr 6087 4034 ς e i
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41.0 22.7 TRAIN Μάνδρα,Ελλάς Mandra,Greece www.trainose.gr 906 8337 Mandra 41.0 22.7 TRAIN Μαράσια,Ελλάς Marasia,Greece www.trainose.gr 906 8337 Marasia TRAIN 38.8 22.4 Μεγάλη Megali Megali www.trainose.gr 9389 7824 Βρύση,Ελλάς Vrysi,Greece Vrysi 40.4 22.5 TRAIN Μεθώνη,Ελλάς Methoni,Greece www.trainose.gr 6001 8047 Methoni TRAIN 37.2 21.9 Μελιγαλάς,Ελλά Meligalas,Gree Meligala www.trainose.gr 216 7619 ς ce s TRAIN Μελιά Melia of 39.5 22.5 Melia of Πλατυκάμπου,Ε Platykambos,Gr www.trainose.gr 6312 9055 Platyka λλάς eece mbos TRAIN 39.3 22.9 Μελισσιάτικα,Ελ Melissiatika,Gre Melissiat www.trainose.gr 8672 1216 λάς ece ika TRAIN 40.5 22.2 Mesi Mesi Μέση,Ελλάς www.trainose.gr 2831 6722 Veroias,Greece Veroias TRAIN 40.7 21.4 Μεσονήσιον,Ελ Mesonision,Gre Mesonisi www.trainose.gr 9737 7329 λάς ece on TRAIN 38.0 22.1 Μεγάλο Megalo Megalo www.trainose.gr 9391 6491 Σπήλαιο,Ελλάς Spilaio,Greece Spilaio 37.0 22.0 TRAIN Μεσσήνη,Ελλάς Messini,Greece www.trainose.gr 5762 6363 Messini 40.9 25.6 TRAIN Μεστή,Ελλάς Mesti,Greece www.trainose.gr 6692 3949 Mesti TRAIN 41.0 22.7 Μεταλλικός,Ελλ Metallikos,Gree Metalliko www.trainose.gr 906 8337 άς ce s 39.3 23.1 TRAIN Μηλέαι,Ελλάς Milee,Greece www.trainose.gr 2675 4467 Milee TRAIN 37.7 21.3 Μιραμάρε,Ελλά Miramare,Gree Miramar www.trainose.gr 0818 2623 ς ce e 41.2 22.8 TRAIN Μουριές,Ελλάς Mouries,Greece www.trainose.gr 6132 3989 Mouries TRAIN 42.0 23.0 Μπλαγκόεβγκρ Blagoevgrad,Gr Blagoev www.trainose.gr 1303 8562 αντ,Ελλάς eece grad
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38.7 22.4 TRAIN Μπράλος,Ελλά Bralos,Greece www.trainose.gr 0214 5771 Bralos ς 37.7 22.7 TRAIN Μυκήνες,Ελλάς Mykinai,Greece www.trainose.gr 2216 2842 Mykinai TRAIN Μύλοι 37.5 22.7 Myloi of Myloi of Ναυπλίου,Ελλά www.trainose.gr 5692 1639 Nafplio,Greece Nafplio ς 37.6 21.3 TRAIN Μυρτιά,Ελλάς Myrtia,Greece www.trainose.gr 9963 3749 Myrtia 40.6 22.1 TRAIN Naoussa,Greec Νάουσα,Ελλάς www.trainose.gr 213 3397 Naoussa e 37.5 22.8 TRAIN Ναύπλιο,Ελλάς Nafplio,Greece www.trainose.gr 6846 0172 Nafplio 41.0 22.7 TRAIN Β. V. www.trainose.gr 906 8337 V. Vissa Βύσσα,Ελλάς Vissa,Greece 37.7 22.7 TRAIN Νεμέα,Ελλάς Nemea,Greece www.trainose.gr 8188 1813 Nemea TRAIN Νέα Nea 38.0 21.3 Nea Μανωλάδα,Ελλ Manolada,Gree www.trainose.gr 5392 8127 Manolad άς ce a TRAIN 41.2 24.6 Νεοχώριο,Ελλά Neochorion,Gre Neochor www.trainose.gr 2056 3392 ς ece ion TRAIN 41.2 23.2 Νέο Neo Neo www.trainose.gr 6981 9706 Πετρίτσι,Ελλάς Petritsi,Greece Petritsi TRAIN 37.4 21.6 Νεοχώρι Neochori Neochor www.trainose.gr 3492 5941 Φιγαλίας,Ελλάς Figalias,Greece i Figalias TRAIN 38.1 22.1 Μικροχελιδού,Ε Microchelidou, Microch www.trainose.gr 5468 7656 λλάς Greece elidou 41.1 24.3 TRAIN Νικηφόρος,Ελλ Nikiforos,Greec www.trainose.gr 6828 1148 Nikiforos άς e TRAIN 39.9 22.6 Νέοι Neoi Neoi www.trainose.gr 761 3864 Πόροι,Ελλάς Poroi,Greece Poroi TRAIN Nea Nea Νέα 40.7 22.8 Philadel Philadelphia Φιλαδέλφεια www.trainose.gr 9662 4883 phia Toumbas,Greec Τούμπας,Ελλάς Toumba e s 41.1 24.8 TRAIN Ξάνθη,Ελλάς Xanthi,Greece www.trainose.gr 2399 9277 Xanthi
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TRAIN 40.5 22.3 Xechas Ξεχασμένη Xechasmeni of www.trainose.gr 6301 3765 meni of Ημαθίας,Ελλάς Imathia,Greece Imathia TRAIN 38.0 22.6 Ξυλόκαστρο,Ελ Xylokastro,Gree Xylokast www.trainose.gr 7438 319 λάς ce ro 39.1 22.2 TRAIN Ξυνιάδα,Ελλάς Xyniada,Greece www.trainose.gr 4321 1798 Xyniada TRAIN 40.6 21.6 Ξινό Xino Xino www.trainose.gr 9137 3377 Νερό,Ελλάς Nero,Greece Nero 38.3 23.6 TRAIN Οινόη,Ελλάς Inoi,Greece www.trainose.gr 2206 0966 Inoi 38.3 23.6 TRAIN Οινόφυτα,Ελλά Oinofyta,Greec www.trainose.gr 0686 3385 Oinofyta ς e 37.6 21.6 TRAIN Olympia,Greec Ολυμπία,Ελλάς www.trainose.gr 4556 2636 Olympia e TRAIN 41.5 26.5 Ορεστιάδα,Ελλ Orestiada,Gree Orestiad www.trainose.gr 0311 3718 άς ce a 39.4 22.2 TRAIN Ορφανά,Ελλάς Orfana,Greece www.trainose.gr 0384 1487 Orfana TRAIN Athens Athens 37.9 23.9 Αεροδρόμιο Internati International www.trainose.gr 3692 448 Σπάτων,Ελλάς onal Airport,Greece Airport TRAIN Αγ. Agioi 37.9 23.1 Agioi Θεόδωροι,Ελλά Theodoroi,Gree www.trainose.gr 3306 3694 Theodor ς ce oi TRAIN Renti Renti (Agiou 37.9 23.6 (Agiou Ρέντη,Ελλάς Ioanni www.trainose.gr 6228 6819 Ioanni Renti),Greece Renti) TRAIN 39.3 22.2 Παλαιοφάρσαλ Palaeofarsalos, Palaeofa www.trainose.gr 1275 4366 ος,Ελλάς Greece rsalos TRAIN Agioi Αγ. Ανάργυροι Agioi Anargyroi 38.0 23.7 Anargyr (Προαστιακός), (Suburban),Gre www.trainose.gr 2071 1848 oi Ελλάς ece (Suburb an)
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TRAIN 38.0 23.7 Άνω Ano Ano www.trainose.gr 7071 1016 Λιόσια,Ελλάς Liosia,Greece Liosia TRAIN 38.1 21.7 Παναχαϊκή,Ελλ Panachaiki,Gre Panacha www.trainose.gr 3015 4241 άς ece iki TRAIN 41.2 24.5 Παρανέστι,Ελλά Paranesti,Gree Paranes www.trainose.gr 6563 017 ς ce ti 37.4 22.5 TRAIN Partheni,Greec Παρθένι,Ελλάς www.trainose.gr 7509 1618 Partheni e 38.5 22.7 TRAIN Παρόρι,Ελλάς Parori,Greece www.trainose.gr 7387 6197 Parori TRAIN 38.0 23.6 Ασπρόπυργος, Aspropyrgos,Gr Aspropy www.trainose.gr 8083 0417 Ελλάς eece rgos 38.2 21.7 TRAIN Πάτρα,Ελλάς Patra,Greece www.trainose.gr 4963 3486 Patra TRAIN Doukissi Δουκίσσης Doukissis 38.0 23.8 s Πλακεντίας,Ελλ Plakentias,Gree www.trainose.gr 2474 3389 Plakenti άς ce as 40.9 22.8 TRAIN Πεδινό,Ελλάς Pedino,Greece www.trainose.gr 039 8184 Pedino 37.9 23.6 TRAIN Πειραιάς,Ελλάς Piraeus,Greece www.trainose.gr 4916 4265 Piraeus 41.0 22.7 TRAIN Πέπλος,Ελλάς Peplos,Greece www.trainose.gr 906 8337 Peplos 40.7 22.1 TRAIN Πετραιά,Ελλάς Petraia,Greece www.trainose.gr 2571 4232 Petraia TRAIN 41.0 22.7 Πετράδες,Ελλά Petrades,Greec Petrade www.trainose.gr 906 8337 ς e s 38.0 23.7 TRAIN Ηράκλειο,Ελλάς Iraklio,Greece www.trainose.gr 5718 7136 Iraklio TRAIN 39.3 23.1 Πινακάτες,Ελλά Pinakates,Gree Pinakate www.trainose.gr 3552 1362 ς ce s 38.0 23.8 TRAIN Kifissias,Greec Κηφισίας,Ελλάς www.trainose.gr 4213 035 Kifissias e 38.0 22.7 TRAIN Κιάτο,Ελλάς Kiato,Greece www.trainose.gr 1222 3583 Kiato 37.9 23.2 TRAIN Κινέττα,Ελλάς Kinetta,Greece www.trainose.gr 6556 0139 Kinetta
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TRAIN 37.9 22.9 Korinthos,Gree Korintho Κόρινθος,Ελλάς www.trainose.gr 2083 3222 ce s 37.9 23.8 TRAIN Κορωπί,Ελλάς Koropi,Greece www.trainose.gr 1297 9582 Koropi 41.2 24.4 TRAIN Πλατάνια,Ελλάς Platania,Greece www.trainose.gr 0446 216 Platania TRAIN Πλατύ / Platy / 37.1 22.0 Platy / Ανεμόμυλος,Ελ Anemomylos,Gr www.trainose.gr 319 0177 Anemo λάς eece mylos 37.9 23.6 TRAIN Λεύκα,Ελλάς Lefka,Greece www.trainose.gr 5389 5222 Lefka TRAIN 38.1 22.2 Πλάτανος Platanos Platanos www.trainose.gr 7035 7002 Ακράτας,Ελλάς Akratas,Greece Akratas 40.6 22.5 TRAIN Πλατύ,Ελλάς Platy,Greece www.trainose.gr 3727 3077 Platy TRAIN Magoula 38.0 23.5 Magoula Μαγούλα (Elefsina),Gree www.trainose.gr 7333 2917 (Elefsina Ελευσίνα,Ελλάς ce ) 37.9 23.3 TRAIN Μέγαρα,Ελλάς Megara,Greece www.trainose.gr 9245 619 Megara TRAIN 38.0 23.7 Μεταμόρφωση, Metamorfosi,Gr Metamor www.trainose.gr 6014 5641 Ελλάς eece fosi TRAIN Nea 38.0 23.4 Nea Νέα Peramos,Greec www.trainose.gr 1333 1472 Peramo Πέραμος,Ελλάς e s TRAIN 38.0 23.7 Νεραντζιώτισσα Nerantziotissa, Nerantzi www.trainose.gr 4516 9301 ,Ελλάς Greece otissa TRAIN 41.0 22.7 Πολύανθος,Ελλ Polyanthos,Gre Polyanth www.trainose.gr 906 8337 άς ece os TRAIN 40.9 22.5 Πολύκαστρο,Ελ Polykastro,Gree Polykast www.trainose.gr 9109 7504 λάς ce ro TRAIN 41.1 25.0 Πολύσιτος,Ελλά Polysitos,Greec Polysito www.trainose.gr 1158 2072 ς e s 37.9 23.8 TRAIN Paiania Kantza Παιανία 8404 6995 Κάντζα
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38.0 23.8 TRAIN Παλλήνη,Ελλάς Pallini,Greece www.trainose.gr 055 6969 Pallini 38.0 23.8 TRAIN Πεντέλη,Ελλάς Penteli,Greece www.trainose.gr 3344 2203 Penteli 41.0 22.7 TRAIN Πραγγί,Ελλάς Praggi,Greece www.trainose.gr 906 8337 Praggi TRAIN 41.3 23.3 Προμαχώνας,Ε Promachonas, Promac www.trainose.gr 63 5571 λλάς Greece honas 37.9 23.7 TRAIN Ρουφ,Ελλάς Rouf,Greece www.trainose.gr 7443 0423 Rouf TRAIN 40.5 21.6 Πτολεμαΐδα,Ελλ Ptolemaida,Gre Ptolemai www.trainose.gr 1542 9385 άς ece da TRAIN Pythion Πύθιο Pythion of 41.3 26.6 of Διδυμοτείχου,Ε Didymoteicho,G www.trainose.gr 7034 2176 Didymot λλάς reece eicho 41.0 22.7 TRAIN Πύθιον,Ελλάς Pythion,Greece www.trainose.gr 906 8337 Pythion TRAIN 37.6 21.4 Πύργος Pyrgos Pyrgos www.trainose.gr 7601 3544 Ηλείας,Ελλάς Ilias,Greece Ilias 39.9 22.6 TRAIN Rapsani,Greec Ραψάνη,Ελλάς www.trainose.gr 0029 1604 Rapsani e 41.0 22.7 TRAIN Ρήγιον,Ελλάς Rigion,Greece www.trainose.gr 906 8337 Rigion TRAIN 39.4 22.7 Rizomyl Ριζόμυλο Rizomylos www.trainose.gr 1627 2694 os Φερρών,Ελλάς (Ferres),Greece (Ferres) 38.3 21.7 TRAIN Ρίο,Ελλάς Rio,Greece www.trainose.gr 0071 8177 Rio TRAIN 41.2 22.9 Ροδόπολις,Ελλ Rodopolis,Gree Rodopoli www.trainose.gr 59 9883 άς ce s 38.8 22.4 TRAIN Ροδίτσα,Ελλάς Roditsa,Greece www.trainose.gr 9512 5739 Roditsa 37.9 23.7 TRAIN Ρουφ,Ελλάς Rouf,Greece www.trainose.gr 7443 0423 Rouf 38.1 21.4 TRAIN Σαγέικα,Ελλάς Sageika,Greece www.trainose.gr 1876 6735 Sageika 41.0 22.7 TRAIN Σάκκος,Ελλάς Sakkos,Greece www.trainose.gr 906 8337 Sakkos
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37.5 21.5 TRAIN Σαμικό,Ελλάς Samiko,Greece www.trainose.gr 4843 9238 Samiko TRAIN 41.5 23.2 Σαντάνσκι,Ελλά Sandanski,Gree Sandans www.trainose.gr 3767 5042 ς ce ki 41.0 23.5 TRAIN Σέρρες,Ελλάς Serres,Greece www.trainose.gr 7381 3619 Serres TRAIN 41.2 23.3 Σιδηρόκαστρο, Sidirokastro,Gr Sidiroka www.trainose.gr 2794 7622 Ελλάς eece stro TRAIN Sidiroka Σιδηρόκαστρο Sidirokastro of 37.2 21.7 stro of Πελοποννήσου, Peloponnisos,G www.trainose.gr 8866 2521 Pelopon Ελλάς reece nisos 40.6 22.8 TRAIN Σίνδος,Ελλάς Sindos,Greece www.trainose.gr 7418 0549 Sindos 40.7 21.5 TRAIN Σιταριά,Ελλάς Sitaria,Greece www.trainose.gr 7875 3856 Sitaria 37.1 21.9 TRAIN Σκάλα,Ελλάς Skala,Greece www.trainose.gr 982 9423 Skala TRAIN SKA SKA (Rail 38.0 23.7 (Rail center of ΣΚΑ,Ελλάς www.trainose.gr 6835 3753 center of Acharnes),Gree Acharne ce s) TRAIN 41.1 23.3 Σκοτούσσα,Ελλ Skotoussa,Gree Skotous www.trainose.gr 3 762 άς ce sa TRAIN 37.6 21.3 Σκουροχώρι,Ελ Skourochori,Gr Skouroc www.trainose.gr 9205 5726 λάς eece hori 40.7 22.1 TRAIN Σκύδρα,Ελλάς Skydra,Greece www.trainose.gr 6808 576 Skydra 41.1 26.3 TRAIN Σουφλι,Ελλάς Soufli,Greece www.trainose.gr 8762 0166 Soufli 39.3 22.0 TRAIN Sofades,Greec Σοφάδες,Ελλάς www.trainose.gr 3952 9204 Sofades e 42.7 23.3 TRAIN Σόφια,Ελλάς Sofija,Greece www.trainose.gr 1287 2075 Sofija TRAIN 41.1 24.7 Stavrou Σταυρούπολη Stavroupoli of www.trainose.gr 9395 0301 poli of Ξάνθης,Ελλάς Xanthi,Greece Xanthi
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38.4 23.5 TRAIN Στενό,Ελλάς Steno,Greece www.trainose.gr 1865 9056 Steno TRAIN 39.4 22.7 Στεφανοβίκειο, Stefanovikeio,G Stefanov www.trainose.gr 5502 1131 Ελλάς reece ikeio TRAIN 41.0 23.5 Στρυμώνας,Ελλ Strymonas,Gre Strymon www.trainose.gr 14 93 άς ece as 38.9 22.6 TRAIN Στυλίδα,Ελλάς Stylida,Greece www.trainose.gr 1322 1469 Stylida 38.9 22.3 TRAIN Στυρφακά,Ελλά Stirfaka,Greece www.trainose.gr 6375 0115 Stirfaka ς TRAIN 40.9 25.7 Συκορράχη,Ελλ Sykorrachi,Gre Sykorrac www.trainose.gr 7438 2028 άς ece hi 38.3 23.6 TRAIN Dilesi (2nd km Chalkida) Δήλεσι (2ο χλμ Αθήνα 5525 0679 Χαλκίδα) in Ath 38.2 23.7 TRAIN Σφενδάλη,Ελλά Sfendali,Greece www.trainose.gr 3551 8458 Sfendali ς 41.0 22.7 TRAIN Σοφικό,Ελλάς Sofiko,Greece www.trainose.gr 906 8337 Sofiko 38.3 23.5 TRAIN Tanagra,Greec Τανάγρα,Ελλάς www.trainose.gr 4002 814 Tanagra e TRAIN ΤΕΙ TEI of 37.0 22.0 TEI of Καλαμάτας,Ελλ Kalamata,Gree www.trainose.gr 5743 6396 Kalamat άς ce a 38.6 22.7 TRAIN Tithorea,Greec Τιθορέα,Ελλάς www.trainose.gr 0855 1878 Tithorea e 41.0 24.7 TRAIN Τοξότες,Ελλάς Toxotes,Greece www.trainose.gr 8553 8112 Toxotes 39.5 21.7 TRAIN Τρίκαλα,Ελλάς Trikala,Greece www.trainose.gr 4615 6336 Trikala 37.5 22.3 TRAIN Τριπολη,Ελλάς Tripoli,Greece www.trainose.gr 0532 7881 Tripoli 38.1 22.1 TRAIN Τρικλιά,Ελλάς Triklia,Greece www.trainose.gr 3015 6714 Triklia 41.0 26.2 TRAIN Τυχερό,Ελλάς Tychero,Greece www.trainose.gr 3573 92 Tychero TRAIN 38.3 23.0 Υψηλάντης,Ελλ Ypsilantis,Gree Ypsilanti www.trainose.gr 8707 2142 άς ce s TRAIN Φανάρι Fanari 39.4 21.8 Fanari Καρδίτσας,Ελλά Karditsas,Greec www.trainose.gr 169 173 Karditsa ς e s
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40.8 26.1 TRAIN Φέρρες,Ελλάς Ferres,Greece www.trainose.gr 9128 8502 Ferres TRAIN 37.4 21.6 Taxiarch Ταξιάρχες Taxiarches www.trainose.gr 2015 6655 es Φιγαλίας,Ελλάς Figalias,Greece Figalias 40.7 21.4 TRAIN Φλώρινα,Ελλάς Florina,Greece www.trainose.gr 8144 1504 Florina 41.0 22.7 TRAIN Φυλακτόν,Ελλά Fylakton,Greec www.trainose.gr 906 8337 Fylakton ς e TRAIN 41.0 24.0 Φωτολίβος,Ελλ Fotolivos,Greec Fotolivo www.trainose.gr 5906 4795 άς e s TRAIN 38.5 22.8 Χαιρώνεια,Ελλά Cheronia,Greec Cheroni www.trainose.gr 0805 5832 ς e a 38.4 23.5 TRAIN Chalkida,Greec Χαλκίδα,Ελλάς www.trainose.gr 6248 8722 Chalkida e TRAIN 41.0 22.7 Χειμώνιον,Ελλά Chimonion,Gre Chimoni www.trainose.gr 906 8337 ς ece on 41.0 22.7 TRAIN Χέρσος,Ελλάς Hersos,Greece www.trainose.gr 906 8337 Hersos 39.5 22.5 TRAIN Χάλκη,Ελλάς Chalki,Greece www.trainose.gr 9228 3525 Chalki TRAIN 37.8 22.8 Chiliomidi,Gree Chiliomi Χιλιομίδι,Ελλάς www.trainose.gr 0533 6904 ce di 38.3 23.6 TRAIN Agios Georgios Chalkida) Αγ. Γεώργιος (4ο χλμ Αθήνα 7172 0492 Χαλκίδα) (4th km Ath TRAIN Chalkida Ναυπηγεια Chalkida 38.4 23.5 shipyard Χαλκίδας (χλμ shipyards (13th www.trainose.gr 1656 9125 s (13th 13),Ελλάς km),Greece km) 39.5 22.6 TRAIN 18th km Larissa Volos Χλμ 18 Λάρισας 5187 1102 Βόλου 39.5 22.6 TRAIN 19th km Larissa Volos Χλμ 19 Λάρισας 3844 3553 Βόλου TRAIN 38.8 22.4 Καλύβια Kalyvia Kalyvia www.trainose.gr 905 054 Λαμίας,Ελλάς Lamias,Greece Lamias TRAIN 38.8 22.4 Παγκράτι Pagkrati Pagkrati www.trainose.gr 9314 2595 Λαμίας,Ελλάς Lamias,Greece Lamias
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TRAIN 38.8 22.4 Ρεβένια Revenia Revenia www.trainose.gr 9301 1743 Λαμίας,Ελλάς Lamias,Greece Lamias TRAIN 41.0 22.7 Sidiroch Σιδηροχώρι Sidirochori www.trainose.gr 906 8337 ori Σερρών,Ελλάς Serron,Greece Serron TRAIN 38.0 22.1 Ζαχλωρού,Ελλά Zachlorou,Gree Zachloro www.trainose.gr 934 649 ς ce u TRAIN 41.0 22.7 Psathades,Gre Psathad Ψαθαδες,Ελλάς www.trainose.gr 906 8337 ece es Ktel- 39.7 21.6 Καλαμπάκα - Kalabaka - Kalabak http://ktel-trikala.gr 0662 2887 Κέντρο Center a Δικα 15, Δικα 15, 40.2 22.5 Ktel- http://www.ktelpierias.gr/el/cont Κατερίνη, Κατερίνη, 7515 1993 Katerinis act-us.html Ελλάς Ελλάς Ktel- Athina- Καβάλας 100, Kavalas 100, 39 23 NULL Kavala- Αθήνα athens Station 39.8 22.1 Ktel- Ελασσόνα, Elasona, http://www.elassona.gov.gr,249 9499 8224 Elasona Ελλάς Greece 30 22528 40.7 21.4 TRAIN Φλώρινα Florina, Greece http://www.trainose.gr 8144 1504 Florina 38.0 22.7 TRAIN- Κιάτο, Ελλάς Kiato, Greece http://www.trainose.gr,ΠΚΙΑ 1222 3583 Kiato 38.0 23.7 TRAIN- Αθήνα, Ελλάς Athens,Greece http:/www.trainose.gr,ΣΚΑΧ 6835 3753 Ska Ktel- 40.8 26.1 Feres Φέρες, Εβρος Feres, Evros http://ktelevrou.gr, 2551026479 925 629 Evros 41.1 26.3 Ktel- Σουφλί Εβρος Soufli Evros http://ktelevrou.gr, 25510 26479 8972 0141 Soufli
201
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Network Links and travel Schedules ‐ Field Trial Distanc e General Duration/Minute Vehicle Ticket KMs Cost s Cost Cost (source-destination) departure time 304 304 225 112.5 25 Αλεξανδρούπολη-Θεσσαλονίκη - 05:15
150 150 120 75.5 15 Αλεξανδρούπολη - Καβάλα 05:15
103 103 70 51.5 15 Αλεξανδρούπολη-Ξάνθη - 05:15 Αλεξανδρούπολη - Αθήνα - 08:00 (Μέσω 800 800 540 400 60 Θεσσ/νικης) 741 741 510 255 60 Κομοτηνή - Αθήνα - 08:30
741 741 510 370 70 Αθήνα - Κομοτηνή - 09:30
95 95 90 42.5 10 Κομοτηνή - Καβάλα - 07:00
95 95 90 42.5 10 Καβάλα - Κομοτηνή - 08:30
248 248 187 124 20 Κομοτηνή - θεσσαλονίκη - 08:30
248 248 187 144 20 θεσσαλονίκη - Κομοτηνή - 08:30
58 58 45 29 10 Ξάνθη - Καβάλα - 08:30
103 103 78 50 12 Ξάνθη - Αλεξανδρούπολη - 10:15
123 123 95 61.5 12 Κοζάνη - Θεσσαλονίκη - 06:00
123 123 95 61.5 12 Θεσσαλονίκη - Κοζάνη - 06:00
55 55 60 27.5 12 Ξάνθη - Κομοτηνή - 06:30
55 55 60 27.5 12 Κομοτηνή - Ξάνθη - 06:30
705 705 480 350 50 Ξάνθη - Αθήνα - 06:00
705 705 480 350 50 Αθήνα - Ξάνθη - 07:00 NUL 121 212 180 NULL Θεσσαλονίκη - Ξάνθη - 06:00 L 121 212 180 61 17 Ξάνθη - Θεσσαλονίκη - 06:00
96 96 90 48 10 Ξάνθη - Δράμα - 08:00
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95 95 90 45 10 Δράμα - Ξάνθη - 08:10
37 37 40 23.5 7 Δράμα - Καβάλα - 06:00
37 37 40 23.5 5 Καβάλα - Δράμα - 06:00
640 640 480 220 50 Δράμα - Αθήνα - 09:00
640 640 480 320 50 Αθήνα - Δράμα - 09:00
148 148 90 74 14 Δράμα - Θεσσαλονίκη - 06:00
148 148 90 74 14 Θεσσαλονίκη - Δράμα - 06:00
58 58 45 29 10 Καβάλα - Ξάνθη - 06:30
648 648 480 324 50 Καβάλα - Αθήνα - 08:45
648 648 480 324 50 Αθήνα - Καβάλα - 08:45
155 155 120 72 15 Καβάλα - Θεσσαλονίκη - 06:00
155 155 120 72 15 Θεσσαλονίκη - Καβάλα - 06:00
50 50 60 25 8 Κιλκίς - Θεσσαλονίκη - 05:45
50 50 60 25 8 Θεσσαλονίκη - Κιλκίς - 06:30
504 504 480 252 40 Βέροια - Αθήνα - 08:00
504 504 480 252 40 Αθήνα - Βέροια - 08:00
70 70 45 35 5 Βέροια - Θεσσαλονίκη - 06:30
70 70 45 35 5 Βέροια - Θεσσαλονίκη - 06:30
20 20 20 10 3 Βέροια - Νάουσα - 06:30
20 20 20 10 3 Νάουσα - Βέροια - 07:25
30 30 40 15 4 Βέροια - Αλεξάνδρεια - 06:50
30 30 40 15 4 Αλεξάνδρεια - Βέροια - 06:30
54 54 54 27 6 Βέροια - Εδεσσα - 07:50
544 544 450 272 55 Εδεσσα - Αθήνα - 08:00
203
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
544 544 450 272 55 Αθήνα - Εδεσσα - 08:30
83 83 90 40 10 Εδεσσα - Θεσσαλονίκη - 06:00
83 83 90 40 10 Θεσσαλονίκη - Εδεσσα - 06:00
39 39 45 20 6 Εδεσσα - Γιαννιτσά - 08:00
39 39 45 20 6 Γιαννιτσά - Εδεσσα - 07:00
98 98 98 49 8 Εδεσσα - Κοζάνη - 15:00
39 39 45 20 6 Κοζάνη - Εδεσσα - 17:00
226 226 180 113 30 Εδεσσα - Ιωάννινα - 16:00
226 226 180 113 30 Ιωάννινα - Εδεσσα - 18:30
87 87 90 43 10 Κοζάνη - Φλώρινα - 06:45
87 87 90 43 10 Φλώρινα - Κοζάνη - 07:00
471 471 450 285 40 Κοζάνη - Αθήνα - 08:15
471 471 450 235 40 Αθήνα - Κοζάνη - 07:45
145 145 72 72 10 Κοζάνη - Ιωάννινα - 10:45
145 145 180 72 10 Ιωάννινα - Κοζάνη - 10:30
65 65 65 23 6 Κοζάνη - Βέροια - 07:15
65 65 65 23 6 Βέροια - Κοζάνη - 06:15
121 121 150 60 10 Κοζάνη - Λάρισα - 08:45
121 121 150 60 10 Λάρισα - Κοζάνη - 08:45
34 34 40 17 4 Κοζάνη - Πτολεμαίδα - 06:45
34 34 40 17 4 Πτολεμαίδα - Κοζάνη - 07:30
182 182 180 91 15 Κοζάνη - Βόλος - 09:00
182 182 180 91 15 Βόλος - Κοζάνη - 13:45
60 60 60 30 6 Κοζάνη - Γρεβενά - 09:00
204
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
60 60 60 30 6 Γρεβενά - Κοζάνη - 08:00
72 72 90 36 10 Κοζάνη - Καστοριά - 07:30
72 72 90 36 10 Καστοριά - Κοζάνη - 09:15
309 309 309 155 30 Κοζάνη - Μεσολόγγι - 11:00
309 309 300 155 30 Μεσολόγγι - Κοζάνη - 08:45
138 138 120 15 69 Πτολεμαίδα - Θεσσαλονίκη - 06:30
138 138 120 69 15 Θεσσαλονίκη - Πτολεμαίδα - 08:30
208 208 200 104 20 Πτολεμαιδα - Βόλος - 07:30
208 208 200 104 20 Βόλος - Πτολεμαιδα - 14:45
496 496 420 298 30 Πτολεμαίδα - Αθήνα - 07:30
496 496 420 298 30 Αθήνα - Πτολεμαίδα - 07:45
170 170 180 85 20 Πτολεμαίδα - Ιωάννινα - 09:40
170 170 200 85 20 Ιωάννινα - Πτολεμαίδα - 10:30
147 147 150 73 10 Πτολεμαίδα - Λάρισα - 07:30
147 147 150 73 10 Λάρισα - Πτολεμαίδα - 10:30
58 58 60 29 5 Πτολεμαίδα - Φλώρινα - 07:15
58 58 60 29 5 Φλώρινα - Πτολεμαίδα - 07:00
534 534 500 267 50 Φλώρινα - Αθήνα - 08:30
534 534 500 267 50 Αθήνα - Φλώρινα - 08:00
156 156 150 78 12 Φλώρινα - Θεσσαλονίκη - 06:30
156 156 150 78 12 Θεσσαλονίκη - Φλώρινα - 07:30
163 163 160 82 15 Γρεβενά - Θεσσαλονίκη - 06:15
163 163 160 82 15 Θεσσαλονίκη - Γρεβενά - 08:30
412 412 420 210 40 Γρεβενά - Αθήνα - 10:00
205
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
412 412 420 206 40 Αθήνα - Γρεβενα - 09:00
93 93 90 42 10 Γρεβενά - Τρίκαλα - 10:00
93 93 90 42 10 Τρίκαλα - Γρεβενά - 10:45
137 137 140 63 15 Γρεβενά - Λάρισα - 15:00
137 137 130 63 15 Λάρισα - Γρεβενά - 07:30
101 101 100 51 15 Γρεβενά - Ιωάννινα - 09:30
101 101 100 51 15 Ιωάννινα - Γρεβενά - 07:00
430 430 420 215 40 Ιωάννινα - Αθήνα - 07:15
430 430 420 215 40 Αθήνα - Ιωάννινα - 06:30
198 198 180 100 20 Ιωάννινα - Βέροια - 07:00
260 260 240 130 25 Ιωάννινα - Θεσσαλονίκη - 07:00
260 260 240 130 25 Θεσσαλονίκη - Ιωάννινα - 08:00
198 198 180 100 20 Βέροιας - Ιωάννινα - 09:00
129 129 180 65 15 Ιωάννινα - Τρίκαλα - 08:45
129 129 180 65 15 Τρίκαλα - Ιωάννινα - 08:30
227 227 320 114 20 Ιωάννινα - Πάτρα - 09:30
227 227 240 114 20 Πάτρα - Ιωάννινα - 09:30
160 160 210 80 15 Ιωάννινα - Αγρίνιο - 07:00
160 160 180 80 15 Αγρίνιο - Ιωάννινα - 07:00
81 81 120 41 10 Ιωάννινα - Ηγουμενίτσα -06:45
81 81 120 41 10 Ηγουμενίτσα - Ιωάννινα - 06:45
70 70 90 35 8 Ιωάννινα - Αρτα - 08:30
70 70 90 35 8 Αρτα - Ιωάννινα - 06:30
103 103 120 52 12 Ιωάννινα - Πρέβεζα - 06:30
206
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
103 103 120 42 12 Πρέβεζα - Ιωάννινα - 07:30
190 190 210 95 18 Ιωάννινα - Καρδίτσα - 12:00
190 190 210 95 18 Καρδίτσα - Ιωάννινα - 14:30
246 246 300 123 25 Ιωάννινα - Λαμία - 12:00
246 246 300 123 25 Λαμία - Ιωάννινα - 13:15
593 593 480 300 50 Ιωάννινα - Χαλκίδα - 12:00
593 593 480 300 50 Χαλκίδα - Ιωάννινα - 10:00
338 338 420 170 35 Ιωαννίνων - Λιβαδειά - 12:00
338 338 420 180 35 Λιβαδειά - Ιωαννίνων - 12:00
310 310 360 155 25 Ιωάννινα - Ιτέα - 12:00
310 310 360 155 25 Ιτέα - Ιωάννινα - 13:00
224 224 300 112 20 Ιωάννινα - Ναύπακτος - 12:00
224 224 300 112 20 Ναύπακτος - Ιωάννινα - 14:00
183 183 240 92 15 Ιωάννινα - Μεσολόγγι - 12:00
183 183 240 92 15 Μεσολλόγι - Ιωάννινα - 15:00
320 320 270 160 25 Τρίκαλα - Αθήνα - 07:00
114 114 140 57 15 Τρίκαλα - Λαμία - 07:00
144 114 140 57 15 Λαμία - Τρίκαλα - 09:30
320 320 270 160 28 Αθήνα - Τρίκαλα - 07:30
25 25 40 13 2.7 Τρίκαλα - Καρδίτσα - 06:15
25 25 40 13 2.7 Καρδίτσα - Τρίκαλα - 06:15
60 60 60 30 6.3 Τρίκαλα - Λάρισα - 06:30
60 60 60 30 6.3 Λάρισα - Τρίκαλα - 06:00
141 141 180 70 13.7 Τρίκαλα - Βόλος - 07:30
207
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
141 141 180 70 13.7 Βόλος - Τρίκαλα - 06:30
223 223 180 112 18.7 Τρίκαλα - Θεσσαλονίκη - 07:30
223 223 180 112 18.7 Θεσσαλονίκη - Τρίκαλα - 08:30
191 191 210 95 20 Τρίκαλα - Αρτα - 16:00
236 236 240 120 25 Τρίκαλα - Πρέβεζα - 16:00
74 74 90 37 10 Τρίκαλα - Φάρσαλα - 07:00
74 74 90 37 10 Φάρσαλα - Τρίκαλα - 08:00
344 344 240 172 30 Λάρισα - Αθήνα - 08:00
344 344 240 172 30 Αθήνα - Λάρισα - 07:30
160 160 120 80 20 Λάρισης - Θεσσαλονίκης - 07:00
160 160 120 80 20 Θεσσαλονίκη - Αθήνα - 07:30
60 60 65 30 6 Λάρισα - Βόλος - 05:45
60 60 65 30 5 Βόλος - Λάρισα - 05:45
34 34 40 17 5 Λάρισα - Φάρσαλα - 05:15
34 34 40 17 5 Φάρσαλα - Λάρισα - 06:15
18 18 25 9 4 Λάρισα - Τύρναβος - 05:15
18 18 25 9 4 Τύρναβος - Λάρισα - 06:10
35 35 45 17 4 Βόλος - Αλμυρός - 05:30
35 35 45 17 4 Αλμυρός - Βόλος - 06:15
324 324 270 162 25 Βόλος - Αθήνα - 04:45
324 324 270 162 25 Αθήνα - Βόλος - 07:00
206 206 180 103 15 Βόλος - Θεσσαλονίκη - 05:45
206 206 180 103 15 Θεσσαλονίκη - Αθήνα - 08:15
248 248 270 124 30 Βόλος - Ιωάννινα - 08:15
208
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
248 248 270 124 30 Ιωάννινα - Βόλος - 10:45
115 115 120 58 10 Βόλος - Λαμία - 08:30
115 115 120 58 10 Λαμία - Βόλος - 09:15
275 275 300 138 30 Βόλος - Αγρίνιο - 12:45
275 275 300 138 30 Αγρίνιο - Βόλος - 14:00
292 292 360 146 35 Βόλος - Πάτρα - 15:00
292 292 360 146 35 Πάτρα - Βόλος - 15:30
296 296 270 150 25 Καρδίτσα - Αθήνα - 01:00
296 296 270 198 26 Αθήνα - Καρδίτσα - 07:30
216 216 180 108 19 Καρδίτσα - Θεσσαλονίκη - 07:00
216 216 180 108 19 Θεσσαλονίκη - Καρδίτσα - 10:00
57 57 70 29 6.4 Καρδίτσα - Λάρισα - 06:00
57 57 70 29 6.4 Λάρισα - Καρδίτσα - 06:00
114 114 130 57 10.5 Καρδίτσα - Βόλος - 07:30
114 114 130 57 10.5 Βόλος - Καρδίτσα - 06:00
235 235 300 118 28 Καρδίτσα - Πάτρα - 15:30
235 235 300 118 28 Πάτρα - Καρδίτσα - 15:15
208 208 140 104 15 Λαμία - Αθήνα - 05:00
208 208 140 104 15 Αθήνα - Λαμία - 07:15
261 261 240 130 20 Λαμία - Θεσσαλονίκη - 09:00
261 261 240 130 20 Θεσσαλονίκη - Λαμία - 09:00
175 175 240 88 15 Λαμία - Πάτρα - 12:15
175 175 240 88 15 Πάτρα - Λαμία - 11:30
91 91 120 46 10 Λαμία - Καρδίτσα - 10:15
209
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
91 91 120 46 10 Καρδίτσα - Λαμία - 08:00
71 71 90 36 5 Λαμία - Αμφισσα - 10:40
71 71 90 36 5 Αμφισσα - Λαμία - 08:30
165 165 150 83 15 Λαμία - Χαλκίδα - 16:30
165 165 150 83 15 Χαλκίδα - Λαμία - 11:00
155 155 210 78 12 Λαμία - Αγρίνιο - 14:30
155 155 210 78 12 Αγρίνιο - Λαμία - 18:00
206 206 240 103 20 Λαμία - Γρεβενά - 11:45
206 206 240 103 20 Γρεβενά - Λαμία - 14:00
76 76 120 38 8 Λαμία - Καρπενήσι - 06:50
76 76 120 38 8 Καρπενήσι - Λαμία - 05:00
285 285 270 143 25 Αγρίνιο - Αθήνα - 06:00
285 285 270 143 25 Αθήνα - Αγρίνιο - 06:00
400 400 360 200 35 Αγρίνιο - Θεσσαλονίκη - 09:30
400 400 360 200 35 Θεσσαλονίκη - Αγρίνιο - 09:30
83 83 100 42 6 Αγρίνιο - Πάτρα - 05:15
83 83 100 42 6 Πάτρα - Αγρίνιο - 06:00
400 400 420 200 35 Αγρίνιο - Λάρισα - 14:00
400 400 420 200 35 Λάρισα - Αγρίνιο - 12:00
211 211 480 106 40 Αγρίνιο - Κέρκυρα - 10:40
315 315 305 160 30 Αγρίνιο - Χαλκίδα - 14:30
315 315 320 160 30 Χαλκίδα - Αγρίνιο - 11:00
221 221 250 110 25 Αγρίνιο - Λιβαδειά - 14:30
221 221 250 110 25 Λιβαδειά - Αγρίνιο - 12:00
210
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
608 608 720 400 80 Αγρίνιο - Ηράκλειο - 14:30
180 180 186 90 15 Αγρίνιο - Πύργος - 15:30
84 84 100 42 10 Αγρίνιο - Αρτα - 06:15
84 84 100 42 10 Αρτα - Αγρίνιο - 08:00
70 70 80 35 8 Αγρίνιο - Βόνιτσα - 05:00
70 70 80 35 8 Βόνιτσα - Αγρίνιο - 06:20
98 98 110 49 10 Αγρίνιο - Πρέβεζα - 08:00
91 91 101 45 12 Αγρίνιο - Λευκάδα - 05:00
91 91 101 45 12 Λευκάδα - Αγρίνιο - 14:30
41 41 50 21 5 Αγρίνιο - Αμφιλοχία - 05:00
41 41 50 21 5 Αμφιλοχία - Αγρίνιο - 07:00
39 39 52 20 5 Αγρίνιο - Μεσολόγγι - 05:15
39 39 52 20 5 Μεσολόγγι - Αγρίνιο - 06:15
29 29 40 15 4 Αγρίνιο - Αιτωλικό - 05:15
29 29 40 20 4 Αιτωλικό - Αγρίνιο - 06:15
66 66 90 33 5 Αγρίνιο - Ναύπακτος - 08:00
271 271 240 135 20 Μεσολόγγι - Αθήνα - 06:30
271 271 240 135 20 Αθήνα - Μεσολόγγι - 06:00
49 49 60 25 5 Μεσολόγγι - Πάτρα - 07:15
49 49 60 30 5 Πάτρα - Μεσολόγγι - 07:30
423 423 360 212 35 Μεσολόγγι - Θεσσαλονίκη - 08:40
423 423 360 212 35 Θεσσαλονίκη - Μεσολόγγι - 09:30
277 277 300 138 30 Μεσολόγγι - Χαλκίδα - 15:00
277 277 300 138 30 Χαλκίδα - Μεσολόγγι - 15:00
211
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
186 186 300 98 25 Μεσολόγγι - Λιβαδειά - 15:00
186 186 300 98 25 Λιβαδειά - Μεσολόγγι - 15:00
45 45 60 23 5 Μεσολόγγι - Ναύπακτος - 15:00
45 45 60 23 5 Ναύπακτος - Μεσολόγγι - 15:00
357 357 300 150 30 Αρτα - Αθήνα - 07:30
357 357 300 180 30 Αθήνα - Αρτα - 07:00
318 318 340 159 25 Αρτα - Θεσσαλονίκη - 09:45
318 318 340 159 25 Θεσσαλονίκη - Αρτα - 10:45
247 247 240 124 30 Αρτα - Λάρισα - 09:45
247 247 240 124 30 Λάρισα - Αρτα - 15:00
154 154 180 90 20 Αρτα - Πάτρα - 11:00
154 154 180 77 20 Πάτρα - Αρτα - 14:45
112 112 120 56 10 Λαμία - Λάρισα - 11:15
112 112 120 56 10 Λάρισα - Λαμία - 14:15
40 35 40 20 3 Λαμία - Ράχες Λαμίας - 07:00
35 35 40 18 3 Λαμία Ράχες - Λαμία - 07:00
142 142 90 71 15 Αταλάντη - Αθήνα - 06:45
142 142 90 71 15 Αθήνα - Αταλάντη - 14:30
285 285 240 193 25 Λαμία - Πύργος - 13:15
930 551 840 300 100 Λαμία - Ηράκλειο - 15:30
88 88 90 44 10 Θήβα - Αθήνα - 06:00
88 88 90 44 10 Αθήνα - Θήβα - 06:00
44 44 40 22 5 Θήβα - Χαλκίδα - 08:00
44 44 40 22 5 Χαλκίδα - Θήβα - 09:30
212
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
133 133 120 66 10 Λιβαδειά - Αθήνα - 06:00
133 133 100 67 10 Αθήνα - Λιβαδειά - 08:00
50 50 50 25 5 Λιβαδειά - Θήβα - 06:00
50 50 50 25 5 Θήβα - Λιβαδειά - 09:00
72 72 60 36 7 Χαλκίδα - Αθήνα - 05:00
72 72 60 36 7 Αθήνα - Χαλκίδα - 05:50
451 451 300 276 40 Χαλκίδα - Θεσσαλονίκη - 09:00
451 451 300 276 40 Θεσσαλονίκη - Χαλκίδα - 13:00
217 217 180 114 15 Πάτρα - Αθήνα - 02:30
217 217 180 109 15 Αθήνα - Πάτρα - 05:30
40 40 45 20 3 Πάτρα - Αίγιο - 07:00
40 40 45 20 5 Αίγιο - Πάτρα - 05:55
474 474 420 237 45 Πάτρα - Θεσσαλονίκη - 08:30
474 474 420 237 45 Θεσσαλονίκη - Πάτρα - 08:15
328 328 300 164 35 Πάτρα - Λάρισα - 08:30
328 328 300 164 35 Λάρισα - Πάτρα - 10:15
213 213 210 107 20 Πάτρα - Καλαμάτα - 08:30
213 213 210 107 20 Καλαμάτα - Πάτρα - 08:30
95 95 100 48 10 Πάτρα - Πύργος - 05:30
95 95 100 43 10 Πύργος - Πάτρα - 05:30
248 248 180 144 20 Καλαμάτα - Αθήνα - 04:45
248 248 180 144 23 Αθήνα - Καλαμάτα - 07:30
60 60 80 30 10 Καλαμάτα - Σπάρτη - 09:15
60 60 80 30 10 Σπάρτη - Καλαμάτα - 09:00
213
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
63 63 120 32 5 Καλαμάτα - Γαργαλιάνοι - 09:30
63 63 120 30 5 Γαργαλιάνοι - Καλαμάτα - 06:15
220 220 150 110 18 Σπάρτη - Αθήνα - 06:00
220 220 150 110 15 Αθήνα - Σπάρτη - 06:30
262 262 200 131 25 Γύθειο - Αθήνα - 07:30
262 262 200 131 25 Αθήνα - Γύθειο - 06:30
164 164 120 82 15 Τρίπολη - Αθήνα - 05:00
164 164 120 82 15 Αθήνα - Τρίπολη - 05:50
37 37 45 19 4 Τρίπολη - Μεγαλόπολη - 07:00
37 37 45 19 3 Μεγαλόπολη - Τρίπολη - 08:00
85 85 80 43 8 Τρίπολη - Καλαμάτα - 08:30
85 85 80 43 8 Καλαμάτα - Τρίπολη - 12:45
165 165 180 83 15 Τρίπολη - Πάτρα - 14:30
165 165 180 83 15 Πάτρα - Τρίπολη - 19:00
54 54 50 27 5 Τρίπολη - Αργος - 10:10
65 65 65 33 7 Τρίπολη - Ναυπλιο - 10:10
54 54 50 27 5 Αργος - Τρίπολη - 08:45
65 65 60 33 7 Ναύπλιο - Τρίπολη - 08:30
197 197 150 99 18 Μεγαλόπολη - Αθήνα - 05:30
197 197 150 99 18 Αθήνα - Μεγαλόπολη - 05:50
133 133 100 67 12 Αργος - Αθήνα - 05:20
133 133 100 67 12 Αθήνα - Αργος - 08:00
12 12 20 6 2 Αργος - Ναύπλιο - 10:00
12 12 15 6 2 Ναύπλιο - Αργος - 05:10
214
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
90 90 75 45 10 Κόρινθος - Αθήνα - 14:30
90 90 75 45 10 Αθήνα - Κόρινθος - 14:10
38 38 45 19 5 Κόρινθος - Νεμέα - 15:30
38 38 45 19 5 Νεμέα - Κόρινθος - 18:20
22 22 40 11 2 Κόρινθος - Κιάτο - 10:00
22 22 40 11 2 Κιάτο - Κόρινθος - 11:00
773 773 879 4638 64.8 Αθήνα-Αλεξανδρούπολη-07:18
476 476 458 2856 44.8 Αθήνα-Βέροια-07:18
374 374 300 2244 35.3 Αθήνα-Βόλος-07:18
916 916 1084 5496 69.1 Αθήνα-Δίκαια-07:18
586 586 698 3516 56.6 Αθήνα-Δράμα-07:18
498 498 495 2988 47.8 Αθήνα-Εδεσσα-07:18
424 424 323 2544 45.4 Αθήνα-Θεσσαλονίκη-07:18
368 368 291 2208 18.3 Αθήνα-Καλαμπάκα-08:27
299 299 242 1794 34.3 Αθήνα-Λάρισα-07:18
216 216 143 1296 20.9 Αθήνα-Λειανοκλάδι-07:18
273 273 222 1638 27.3 Αθήνα-Παλαιοφάρσαλος-07:18
521 521 648 3126 54.3 Αθήνα-Σέρρες-07:18
245 245 270 1470 22.2 Αθήνα-Στυλίδα-07:18
773 773 779 4638 64.8 Αλεξανδρούπολη-Αθήνα-07:25
435 435 451 2610 36.7 Αλεξανδρούπολη-Βέροια-07:25
592 592 1 3552 37.2 Αλεξανδρούπολη-Βόλος-07:25
143 143 186 858 5.1 Αλεξανδρούπολη-Δίκαια-08:03
208 208 180 1248 10.4 Αλεξανδρούπολη-Δράμα-07:25
215
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
456 456 488 2736 32.7 Αλεξανδρούπολη-Εδεσσα-07:25
349 349 343 2094 19.4 Αλεξανδρούπολη-Θεσσαλονίκη-07:25
563 563 720 3378 34.5 Αλεξανδρούπολη-Καλαμπάκα-07:25
518 518 515 3108 33.4 Αλεξανδρούπολη-Λάρισα-07:25
630 630 638 3780 46.6 Αλεξανδρούπολη-Λειανοκλάδι-07:25
572 572 560 3432 40.2 Αλεξανδρούπολη-Παλαιοφάρσαλος-07:25
277 277 230 1662 13.3 Αλεξανδρούπολη-Σέρρες-07:25
660 660 678 3960 48.1 Αλεξανδρούπολη-Στυλίδα-07:25
476 476 332 2856 44.8 Βέροια-Αθήνα-06:52
435 435 905 2610 36.7 Βέροια-Αλεξανδρούπολη-06:52
578 578 1110 3468 40.8 Βέροια-Δίκαια-06:52
249 249 724 1494 28.1 Βέροια-Δράμα-06:52
46 46 37 276 3 Βέροια-Εδεσσα-08:19
86 86 57 516 5 Βέροια-Θεσσαλονίκη-06:52
198 198 94 1188 15.9 Βέροια-Λάρισα-06:52
313 313 194 1878 27.7 Βέροια-Λειανοκλάδι-06:52
249 249 113 1494 20.8 Βέροια-Παλαιοφάρσαλος-06:52
184 184 674 1104 26.2 Βέροια-Σέρρες-06:52
374 374 293 2244 35.3 Βόλος-Αθήνα-05:30
592 592 987 3552 37.2 Βόλος-Αλεξανδρούπολη-05:30
736 736 1192 4416 41.3 Βόλος-Δίκαια-05:30
406 406 806 2436 28.9 Βόλος-Δράμα-05:30
243 243 206 1458 17.6 Βόλος-Θεσσαλονίκη-05:30
224 224 142 1344 16.5 Βόλος-Καλαμπάκα-05:30
216
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
74 74 48 444 3.6 Βόλος-Λάρισα-05:30
189 189 160 1134 16.5 Βόλος-Λειανοκλάδι-05:30
128 128 82 768 12.9 Βόλος-Παλαιοφάρσαλος-05:30
341 341 756 2046 26.5 Βόλος-Σέρρες-05:30
219 219 198 1314 18.2 Βόλος-Στυλίδα-05:30
916 916 1439 5496 42.3 Δίκαια-Αθήνα-04:26
143 143 188 858 5.1 Δίκαια-Αλεξανδρούπολη-04:26
736 736 1439 4416 29.2 Δίκαια-Βόλος-04:26
351 351 871 2106 11.3 Δίκαια-Δράμα-04:26
492 492 1062 2952 17.2 Δίκαια-Θεσσαλονίκη-04:26
811 811 1439 4866 32.3 Δίκαια-Καλαμπάκα-04:26
661 661 1217 3966 25.5 Δίκαια-Λάρισα-04:26
773 773 1325 4638 32.3 Δίκαια-Λειανοκλάδι-04:26
715 715 1241 4290 28.1 Δίκαια-Παλαιοφάρσαλος-04:26
421 421 925 2526 12.9 Δίκαια-Σέρρες-04:26
803 803 1439 4818 33.6 Δίκαια-Στυλίδα-04:26
586 586 599 3516 56.6 Δράμα-Αθήνα-10:25
208 208 191 1248 6.6 Δράμα-Αλεξανδρούπολη-10:26
249 249 271 1494 28.1 Δράμα-Βέροια-10:25
406 406 413 2436 28.9 Δράμα-Βόλος-10:25
351 351 442 2106 11.3 Δράμα-Δίκαια-10:26
269 269 308 1614 24.1 Δράμα-Εδεσσα-10:25
162 162 163 972 11.2 Δράμα-Θεσσαλονίκη-10:25
376 376 540 2256 26.4 Δράμα-Καλαμπάκα-10:25
217
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
332 332 335 1992 25.2 Δράμα-Λάρισα-10:25
444 444 458 2664 38.5 Δράμα-Λειανοκλάδι-10:25
385 385 380 2310 32.1 Δράμα-Παλαιοφάρσαλος-10:25
72 72 50 432 5.3 Δράμα-Σέρρες-10:25
473 473 498 2838 40 Δράμα-Στυλίδα-10:25
498 498 370 2988 47.8 Εδεσσα-Αθήνα-06:14
456 456 943 2736 32.7 Εδεσσα-Αλεξανδρούπολη-06:14
46 46 38 276 3 Εδεσσα-Βέροια-06:14
599 599 1148 3594 36.6 Εδεσσα-Δίκαια-06:14
269 269 762 1614 24.1 Εδεσσα-Δράμα-06:14
106 106 95 636 7 Εδεσσα-Θεσσαλονίκη-06:14
220 220 132 1320 18.9 Εδεσσα-Λάρισα-06:14
335 335 232 2010 30.7 Εδεσσα-Λειανοκλάδι-06:14
271 271 151 1626 23.8 Εδεσσα-Παλαιοφάρσαλος-06:14
204 204 712 1224 22.2 Εδεσσα-Σέρρες-06:14
424 424 310 2544 45.4 Θεσσαλονίκη-Αθήνα-05:13
349 349 386 2094 12.9 Θεσσαλονίκη-Αλεξανδρούπολη-07:11
86 86 59 516 5 Θεσσαλονίκη-Βέροια-07:20
243 243 132 1458 21.9 Θεσσαλονίκη-Βόλος-05:13
492 492 637 2952 17.2 Θεσσαλονίκη-Δίκαια-07:11
162 162 195 972 7.4 Θεσσαλονίκη-Δράμα-07:11
106 106 96 636 7 Θεσσαλονίκη-Εδεσσα-07:20
169 169 80 1014 18.3 Θεσσαλονίκη-Λάρισα-05:13
281 281 177 1686 27.3 Θεσσαλονίκη-Λειανοκλάδι-05:13
218
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
223 223 99 1338 20.9 Θεσσαλονίκη-Παλαιοφάρσαλος-05:13
97 97 139 582 5.1 Θεσσαλονίκη-Σέρρες-07:11
310 310 215 1860 28.8 Θεσσαλονίκη-Στυλίδα-05:13
368 368 280 2208 18.3 Καλαμπάκα-Αθήνα-17:32
563 563 818 3378 34.5 Καλαμπάκα-Αλεξανδρούπολη-08:19
706 706 1023 4236 38.8 Καλαμπάκα-Δίκαια-08:19
376 376 637 2256 26.4 Καλαμπάκα-Δράμα-08:19
155 155 131 930 8.4 Καλαμπάκα-Λειανοκλάδι-17:32
96 96 54 576 5 Καλαμπάκα-Παλαιοφάρσαλος-05:42
311 311 587 1866 24 Καλαμπάκα-Σέρρες-08:19
184 184 316 1104 9.9 Καλαμπάκα-Στυλίδα-17:32
299 299 258 1794 17.8 Λάρισα-Αθήνα-00:43
518 518 559 3108 21.2 Λάρισα-Αλεξανδρούπολη-04:18
198 198 241 1188 8.8 Λάρισα-Βέροια-04:18
74 74 48 444 3.6 Λάρισα-Βόλος-04:37
661 661 810 3966 25.5 Λάρισα-Δίκαια-04:18
332 332 368 1992 15.8 Λάρισα-Δράμα-04:18
220 220 278 1320 11.8 Λάρισα-Εδεσσα-04:18
169 169 103 1014 8.4 Λάρισα-Θεσσαλονίκη-04:18
115 115 108 690 6.8 Λάρισα-Λειανοκλάδι-00:43
53 53 24 318 2.9 Λάρισα-Παλαιοφάρσαλος-00:43
267 267 312 1602 13.4 Λάρισα-Σέρρες-04:18
630 630 667 3780 28 Λειανοκλάδι-Αλεξανδρούπολη-02:30
313 313 349 1878 15.6 Λειανοκλάδι-Βέροια-02:30
219
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
189 189 175 1134 9.4 Λειανοκλάδι-Βόλος-02:30
773 773 918 4638 32.3 Λειανοκλάδι-Δίκαια-02:30
444 444 476 2664 22.6 Λειανοκλάδι-Δράμα-02:30
335 335 386 2010 18.6 Λειανοκλάδι-Εδεσσα-02:30
281 281 211 1686 15.2 Λειανοκλάδι-Θεσσαλονίκη-02:30
155 155 136 930 8.4 Λειανοκλάδι-Καλαμπάκα-11:02
115 115 108 690 6.8 Λειανοκλάδι-Λάρισα-02:30
67 67 83 402 5 Λειανοκλάδι-Παλαιοφάρσαλος-02:30
379 379 420 2274 20.2 Λειανοκλάδι-Σέρρες-02:30
29 29 33 174 1.5 Λειανοκλάδι-Στυλίδα-05:15
572 572 584 3432 23.8 Παλαιοφάρσαλος-Αλεξανδρούπολη-03:53
249 249 266 1494 10.9 Παλαιοφάρσαλος-Βέροια-03:53
128 128 92 768 5.8 Παλαιοφάρσαλος-Βόλος-03:53
715 715 835 4290 28.1 Παλαιοφάρσαλος-Δίκαια-03:53
385 385 393 2310 18.4 Παλαιοφάρσαλος-Δράμα-03:53
271 271 303 1626 13.9 Παλαιοφάρσαλος-Εδεσσα-03:53
223 223 128 1338 11 Παλαιοφάρσαλος-Θεσσαλονίκη-03:53
96 96 54 576 5 Παλαιοφάρσαλος-Καλαμπάκα-04:38
53 53 25 318 2.9 Παλαιοφάρσαλος-Λάρισα-03:53
320 320 337 1920 16 Παλαιοφάρσαλος-Σέρρες-03:53
521 521 549 3126 54.3 Σέρρες-Αθήνα-11:15
277 277 247 1662 8.6 Σέρρες-Αλεξανδρούπολη-09:30
184 184 221 1104 26.2 Σέρρες-Βέροια-11:15
341 341 363 2046 26.5 Σέρρες-Βόλος-11:15
220
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
421 421 498 2526 12.9 Σέρρες-Δίκαια-09:30
72 72 56 432 2.7 Σέρρες-Δράμα-09:30
204 204 258 1224 22.2 Σέρρες-Εδεσσα-11:15
97 97 113 582 8.9 Σέρρες-Θεσσαλονίκη-11:15
311 311 490 1866 24 Σέρρες-Καλαμπάκα-11:15
267 267 285 1602 22.9 Σέρρες-Λάρισα-11:15
379 379 408 2274 36.1 Σέρρες-Λειανοκλάδι-11:15
320 320 330 1920 29.7 Σέρρες-Παλαιοφάρσαλος-11:15
408 408 448 2448 37.6 Σέρρες-Στυλίδα-11:15
660 660 950 3960 48.1 Στυλίδα-Αλεξανδρούπολη-06:07
219 219 371 1314 18.2 Στυλίδα-Βόλος-06:07
803 803 1155 4818 52.2 Στυλίδα-Δίκαια-06:07
473 473 769 2838 40 Στυλίδα-Δράμα-06:07
310 310 394 1860 28.8 Στυλίδα-Θεσσαλονίκη-06:07
184 184 431 1104 9.9 Στυλίδα-Καλαμπάκα-06:07
144 144 313 864 15.4 Στυλίδα-Λάρισα-06:07
29 29 32 174 1.5 Στυλίδα-Λειανοκλάδι-06:07
97 97 293 582 13.6 Στυλίδα-Παλαιοφάρσαλος-06:07
408 408 719 2448 37.6 Στυλίδα-Σέρρες-06:07 Κτέλ Αλεξανδρούπολη - ΤΡΑΙΝΟ 0.3 0.3 2 0 0 Αλεξανδρούπολη ΤΡΑΙΝΟ Αλεξανδρούπολη - Κτέλ 0.3 0.3 4 0 0 Αλεξανδρούπολη 2.8 2.8 10 0.5 4 Κτέλ Βέροια - ΤΡΑΙΝΟ Βέροια
2.8 2.8 10 0.5 4 ΤΡΑΙΝΟ Βέροια - Κτέλ Βέροια
0.85 0.85 10 0 0 Κτέλ Βόλου - ΤΡΑΙΝΟ Βόλου
221
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
0.85 0.85 10 0 0 ΤΡΑΙΝΟ Βόλου - Κτέλ Βόλου
1.2 1.2 8 0.5 3.5 Κτελ Δράμας - ΤΡΑΙΝΟ Δράμας
1.2 1.2 8 0.5 3.5 ΤΡΑΙΝΟ Δράμα - Κτελ Δράμα
2 2 10 0.5 4 Κτέλ Εδεσσας - ΤΡΑΙΝΟ Εδεσσα
2 2 10 0.5 4 ΤΡΑΙΝΟ Εδεσσα - Κτέλ Εδεσσας
2.6 2.6 10 0 0.9 Κτέλ Θεσσαλονίκη - ΤΡΑΙΝΟ Θεσσαλονίκη
2.6 2.6 10 0.5 0.9 ΤΡΑΙΝΟ Θεσσαλονίκη - Κτέλ Θεσσαλονίκη
30 30 40 15 2.1 Τρίκαλα - Καλαμπάκα - 05:15
30 30 40 15 2.1 Καλαμπάκα - Τρίκαλα - 05:50
0.6 0.6 8 0 0 Κτέλ Καλαμπάκα - ΤΡΑΙΝΟ Καλαμπάκα
0.6 0.6 8 0 0 ΤΡΑΙΝΟ Καλαμπάκα - Κτελ Καλαμπάκα
1.3 1.3 4 0.5 3.5 Κτέλ Λάρισα - ΤΡΑΙΝΟ Λάρισα
1.3 1.3 4 0.5 3.5 ΤΡΑΙΝΟ Λάρισα - Κτελ Λάρισα
8 8 15 4 10 Κτελ Λαμία - ΤΡΑΙΝΟ Λειανοκλάδι
8 8 15 4 10 ΤΡΑΙΝΟ Λειανοκλάδι - Κτέλ Λαμία
1.7 1.7 7 0.9 4 Κτέλ Σέρρες - ΤΡΑΙΝΟ Σέρρες
1.7 1.7 7 0.9 4 ΤΡΑΙΝΟ Σέρρες - Κτέλ Σέρρες
0.25 0.25 4 0 0 Κτέλ Στυλίδα - ΤΡΑΙΝΟ Στυλίδα
0.25 0.25 4 0 0 ΤΡΑΙΝΟ Στυλίδα - Κτελ Στυλίδα
5 5 25 0 1 Κτελ Αθήνα - ΤΡΑΙΝΟ Αθήνα
5 5 25 0 1 ΤΡΑΙΝΟ Αθήνα - ΚΤΕΛ Αθήνα
426 426 300 123 30 Κατερίνη - Αθήνα - 09:30
426 426 300 123 30 Αθήνα - Κατερίνη - 09:45
66 66 60 33 7 Κατερίνη - Θεσσαλονίκη - 05:15
222
D5.2 [Report on Collective Evaluation from Field Trials in Phase‐1]
66 66 60 33 7 Θεσσαλονίκη - Κατερίνη - 06:15
55 55 60 27 5 Κατερίνη - Αλεξάνδρεια - 07:05
55 55 60 27 5 Αλεξάνδρεια - Κατερίνη - 08:05
70 70 90 35 7 Κατερίνη - Ελασσόνα - 06:30
70 70 90 35 7 Ελασσόνα - Κατερίνη - 08:55
40 40 45 20 5 Λάρισα - Ελασσόνα - 05:15
40 40 45 20 5 Ελασσόνα - Λάρισα - 06:15
208 208 120 1000 9 ΤΡΑΙΝΟ - Θεσσαλονίκη-Φλώρινα - 07:14
208 208 120 1040 5 ΤΡΑΙΝΟ -Φλώρινα - Θεσσαλονίκη- 06:154
107 107 71 535 1.8 TΡΑΙΝΟ ΣΚΑ - Κιάτο - 06:13
107 107 71 535 1.8 TΡΑΙΝΟ Κιάτο - ΣΚΑ - 07:00
11 11 9 55 1.5 TΡΑΙΝΟ Αθήνα- ΣΚΑ - 04:49
11 11 9 55 1.8 TΡΑΙΝΟ ΣΚΑ - Αθήνα - 04:52
28 28 35 14 5 Αλεξανδρούπολη - Φέρες Εβρου - 04:15
223