D2.1: Definition of the Use Case
Work package WP2: Requirements and Specifications
Task Task 2.1: Use cases and transport mission
CODOGNOTTO and ELEVANTE: Fabrizio Borgogna, Valentina Boschian, Andrea Condotta;OKAN: Orhan Alankus;LEEDS: Haibo Authors Chen;FORD OTOSAN: Kerem Behlivan, Kerem Koprubasi;CERTH/ELIAD: Dimitris Margaritis
Dissemination level Public (PU)
Status Final
Due date 31/12/2016
Document date 31/01/2017
Version number 1.0
File Name optiTruck_D2.1_Definition of the Use Case_v1.0
optiTruck is co-funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 713788
Control sheet
Version history
Version Date Main author Summary of changes
0.1 18/10/2016 Valentina Boschian Table of Content
Valentina Boschian, 0.2 20/10/2016 First description of the approach Andrea Condotta
First results about statics data about EU 0.3 18/11/2016 Fabrizio Borgogna transport missions
Valentina Boschian, Codognotto survey and summary of the 0.4 21/11/2016 Andrea Condotta workshop with the users
Fabrizio Borgogna, Additional results about statics data 0.5 24/11/2016 Valentina Boschian about EU transport missions
0.6 00/00/2016 Valentina Boschian Use Case template
0.7 00/00/2016 Valentina Boschian First definition of project Use Cases
Chapter 2: Literature analysis and 0.8 16/12/2016 Haibo Chen background
Orhan Alankus, Kerem Chapter 2: TEN-T corridors analysis and 0.9 19/12/2016 Behlivan,Kerem fuel-emission review Koprubasi
Valentina Boschian, First draft of D2.1 circulated for internal 0.10 21/12/2016 Andrea Condotta review
Consolidated version after partners (IAV, 0.11 09/01/2017 Valentina Boschian OKAN and FO) contributions
Contributions in Chapter 3, 4 and Annexes 1 and 3 concerning Eliadis data 0.12 16/01/2017 Dimitris Margaritis and summary of the questionnaire results
Complete version of the deliverable 0.13 19/01/2017 Valentina Boschian presented for partners’ comments in WP2 weekly telco
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page i
Final version submitted for internal 0.14 20/01/2017 Valentina Boschian review
1.0 31/01/2017 Valentina Boschian Final submitted version
Name(s) Organisation(s) Date
Valentina Main author/ editor: Boschian, Andrea Codognotto, Elevante 30/01/2017 Condotta
Alfredo Favenza ISBM Peer reviewed by: Thorsten Stamm 27/01/2017 IAV von Baumgarten
Orhan Behiç Alankuş OKAN (WP2 leader) Authorised by: 31/01/2017 Jean-Charles ERTICO (Project Coordinator) Pandazis
Jean-Charles Submitted by: ERTICO (Project Coordinator) 31/01/2017 Pandazis
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page ii
Table of contents
Executive Summary ...... xi
1. Introduction ...... 1
1.1 Background ...... 1 1.2 Purpose of the Deliverable ...... 1 1.3 Proposed Approach ...... 2 1.4 Content of the Deliverable ...... 3
2. Literature analysis and background ...... 4
2.1 An overview of EU transport corridors ...... 4 2.1.1 Comparison of the Corridors and Results ...... 4
2.2 Transport mission profiles and CO2 emissions of HDVs in EU ...... 7 2.2.1 Heavy Duty EU Emission Legislation / Regulations ...... 7 2.2.2 Analysis of transport mission profiles in Europe ...... 8 2.2.3 Fuel efficiency improvement technologies for HDVs ...... 10 2.3 Relevant projects ...... 17 2.3.1 SmartDrive ...... 17 2.3.2 ECOWILL ...... 18 2.3.3 CORE ...... 18 2.3.4 eCoMove ...... 18 2.3.5 TRANSFORMER ...... 19
3. Key elements of representative transport missions ...... 21
3.1 Definition of transport mission parameters ...... 21 3.2 Demand analysis perspective ...... 23 3.3 Transport mission perspective ...... 35 3.3.1 Codognotto fleet management data ...... 35 3.3.2 Eliadis fleet management and CAN-Bus data ...... 39 3.4 Environmental perspective ...... 43 3.5 Focus on Turkey ...... 45 3.6 Summary of the results ...... 48
4. Key requirements and criteria from users’ perspective ...... 50 4.1 Users Workshops ...... 50
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page iii
4.1.1 Workshop organized by Codognotto ...... 50 4.1.2 Workshop organized by Eliadis ...... 52
5. Use cases definition ...... 58
5.1 Use case specification ...... 58 5.1.1 Matching use cases with Innovation Elements ...... 59 5.2 Transport Missions and Scenarios definition ...... 60 5.3 Use case definition ...... 61 5.4 Matching Use Cases with Innovation Elements ...... 84
6. Conclusions ...... 87
7. References ...... 90
Annex 1: Details of representative transport missions of Eliadis ...... 94
Annex 2: Codognotto Users’ survey details ...... 112
Annex 3: ELIADIS Users’ survey details ...... 119
Annex 4: TEN-T CORRIDORS ...... 123
1. TEN-T Network ...... 123 2. Corridors ...... 124
Annex 5: Heavy Duty EU Emission Legislation/Regulations ...... 165
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page iv
Index of figures
Figure 1: Results related to Deliverable D2.1 ...... 2 Figure 2: TENtec roads compulsory parameters (Source: European Commission) ...... 6 Figure 3: Limits and Test Cycles for HD vehicle ...... 7 Figure 4: Mission profiles of the EU truck fleet in 2010...... 8 Figure 5: Share of different mission profiles in total EU HDV CO2 emissions ...... 9 Figure 6: Total operating costs of a 40-tonne tractor-semitrailer combination ...... 10 Figure 7: EU28 Road transport emissions 1990 - 2014 ...... 12 Figure 8: Estimated CO2 emissions by type of road vehicle ...... 13 Figure 9: Infographic: VECTO – computer simulation tool ...... 14 Figure 10: Scheme of the HDV CO2 simulator ...... 14 Figure 11: VECTO architecture ...... 16 Figure 12: Fuel consumption over sub-cycle ...... 16 Figure 13: Fuel-waste distribution of driving manoeuvres ...... 17 Figure 14: Motorway density by NUTS2 Region, 2014 ...... 25 Figure 15: Regions with the most significant motorway expansion between 2005 and 2014 (in kilometers) ...... 26 Figure 16: EU 28, Millions of Tkm carried by road ...... 26 Figure 17: 2014, Share on EU28 total of TKM transported by road ...... 27 Figure 18: EU28, Number of lorries ...... 28 Figure 19: Share on EU number of lorries ...... 29 Figure 20: optiTruck and EU TEN-T Core Network Corridors ...... 30 Figure 21: Motorisation in EU Mediterranean Corridor, Heavy vehicles/1000 inhabitants ... 30 Figure 22: Road traffic loads for market sections of ScanMed Corridor in 2030 ...... 31 Figure 23: Equipment rate for utility vehicles (lorries, road tractors and special vehicles) by NUTS2 Regions, 2014 – number of road freight vehicles per 1.000 inhabitants ...... 33 Figure 24: Road freight transport within the EU-28 according to region of loading/unloading by NUTS1 regions, 2014 – million Tkm and % ...... 34 Figure 25: Exemplifying international Transport Missions of Codognotto ...... 39
Figure 26: Greenhouse gas emissions (including international aviation and indirect CO2, excluding LULUCF) trend, EU-28, 1990–2014 (1990 = 100) ...... 43 Figure 27: Greenhouse gas emissions by source sector, EU-28, 1990 and 2014 (percentage of total) ...... 44
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page v
Figure 28: GHG Emissions from Transport by mode and country (million tonnes CO2 equivalent, 2012) ...... 44 Figure 29: The 2023 and 2035 Targets for the Turkish Highway Network ...... 46 Figure 30: Growth Projection of Freight Carried Via Roadways in Turkey, 2013-2017 ...... 46 Figure 31: Total Freight carried via Roadways in Turkey, 2007-2012 ...... 47 Figure 32: Greenhouse gas emissions in Turkey (2012), also by vehicle type ...... 47 Figure 33: The six proposed geographical areas with common features in road transport field ...... 48 Figure 34: Main parameters related to road transportation and identified areas, radar chart ...... 49 Figure 35: Cargo type carried by the trucks (percentage of the total cargo carried) ...... 53 Figure 36: Road type used (percentage of total missions) ...... 54 Figure 37: Traffic density in the route (percentage average) ...... 54 Figure 38: Weather conditions during the route ...... 55 Figure 39: Average speed for each type of road ...... 55 Figure 40: Average fuel consumption per 100km (liters) ...... 56 Figure 41: Use Case specification template ...... 59 Figure 42: Use case matching matrix with Innovation Elements ...... 60 Figure 43: UC01 diagram: Create new mission ...... 65 Figure 44: UC02 diagram: On-board data collection ...... 67 Figure 45: UC03 diagram: Cloud system data collection ...... 69 Figure 46: UC04 diagram: Calculation and planning of the best route options ...... 71 Figure 47: UC05 diagram: Detection of deviations from the initial plan and detection of best route related deviations ...... 73 Figure 48: UC06 Diagram: Cloud optimization ...... 75 Figure 49: UC07 diagram: On-board optimization ...... 77 Figure 50: UC08 diagram: Support the driver in real time ...... 79 Figure 51: UC09 diagram: Measure and evaluate transport mission performances in terms of consumption efficiency ...... 81 Figure 52: UC10 diagram: Post-mission data collection for knowledge-base mission enrichment ...... 83 Figure 53: Ten-T Network ...... 123 Figure 54: Baltic-Adriatic Corridor ...... 125
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page vi
Figure 55: Extension of the non-compliant road infrastructure in km and % of the total length ...... 126 Figure 56: Performance and modal share of the Baltic-Adriatic transport modes (millions of tons*km/year) ...... 128 Figure 57: Intensity of road transport (2014, veh/day/lane) ...... 129 Figure 58: Intensity of road transport (vehicles/day) ...... 130 Figure 59: North Sea-Baltic Corridor...... 131 Figure 60: Mediterranean Corridor ...... 136 Figure 61: Orient/East-Med Corridor ...... 140 Figure 62: Scandinavian-Mediterranean Corridor ...... 143 Figure 63: Rhine-Alpine Corridor ...... 146 Figure 64: Atlantic Corridor ...... 150 Figure 65: Atlantic Corridor Road Network ...... 151 Figure 66: North Sea-Mediterranean Corridor ...... 155 Figure 67: Rhine-Danube Corridor ...... 160 Figure 68: Road alignment of the Rhine-Danube Corridor and assigned infrastructure ..... 162 Figure 69: Exhaust emissions of C.I. engines for vehicles > 25 km/h for EURO I and EURO II standards ...... 165 Figure 70: Limit values – ESC, ELR and ETC Tests for EURO III standards ...... 167 Figure 71: Limit values – ESC, ELR and ETC Tests for EURO IV standards ...... 167 Figure 72: Limit values – ESC, ELR and ETC Tests for EURO V standards ...... 167 Figure 73: EURO VI emission limits ...... 168 Figure 74: Test cycles EURO I and II/ ECE R49 OR 13 MODE CYCLE ...... 169 Figure 75: Test Cycles EURO III and later ...... 169 Figure 76: Sequence of the European Transient Cycle Test - ETC ...... 170 Figure 77: Sequence of ELR Test ...... 171 Figure 78: Sequence of World Heavy Duty Transient Cycle – WHTC second by second sequence of normalized speed and torque values ...... 171 Figure 79: World Heavy Duty Steady-State Cycle ...... 172 Figure 80: Off Cycle Emissions (OCE) ...... 173
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page vii
Index of tables
Table 1: CO2 reduction potential of main fuel-saving categories in Europe from 2015-2020 11 Table 2: A combination of different conditions in TRANSFORMER ...... 19 Table 3: Criteria and parameters for long haul cycle ...... 20 Table 4: Turnover of Road Freight Transport sector, 2012 ...... 23 Table 5: Growth of Road transport on OEM corridor (2010-2030) in % ...... 32 Table 6: 2016 Share of TEN-T Corridors on total EU FTL transport missions, number of voyages (forecasted) ...... 35 Table 7: Max lengths of national and international transport missions of road operator .... 36 Table 8: Parameters analyzed per route ...... 40 Table 9: Participants of the workshop organized by Codognotto ...... 51 Table 10: Participants profile of the Eliadis survey ...... 53 Table 11: Profile of transport mission of trucks >40tn ...... 56 Table 12: Definition of the Actors involved in the Use Cases ...... 62 Table 13: Matching use cases with Innovation Elements ...... 85 Table 14: International Road Freight Flows in 1,000 tonnes ...... 127 Table 15: Modal split of corridor-related international freight transport flows by country in 2010 ...... 132 Table 16: Freight transported on the North-Sea Baltic Corridor in 2012 (x 1000 Tonnes) .. 134 Table 17: Freight transported on the North Sea – Baltic Corridor in 2012 in % ...... 134 Table 18: Total freight demand between corridor countries in 2010 ...... 137 Table 19: Freight flows in the corridor’s market area in 2010 (1000 tons / year) ...... 137 Table 20: Forecasting results for the corridor's market area ...... 138 Table 21: Freight transport volume between the OEM regions for 2010, 2030 reference scenario; in 1,000 tonnes ...... 141 Table 22: International rail freight flows covering ScanMed corridor countries in 2010 .... 145 Table 23: Overview of corridor nodes ...... 147 Table 24: Lengths per mode along the Rhine-Alpine Corridor by country ...... 148 Table 25: Existing international freight transport flows (2010) (in thousand tonnes) ...... 149 Table 26: Model results (billion tonne-kms) ...... 152 Table 27: Model results ...... 152 Table 28: Corridor Traffic Shares of EU27 Volumes ...... 156
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page viii
Glossary of terms
Term Description
Transport mission Defined transport route
Scenario Definition of specific elements that characterize the test of a transport mission
Innovation Element Core element of the optiTruck concept that explain how the project results contribute to fuel savings
Use Case The use case is a set of possible sequences of interactions between systems and users related to a particular goal.
Cloud Optimizer One component of the optiTruck Global Optimizer that supervises the optimization on the cloud
On-board Optimizer One component of the optiTruck Global Optimizer that supervises the optimization on the on-board system
Mission Dashboard Dashboard used to collect transport mission data from the fleet management company
Mission User Interface Interface used to communicate with the driver
Data architecture A component of the cloud system responsible for the storage of data coming from on-board system and from external services
On-board sensors Sensors installed on the equipped optiTruck vehicle
Sensors Fusion Module A module of the optiTruck architecture that includes all the components to fuse the data from the different sensors placed on the truck (e.g., load, cameras, temperature, etc.)
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page ix
Acronyms
Acronym Description
WP Workpackage
D Deliverable
T Task
DoW Description of Work
UC Use case
SoA State of the Art
MUI Mission User Interface
HMI Human Machine Interface
IE Innovation Elements
ETA Estimated Time of Arrival
UML Unified Modelling Language
PaaS Platform-as-a-Service
OBD On Board Diagnostics
ESC European Steady-State Cycle
ETC European Transient Cycle
ELR European Load Response
FTL Full Truck Load
LTL Less than Truck Load
GDP Gross Domestic Product
ADAS Advanced Driver Assistance Systems
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page x
Executive Summary
The present Deliverable contributes to achieve the objectives of WP2, by identifying stakeholder and users needs, and analysing transport missions. These results represent an important input for the identification of the project use cases. This document reports the results produced by the Task 2.1 “Use cases and transport mission”, which has the purpose to find out and analyse transport mission statistics concerning over 40T trucks logistics, fuel consumption related to different routes, selection of the criteria of analysis to identify possible use cases which will give the most impact of project results. To fulfil these purposes, two main outputs are provided in D2.1: analysis of the most frequent transport missions and associated parameters to find out how the selected optiTruck transport missions are representative of the average EU transport missions; definition of the project use cases in a detailed way and in coordinated approach with the project architecture defined in Task 2.2. To reach these outputs many sources have been analysed, direct answers from other transport operators and fleet management companies are collected and a strong collaboration among optiTruck partners took place to reach a shared definition of project use cases. The organisation deliverable’s chapter reflects the approach respected in T2.1 that is organized in the following main phases: (i) analysis of the state-of-the-art and related literature to have a preliminary outlook of the current scenario concerning the situation of typical transport missions in Europe; (ii) definition of key criteria (demand, transport mission and environmental perspective) to deeply analyse statistical data coming from several different sources to finally link the impact of the optiTruck project to the European trends, showing real representative and typical transport missions of Codognotto and Eliadis; (iii) collection of transport mission requirements from users’ perspective by distributing two dedicated surveys prepared by Codognotto and Eliadis; (iv) definition and selection of representative use cases (UCs) for pre-mission, in-mission and post-mission phases accordingly to the architecture definition in D2.2. Ten UCs have been defined (UC01 - Create new transport mission, UC02 - On-board data collection, UC03 - Cloud system data collection, UC04 - Calculation and planning of the best route options, UC05 - Detection of deviations from the initial plan and detection of best route related deviations, UC06 - Cloud optimization, UC07 - On-board optimization, UC08 - Support the driver in real-time, UC09 - Measure and evaluate transport mission performances in terms of consumption efficiency, UC10 – Post-mission data collection for knowledge-based mission enrichment) and each of them is linked to at least one optiTruck Innovation Element (IE) and through a mapping between UCs and IEs these links are shown. Since, as indicated in the DoW, the D2.1 has to cover all the above mentioned aspects and topics a strong involvement of project partners supported the achievement of the presented outputs. In particular, academic partners (OKAN, LEEDS) and FORD OTOSAN contributed in Chapter 2, the transport operators (Codognotto supported by Eliadis and CERTH) contributed in Chapter 3 and 4 and finally in Chapter 5 Codognotto was strongly supported by almost all project partners (ISMB, IAV, FORD OTOSAN, LEEDS, OKAN and CERTH). In order to shorten as much as possible the core content of D2.1, we provide five
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page xi
Annexes collecting specific elements and details (such as example of Eliadis transport missions and the structure of the used surveys) and additional literature analysis (in relation of TEN-T corridors and regulation issues).
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page xii
1. Introduction
1.1 Background To achieve the objectives of WP2 and of the present deliverable stakeholders’ and users’ needs were identified and transport missions were analysed. These results represent an important input to determine the requirements and specifications related to the different stages of the project following the V-Model approach. This approach allows concurrency activities in the design and development phases and it identifies a counterpart in the evaluation and impact assessment phases. Therefore the identification of the key elements of the transport mission and the selection of representative use cases are a fundamental input to this project approach.
1.2 Purpose of the Deliverable The present document reports the results produced by the project Task 2.1 “Use cases and transport mission”, which has the purpose to find out and analyse transport mission statistics concerning over 40T trucks logistics, fuel consumption related to different routes, selection of the criteria of analysis to identify possible use cases which will give the most impact of project results. The main aim of the present document consists in providing two key outputs for the project:
analysing the most frequent transport missions and associated parameters to find out how the selected optiTruck transport missions are representative for the average EU transport missions and verify, once optiTruck will be implemented, the EU areas in which the solution may have the biggest impact; defining the project use cases in a detailed way and in coordinated approach with the project architecture defined in Task 2.2. Deliverable D2.1 will contribute to the analysis and definition of User and System Requirements in Task 2.3 and Task 2.4. The outputs of these two tasks will end the definition of use cases performed and presented in D2.1. As shown in Figure 1, the D2.1 will contribute to Task 2.2, to Task 2.3 and Task 2.4 concerning the identification of stakeholders and the inputs to the exploration of experience of end users, such as drivers' behaviour.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 1
• Exploring experiences of end-users D2.1 • Determina on • Representa ve of stakeholders transport missions T2.3 T2.4 • Use cases
System and Users’ T2.2 Requirements System and architecture defini on
Figure 1: Results related to Deliverable D2.1
1.3 Proposed Approach Real world driving conditions and individual transport mission conditions cannot always comply with the conditions and standards at EU level. Therefore, in order to reliably evaluate the impact of the optiTruck solutions on the reduction of fuel consumption, the general characteristics of typical missions are important to determine and to be taken into account, based on the common practice of different sized transport companies and Trans- European Long Distance Transport Corridors. The present Deliverable D2.1 gives an overview of the current situation concerning the most representative transport missions in Europe. The first step of the proposed approach in D2.1 is to identify the general characteristics of typical missions and the related parameters. Then, D2.1 defines accurately the project scenarios, based on the national and international transport missions of the project, and the use cases. Therefore, the proposed approach to reach the expected results of this Deliverable D2.1 is divided in the following main phases:
Analysis of the background: the analysis of the state-of-the-art and related literature gives a preliminary outlook of the current scenario concerning the situation typical transport missions in Europe. Moreover data and results from other EU project and initiatives will be provided; Identification of criteria: in order to properly analyse statistical data coming from several different sources, the definition of some key criteria is needed. These criteria represent key elements to aggregate the collected data and to link the impact of the optiTruck project to the European trends. The main identified criteria deal with: demand perspective, transport mission perspective and environmental perspective. In addition to these statistical EU data, internal data from Codognotto and Eliadis are used to define some typical transport missions based on selected key parameters.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 2
Collection of transport mission requirements from users’ perspective: in the definition of representative transport mission is essential to take into consideration also the users' perspective. To this purpose, a dedicated survey will be prepared and distributed by Codognotto and Eliadis. Definition and selection of representative use cases: the project use cases will be defined and detailed. These use cases will focus on different phases of the transport mission (pre-mission, in-mission and post-mission) and will be matched to project innovation elements.
1.4 Content of the Deliverable Since the main contribution of the Deliverable consists in analysing the current scenario in Europe on the basis of statistics and to define some reference use cases of the project, the structure of the present document reflects the different phases defined in the approach in Section 1.2. The deliverable is structured as follows: Chapter 2 provides an analysis of the literature and the background. Chapter 3 define the key elements of representative transport missions on the basis of the identification of different criteria related to transport and logistics elements, environmental issues. Chapter 4 focuses on the identification of key requirements from users' perspective using a dedicated survey to be distributed to representative users. This activity has been carried out by Codognotto and Eliadis which contacted several other transport operators and fleet managers. Chapter 5 outlines the use cases which have most impact for the project. These use cases are defined taking into account inputs from Chapter 4 and 5.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 3
2. Literature analysis and background
In this chapter, we present the analysis of the literature conducted by the partners of optiTruck project concerning the review of fuel consumption and CO2 emissions of heavy- duty vehicles (HDVs) in Europe, and vehicular and environmental parameters which influence the statistics related to different routes and transport missions. The review can be seen as supplementary to the detailed analysis of the telemetry-CAN Bus data (e.g. vehicle speed, throttle position, fuel consumption, geographical positions etc.) collected by ELIAD and presented in Chapter 3. The findings derived from the literature review and the results of the data analysis will represent the inputs for the definition of use cases and transport missions to be tested in this project. We are also presenting a brief summary of the main related EU projects and initiatives.
2.1 An overview of EU transport corridors
2.1.1 Comparison of the Corridors and Results First comparison between road traffic in the EU corridors Chapter 3 will realize an in depth analysis of road traffic market in EU, with specific reference to optiTruck transport mission, anyway in this paragraph a first overview is presented. The information about the road traffic in the Ten-T Corridors can be gathered in detail from work plans of the respective European Coordinators published periodically (for further details please see Annex 4). Nevertheless, although mentioned work plans have common outlines, the information presented in the contents may vary greatly. Especially on Transportation Market Study sections, different types of values are presented about the freight transportation on the corridors. Lack of standardization between the work plans results in difficulties on comparing the performance of one corridor with another. However, subjective but realistic assessments can be made about the corridors using the information on the work plans. In this perspective, Mediterranean, Scandinavian-Mediterranean, Baltic- Adriatic and Rhine-Alpine are the busiest corridors in terms of highway freight traffic. The Mediterranean Corridor is the main east-west axis in the TEN-T network south of the Alps. It runs between the south-western Mediterranean region of Spain and the Ukrainian border with Hungary, following the coastlines of Spain and France and crossing the Alps towards the east through Italy, Slovenia and Croatia and continuing through Hungary up to its eastern border with Ukraine. The Scandinavian-Mediterranean Corridor is a crucial north-south axis for the European economy. Crossing the Baltic Sea from Finland to Sweden and passing through Germany, the Alps and Italy, it links the major urban centres and ports of Scandinavia and Northern Germany to continue to the industrialised high production centres of Southern Germany, Austria and Northern Italy further to the Italian ports and Valletta. The most important projects in this corridor are the fixed Fehmarnbelt crossing and Brenner base tunnel, including their access routes. It extends, across the sea, from Southern Italy and Sicily to Malta.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 4
The Baltic-Adriatic Corridor is one of the most important trans-European road and railway axes. It connects the Baltic with the Adriatic Sea, through industrialized areas between Southern Poland (Upper Silesia), Vienna and Bratislava, the Eastern Alpine region and Northern Italy. It comprises important railway projects such as Semmering base tunnel and Koralm railway in Austria and cross-border sections between Poland, Czech Republic and Slovakia. The Rhine-Alpine Corridor constitutes one of the busiest freight routes of Europe, connecting the North Sea ports of Rotterdam and Antwerp to the Mediterranean basin in Genoa, via Switzerland and some of the major economic centres in the Rhein-Ruhr, the Rhein-Main-Neckar, regions and the agglomeration of Milan in Northern Italy. As mentioned Mediterranean corridor is an important and busy corridor. In the JRC’s report of 20081, which analysed the parameters influencing emissions of heavy duty vehicles just for this corridor (once called “Corridor V”) we can find a detailed analysis of different “use cases” on fuel consumption. To analyse the fuel consumption and emission different transport mission characteristics were considered. The effect of vehicle operational parameters on the fuel consumption have been analysed and the following parameters have been taken into account,
speed driving dynamic different types of vehicle operation (different drivers) optimal – non optimal type of operation characteristic of the infrastructure optimal (flat) type of road road slope urban – non urban roads highway cycle weather conditions transport flows density congestions free flow traffic extreme situations (administrative constrains) toll stations border crossing weight control stations
1 Technical, operational and logistical parameters influencing emissions of heavy duty vehicles, Based on real-world emission measurements of HDV along the extended Trans-European transport CORRIDOR V, M. Pregl, A. Perujo, P. Bonnel, JRC ,2008
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 5
To analyse the influence on the emission on logistics factors such as:
occupancy rate emissions at loading and uploading idle running of the engine (driver resting periods – heating, etc) Finally, is useful to underline that in the Report “Mediterranean Core Network Corridor Study” we can find detailed information on TENtec Database for single corridor characteristics. For roads, reports requires data for fourteen parameters (Figure 2). Information may be useful among others to define the transport mission more in detail. The information also shows that especially in rush hours there is heavy traffic around big cities, and for some cities during certain hours, big trucks cannot circulate in the region of the cities.
Figure 2: TENtec roads compulsory parameters (Source: European Commission)
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 6
2.2 Transport mission profiles and CO2 emissions of HDVs in EU
2.2.1 Heavy Duty EU Emission Legislation / Regulations Emission limits for HD vehicles define the maximum allowed tailpipe emissions where the test cycles define how the engine or vehicle is operating during the measurement. The test procedures and specifications for the test and measurement systems are also defined. Below the limits and the test cycles for HD vehicle are summarized.
Figure 3: Limits and Test Cycles for HD vehicle2
Heavy-duty vehicle emission standards were introduced in Europe in 1988, while the “Euro” track was established in 1992 with increasingly stringent standards implemented every few years. The heavy-duty Euro standards are numbered using Roman numerals (e.g. Euro I, II...V), whereas light-duty standards use Arabic numbers (e.g. Euro 1, 2…5). Many countries have since developed regulations that are aligned in large part with the European standards. Euro I standards were introduced in 1992, followed by the introduction of Euro II regulations in 1996. These standards were applied to both truck engines and urban buses; the urban bus standards, however, were voluntary. Test Cycles used for HD Emission Regulations for Euro I and Euro II standards were ECE R49 or 13 Mode Cycle. With the Directive 1999/96/EC in 1999, the EU introduced Euro III standards in 2000 and Euro IV/V standards (2005/2008). The directive set voluntary emission limits that are slightly more stringent than Euro V standards for “enhanced environmentally friendly vehicles” or EEVs. As Test Cycles for Euro III, Euro IV and Euro V were used European Steady-State Cycle (ESC), European Transient Cycle (ETC) and European Load Response (ELR). In 2001, the European Commission adopted directives which represented important additions to the original standards: Directive 2001/27/EC which prohibits the use of emission “defeat devices” and “irrational” emission control strategies and Directive 2005/55/EC adopted by the EU Parliament in 2005 which introduced durability and on board diagnostics (OBD) requirements, and which re-stated the emission limits for Euro IV and Euro V. However, the results obtained, in terms of level of reduction of emission of the smallest and most hazardous particles after introducing the Euro IV and Euro V standards, did not meet the results expected by the EU Commission and in July 2009 was published
2 Source: AVL – Emission: Heavy Duty and Off-Road Emission Test Systems
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 7
new Regulation No 595/2009 which introduced the Euro VI standards, which became effective in 2013 for new type approvals and for all registrations in 2014. The Euro VI regulation introduces new emission limits in terms of ammonia concentration, NO2 component and new test cycles such as World Harmonised Test Cycle (WHTC), World Harmonised Steady-State Test Cycle (WHSC) and OFF Cycle Emissions (OCE). A more detailed description of all directives and regulations as well as the Test Cycles regarding the Euro standards, can be found in Annex 4 of the present deliverable.
2.2.2 Analysis of transport mission profiles in Europe Transport mission profiles and CO2 emissions of HDVs in EU The EU currently has the largest in-use passenger car fleet and the second largest commercial vehicle fleet in the world3. The recent ACEA automobile industry pocket guides show that there has been a steady increasing trend in the registration of new commercial vehicles (trucks) over 3.5t in the EU at 6.5% from 2012 to 2013, 3.2% (2013-2014) and 3.7% (2014-2015), and the number of registered trucks reached 325.689 in 2015. These numbers seem to confirm that “CO2 emissions from HDVs are expected to remain stable over the long term at around 35% above their 1990 level”4 as the growth in HDV transport and the improved fuel efficiency are counterbalanced each other. Most vehicles in the HDV fleet are used for long haul or service deliveries, followed by regional deliveries and construction purposes. Only a small part of the HDV fleet is used for urban deliveries, utilities, buses, and coaches. The mission profiles of the whole EU truck fleet (i.e. both rigid trucks and road tractors) are depicted in Figure 4.
Figure 4: Mission profiles of the EU truck fleet in 20105
3 "The Automobile Industry Pocket Guide", ACEA, 2016-2017. 4 http://europa.eu/rapid/press-release_MEMO-14-366_en.htm 5 Source: AEA and Ricardo
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 8
The distribution is based on vehicle numbers, not on vehicle ton or vehicle kilometres. In these latter cases, the shares of those mission profiles that have lower annual driving cycles (e.g. urban, regional) would become even smaller, while the share of long haul would increase significantly).
6 The study carried out by AEA and Ricardo shows total CO2 emissions from EU HDVs equal 241 Mt., with trucks responsible for 86%. Figure 5 shows that the main share of HDV emissions are caused by long haul transport, followed by regional, service, and construction. The large share for long haul is the result of the high share in the fleet (see Figure 4). They conclude that the relatively high CO2 emissions per vehicle, were most likely caused by a relatively high annual distance per vehicle.
Figure 5: Share of different mission profiles in total EU HDV CO2 emissions7
6 AEA and Ricardo (2011), “Reduction and Testing of Greenhouse Gas (GHG) Emissions from Heavy Duty Vehicles”, Lot 1: Strategy. 7 Source: AEA and Ricardo
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 9
2.2.3 Fuel efficiency improvement technologies for HDVs Trends in fuel efficiency and fuel-saving technologies As the latest ACEA position paper (2016) points out, fuel efficiency is one of the most important competitive factors in developing and selling trucks and market forces ensure continuous progress in improving fuel economy and further reducing CO2 emissions in the most efficient way8. Figure 6 shows that fuel represents 30% of the running costs of a 40-tonne tractor- semitrailer combination, and costs more than employing drivers. The business case for fuel efficiency is clear. Market forces will prompt strong economic incentives and encourage further improvement in fuel efficiency.
Figure 6: Total operating costs of a 40-tonne tractor-semitrailer combination9
10 TIAX (2011) carried out a well-known study of CO2 reduction potential of 8 HDV duty cycles (or transport mission profiles) in Europe and for 7 main fuel-saving categories as summarised in Table 1. As can be seen, the potential combined fuel consumption benefit of all technologies in the Long Haul segment is 47%, followed up by urban delivery and collection (46%) and by regional delivery and collection (41%) which are expected to be part of the transport missions to be piloted in this project.
8 ACEA Position Paper (2016), "Reducing CO2 Emissions from Heavy-Duty Vehicles", JANUARY 2016 9 ACEA Commercial Vehicles and CO2, 2010 10 TIAX (2011), "European Union Greenhouse Gas Reduction Potential for Heavy-Duty Vehicles"
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 10
Table 1: CO2 reduction potential of main fuel-saving categories in Europe from 2015-202011
From the measures that can be applied to conventional powertrains, improving engine efficiency has the largest overall potential to reduce emissions (10.7-16.25%), except in the service/delivery segment (4.5%). The fuel-saving benefits from aerodynamics are significant for long haul, urban and regional vehicles (7-8%). It should be noted that although the optiTruck innovation elements (IEs) don’t consider the reduction potential of two categories in the table (i.e. rolling resistance and aerodynamics), the optiTruck on-board optimiser which integrates enhanced gear shifting and engine control strategies monitors and calibrates powertrain control in response to the changes in road, traffic and weather conditions during the transport mission to ensure that predetermined velocity profile is applied effectively. It is expected that the effect of the velocity depending air drag on the driving resistance might be reduced by the cloud optimizer due to a changed velocity profile, a reduced average velocity or by avoiding routes with predicted heavy head or cross wind. HD vehicles fuel consumption – CO2 emission regulation approach in the future
Heavy Duty vehicle CO2 emissions represent about one quarter of road transport emissions for CO2. This share is expected to increase in the coming years. Considering their absolute size and relative share in total road transport and overall GHG emissions, they need to be addressed and curbed.
11 Source: TIAX, “European Union Greenhouse Gas Reduction Potential for Heavy-Duty Vehicles”
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 11
The regulations from 2009 and 2011 in Europe already set out mandatory CO2 emission standards for the new passenger car and light commercial vehicle fleets in Europe. However it did not consider identical CO2 emission rules for HDVs as for those introduced for light duty vehicles. This lack of monitoring mechanism is responsible for the lack of knowledge on exact HDV CO2 emissions which in turn made effective policy making more difficult. The European commission for Post 2020 strategy for trucks and buses has decided to take action for:
Certification, monitoring and reporting of CO2 emissions and fuel consumption
Additional measures to actively curb CO2 emissions Other parts of the world, such as the US, China, Japan and Canada, have already introduced standards Lower running costs for transport of goods, more fuel efficient vehicles will benefit the entire economy and ultimately, the consumers and passengers.
Figure 7: EU28 Road transport emissions 1990 - 201412
12 Source: GHG Emission Inventory Date 2016
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 12
Figure 8: Estimated CO2 emissions by type of road vehicle13
VECTO (Vehicle Energy Consumption Calculation Tool) The European Union commission has established a strategy for Heavy Duty Vehicles fuel consumption and emissions which was issued by the commission on 21.05.2014. In order to address the lack of consistent monitoring framework for HDV fuel consumption and CO2 emissions, the Commission has launched an on-going program to establish a simulation tool customized to calculating HDV fuel consumption and CO2 emissions. The first version of the Vehicle Energy Consumption calculation Tool (henceforward VECTO), was developed by the Graz University of Technology and the Joint Research Centre of the European Commission
(JRC) in order to serve as a reference platform on which an HDV CO2 emissions monitoring methodology will be developed and tested.
To determine whole vehicle HDV CO2 emissions, i.e including emissions due to vehicle’s motor, transmission, aerodynamics, rolling resistance, and auxiliaries. VECTO is intended to be a methodology geared to estimate whole vehicle, including trailer, HDV CO2 emissions. VECTO is intended to be operational for at least 3 HDV categories, representing more than
50% of HDV CO2 emissions. In its analysis, the Commission will make full use of all available data, including the simulation tool VECTO to be developed in close collaboration with stakeholders. The development of VECTO is still on-going and targets as possible first reporting year 2018/2019 time frame.
13 Source: Ricardo - AEA
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 13
Figure 9: Infographic: VECTO – computer simulation tool14
VECTO simulates CO2 emissions and fuel consumption based on vehicle longitudinal dynamics using a driver model for simulation of target speed cycles. The required load to be delivered by the internal combustion engine is calculated in 1Hz based on the driving resistances, the power losses in the drivetrain system and the power consumption of the vehicle auxiliary units. Engine speed is determined based on a gear shift model, the gear ratios and the wheel diameter. Fuel consumption and CO2 emissions are then interpolated from an engine fuel/CO2 map.
Figure 10: Scheme of the HDV CO2 simulator15
14 Source: ACEA
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 14
HDVs are more complicated than LDVs. Since they have:
Low, medium, high, long, short cab, etc. 2,3,4,5,6 axles, 4x2, 4x4, 6x2, 6x4, 6x6, etc. Different tires for each axle, single/twin tires, etc. Same engine but different gear boxes/axles, etc. Rigid, semi-trailer, tractor, coach, bus, citybus, etc. Any combination mentioned above. VECTO targets to include all these parameters in the simulation results. For the following components, relevant input data for VECTO have to be delivered from standardised test procedures:
Vehicle mass Tires (dimensions and rolling resistance coefficient) Engine (engine fuel flow map) Transmission (transmission ratios, loss maps for gear box and axle, default values optional) Aerodynamic drag (Cd x A, for some vehicle classes generic values can be used) For the following components generic values are defined, which are allocated by the software VECTO to the vehicle depending on the vehicle class and mission profile:
Auxiliaries (alternator, air compressor, alternator, steering pump, cooling fan, Heating Ventilation AC-HVAC) Mass of the standard bodies and trailers Vehicle payload (truck) or passengers weight (bus) Test cycle. VECTO offers a declaration mode, where all generic data and the test cycle are allocated automatically as soon as the vehicle class is defined. In the declaration mode of VECTO, fuel consumption and CO2 emissions are automatically calculated for all CO2 test cycles allocated to the vehicle for average payload, full load and empty driving. Results are given in g/km and g/ton-km or g/pass-km. An engineering mode is also offered, where the user can select and change all input data to allow recalculation of test data, e.g., for model validation.
15 Source: VECTO Project
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 15
Figure 11: VECTO architecture16
Figure 12: Fuel consumption over sub-cycle17
Initial versions of VECTO simulation tool has shown very good accuracy when compared to commercial simulation tools.
16 Source: VECTO Project 17 Source: VECTO Project
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 16
2.3 Relevant projects In this sections, we analyse some innovative solutions developed in previous and on-going projects relevant to the optiTruck project. These developments can be taken into account in the design of use cases in Task 2.1. During the course of the project, new innovation elements may also be considered and new use cases may then be derived accordingly. In the total Road transport carbon emissions in Europe, more than 30 % of emissions is related to heavy duty road transport. For this reason, there are many EU projects aiming to improve the efficiency of heavy duty trucks. In the following sections, a brief overview of the main projects related to truck efficiency, transport missions and use case determination is presented.
2.3.1 SmartDrive The study of truck fleet fuel efficiency carried out by SmartDrive in 2013 took a fresh look at the impact of eco-driving techniques on fuel consumption in work trucks. Using an enhanced methodology, it evaluated 1,795 Work Truck vehicles and drivers in a broad range of operating environments to assess fuel use and determine the effect of fuel-efficiency training combined with in-vehicle, instant driver feedback, driver fuel and site fuel scorecards on improving fuel economy18. The findings show that 57% of fuel waste involves hard acceleration, braking and turning, and over speeding as indicated in Figure 13.
Figure 13: Fuel-waste distribution of driving manoeuvres19
Furthermore, the study found that by following eco-driving best practices, drivers could improve fuel economy up to 30%, higher than 22% as estimated in their 2011 Commercial Transportation Fleet Fuel Efficiency Study20.
18 SmartDrive Fuel Efficiency Study: Work Truck Fleets, 2013 19 Source: SmartDrive Fuel Efficiency Study 20 SmartDrive Trucking Fuel Study, 2011
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 17
Idling is defined as time spent with the engine running while no movement was recorded for greater than five minutes and can result in as much as 43% of fuel consumption. The waste can be reduced by turning off the engine while parked and more importantly avoiding excessive traffic congestion during the mission. In optiTruck, the latter can be done by using predicted traffic and environmental conditions produced by the cloud-based optimiser.
2.3.2 ECOWILL21 The ECOWILL project indicates that “eco-driving easily saves 10-15% fuel reduction on average and possible cost savings correspond to up to 300 Euros per year”. Although the project is mainly concerned with cars, the recommended golden rules for improving fuel efficiency can also be applicable to HDVs including:
Anticipate traffic flow: Read the road as far ahead as possible and anticipate the flow of traffic; Maintain a steady speed at low RPM: Drive smoothly, using the highest possible gear at low RPM; Shift up early: Shift to higher gear at approximately 2,000 RPM Consider traffic situation, safety needs and vehicles specifics. These fuel-saving strategies can be easily implemented by the optiTruck system as it has the ability of using real-time conditions about the traffic and road environment to help the driver anticipate traffic flow and surrounding vehicles.
2.3.3 CORE
The CORE Project aims to demonstrate a substantial reduction of CO2 emissions through improved powertrain efficiency for long haul applications, with technologies having the potential to be implemented in production around 2020. To demonstrate the results of the Project they use ETC (the European Transient Cycle) and WHTC (the World Harmonized Transient Cycle) which are cycles accepted by EU for heavy duty truck emission legislation requirements.
2.3.4 eCoMove22 The eCoMove concept is that of the “perfect eco-driver” travelling through the perfectly “eco-managed” road network, i.e. a combination of cooperative applications for eco-driving and eco-traffic management can – for any given trip by a particular driver in a particular vehicle – help to approach the theoretical least possible fuel consumption (and thus CO2 emissions). All without compromising the quality of people’s and goods mobility. eCoMove conducts verification and validation through identified problem areas and designing “use cases” for such areas. Then simulation system is used for verification of the results and final validation is done on selected real-world areas which can give similar situation as determined on the use cases. The eCoMove applications can be categorised into three
21 “Implementing eco-driving on a wide scale”, Road Safety Day 2012 Cyprus ,Gabriel Simcic 22 http://www.ecomove-project.eu/
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 18
phases, namely “Pre-trip”, On-trip” and “Post-trip”, as listed below, which are similar to the approach adapted in the optiTruck project:
eco-pre-Trip Planning advising optimal departure time and greenest route, in combination with energy-relevant information about vehicle functions, for least impact journey; ecoSmartDriving “virtual coach” providing dynamic green driving and routing guidance as well as on trip tips to tune vehicle functions for minimum fuel use, but also ecoPostTrip personalised recommendations based on driving record for eco- driving optimisation; ecoMonitoring information derived from vehicles' post trip eco record is distributed in a fully anonymous way to the traffic control centre, to identify energy blackspots; Dynamic ecoDriver Coaching for commercial vehicle drivers including training and incentive scheme; ecoTour Planning for logistics companies to define eco-efficient tours considering drivers’ eco-performance, vehicle payload and road infrastructure status; Truck ecoNavigation calculating the most fuel efficient route based on truck-specific attributes and traffic state information; ecoAdaptive Balancing & Control strategies for energy-optimised traffic distribution at network and local levels, e.g. traffic signal optimisation (green waves); ecoAdaptive Traveller Support to drivers by sending information on traffic state, route recommendations and speed profile data needed by on-board assistance systems; ecoMotorway Management measures for energy-optimised flow management on the interurban network coupled with ramp metering and merging assistance at individual vehicle level.
2.3.5 TRANSFORMER23 TRANSFORMER aims to develop a configurable and adaptable truck to reduce transport energy-use/tonne-km by 25% in the direction of EU Objective of achieving 60% reduction on carbon emission in year 2050. Two test cases were performed in the TRANSFORMER project. One of the cases was carried out by Volvo on a public road between Göteborg to Ödeshög in Sweden. It was a 420km round trip and ran the demonstrator along with the reference truck. Due to the paucity of time and resources, the project carried out a limited number of demonstrations but developed simulations to ‘bridge’ the gap. A combination of different conditions were tested in the simulations as given in Table 2.
Table 2: A combination of different conditions in TRANSFORMER24
23 http://www.transformers-project.eu/ 24 Source: TRANSFORMER Project
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 19
When testing the combination, TRANSFORMER defined 19 parameters in order to gain a good understanding of the conditions under which each the vehicle is tested.
Table 3: Criteria and parameters for long haul cycle
The optiTruck partners were aware of the limited number of demonstration trials and limited choices of routes, and thus proposed a simple but plausible interpolation/extrapolation model which is calibrated to the trials’ conditions and results, and used to calculate the impacts of the optiTruck system for a variety of traffic and weather conditions, topologies and route choices, vehicle mass and types, and as well as optimisation settings. This model will also be used to simulate future scenarios (e.g. fully automatic powertrain calibration/ optimisation with real-time environment conditions, traffic data and load factors) so that the long-term impact with new trucks can be estimated.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 20
3. Key elements of representative transport missions
The goal of this chapter consists in giving an overview of the most frequent transport missions and associated parameters, of the EU areas in which optiTruck solution may have the biggest impact and importance in terms of representativeness of the Regions covered by the optiTruck use cases. To perform the analysis we considered the following data sources:
• Eurostat - archive “Road Transport” (road) and statistics explained data; • EC: mostly the final reports studies realized for each one of the 9 TEN-T corridors (provided by EU transport corridors coordinators); both Eurostat and EC data were precious for the demand analysis (see later); • Study Fraunhofer SCS, especially for the methodology used; • Codognotto internal data, which provided concrete and real-world examples of transport missions; • ELIAD internal data.
3.1 Definition of transport mission parameters The EU’s objective is to create the conditions whereby the road transport sector can operate efficiently, safely and with a minimum impact on our environment. Today, the road transport sector in the EU is facing a number of challenges. Drivers are confronted with ever more congested roads while one out of four heavy duty vehicles still runs empty. At the same time, fuel prices keep on rising, as does the need to reduce air pollution and noise and the carbon footprint to which road transport contributes (Oberhausen, 2003). Congestion is not just a nuisance for road users; it also results in an enormous waste of fuel and productivity. Many manufacturing processes depend on just-in-time deliveries and free flow transport for efficient production. Congestion costs the EU economy more than 1% of GDP – in other words, more than the EU budget. To reduce it, the EU needs more efficient transport and logistics, better infrastructure and the ability to optimise capacity use. Last but not least, Europe needs transport which is cleaner and less dependent on oil, whose price seems set to remain high in the medium to long run. Moving towards low-carbon and more energy efficient transport will depend on new technologies like hybrid and electrical motors, as well as developing more efficient urban and intermodal transport solutions as alternatives to road haulage (Oberhausen, 2003). In this context, optiTruck solution aims at reducing the fuel consumption and therefore the general characteristics of typical missions are important to determine and to be taken into account, based on the common practice of different sized transport companies and Trans- European Long Distance Transport Corridors. The analysis presented in this section aims at giving an overview of the most frequent transport missions and associated parameters of the EU areas in which optiTruck solution may have the biggest impact and importance in terms of representativeness of the Regions
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 21
covered by the optiTruck use cases and this issue is addressed from three different point of view:
1. Demand analysis perspective: to perform this analysis we have to answer to the following main questions: When optiTruck will be implemented, what are the EU areas in which the solution may have the biggest impact? Are the regions selected for our use cases representative in terms of market and demand? The data to be analysed mainly refer to parameters related to:
Quantity of transport: t/km transported by road in the regions involved in the corridors per year; number of vehicles circulating per year and type;
Socio-economic values: number of road haulers companies in the areas considered; turnover of road haulers companies in the regions involved; motorisation rate;
Road Infrastructures: short representation of the road network composition in the EU regions.
2. Transport mission perspective: in this case the considered data is independent from the routes and the regions and to perform this analysis we have to answer to the following main question: Are the selected routes representative in terms of average EU transport mission? The data to be analysed mainly refer to parameters related to:
average distance of the missions (long distance mission, short distance); duration and/or average speed; number of delivery stops during the mission; Road split during the mission (e.g. motorway, national road, urban, hilly road). 3. Environmental perspective: in this case the considered data are independent from the routes and the regions and to perform this analysis we have to answer to the following main question: What is the environmental situation in EU regions now with regard to road transport?
In the future, what can we expect from optiTruck in terms of impact on CO2 global emissions, basing on current situation? The data to be analysed mainly refer to parameters related to:
CO2 emissions in the regions involved in the EU corridors.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 22
3.2 Demand analysis perspective In this context, the aim of the analysis of the available data at EU level is related to the following issues: How much freight is being moved? The amount of freight movement can be a good barometer of the level of economic activity. Traditionally, there has been a close correlation between freight tonne-kms and Gross Domestic Product (GDP), though the ratio of these variables can decline as an economy develops and services increase their share of total output. Knowing how much freight is being moved also indicates the related transport demands for infrastructural capacity, fuel, labour and vehicles. How much road traffic is generated by the movement of freight? Road is by far the dominant mode of freight transport in all European countries and trucks represent a significant proportion of total traffic on the road network. There is therefore a particular interest in understanding the dynamics of road freight traffic. Data on truck-kms are required to analyse the relationship between the total volume of freight movement and the amount of vehicle traffic required to carry it and the contribution that this traffic makes to congestion and environmental degradation. Before starting the analysis of the demand for road transport from a quantitative point of view, it is useful to take a quick look at the economic values and infrastructure parameters related to this mode of transport.
Table 4: Turnover of Road Freight Transport sector, 201225
25 European Commission, EU Transport in figures, Statistical pocketbook, 2015.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 23
Turnover (absolute) Turnover*inhab. Country million € €*inhab. ranking Italy 44.310 € 746 11 France 42.997 € 659 14 Germany 37.905 € 472 18 Spain 31.726 € 678 13 United Kingdom 29.031 € 457 19 Poland 19.893 € 523 16 Netherlands 19.657 € 1.175 3 Belgium 11.624 € 1.048 6 Sweden 11.373 € 1.199 2 Austria 9.493 € 1.129 4 Czech Republic 7.797 € 742 12 Finland 5.935 € 1.099 5 Denmark 5.742 € 1.029 7 Romania 5.451 € 271 25 Portugal 4.709 € 447 22 Hungary 4.469 € 450 21 Lithuania 3.028 € 1.008 8 Bulgaria 2.672 € 365 23 Greece 2.452 € 221 26 Slovakia 2.435 € 451 20 Ireland 2.337 € 510 17 Slovenia 2.059 € 1.002 9 Latvia 1.333 € 652 15 Luxembourg 1.210 € 2.305 1 Croatia 1.164 € 272 24 Estonia 1.100 € 830 10 Cyprus 148 € 172 28 Malta 74 € 177 27
Table 4 shows the turnover of the road transport sector for each EU28 country. Countries are ordered by turnover, and on the right also the values in € per inhabitant are reported. In absolute way, the predominant position of a country as Italy is mitigated in per capita ratio and it is situated at the eleventh place; in this special ranking the nations that excel are mainly those in Northern Europe as well as those with a strong tradition of logistics industry (e.g. Belgium, Netherlands). Moving on to the side of infrastructures, as an indicator of the capillarity of the road network, we can use the motorway density of which the Figure 14 provides a recent image, despite partial.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 24
Figure 14: Motorway density by NUTS2 Region, 201426
This image needs to be integrated with the Figure 15. Certainly, at the moment the areas with the highest density are those related to historically high-production zones such as Central Europe, but there are clear signs of growth on one hand in Spain and on the other in countries of Eastern Europe such as Poland and Bulgaria.
26 Source: Eurostat
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 25
Figure 15: Regions with the most significant motorway expansion between 2005 and 2014 (in kilometers)27
To begin the quantitative analysis, it is primarily useful to check a data28 the most possible broad and overall.
2.000.000
1.800.000
1.600.000
1.400.000
1.200.000
M
K
T
s
n 1.000.000
o i
l Western E.
l i
M 800.000 Eastern E.
600.000
400.000
200.000
0 2008 2009 2010 2011 2012 2013 2014 Year
Figure 16: EU 28, Millions of Tkm carried by road29
27 Source: Eurostat
28 Analyzes of this paragraph have a European point of view, therefore the Turkish segment is not considered. However, an overview of Turkish road sector is shown in paragraph 3.6. 29 Source: ELEVANTE from Eurostat Data
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 26
As shown in Figure 16, EU28 road transport has undergone a considerable quantitative reduction following the economic crisis of 2008. Taking as a measure the total tonne kilometres carried by road30, the value for 2008, close to 1.9 million MTKM, the following year was reduced by 200.000 million, to remain settled on these values for all subsequent years without any particular signal of changing trend; albeit for the last available two years the glimpse of a growth was detected, this is due to the fact that in 2012 the value has reached its minimum point. Analysing internal differences in individual countries, we see a clear trend: countries of Western Europe are characterized by negative or fluctuating trend while those in Eastern Europe offset this trend with opposite numbers, in particular Bulgaria and Poland are showing constantly increasing trends31. The graph of Figure 17 below analyses for 2014 value (1.725.240 million of Tkm) the share of individual nations on total.
Germany ; 18% Others; 24%
Czech Republic; 3% Poland; 15%
Netherlands; 4%
Italy; 7%
Spain; 11%
United Kingdom; 8% France; 10%
Figure 17: 2014, Share on EU28 total of TKM transported by road32
The graph confirms what we said previously: although in the first positions there are still Germany, Spain and France (moreover it has to be taken into account the dependence of the analysed data to the dimensional factors, it is quite likely that the first nations are the most extensive in terms of area) in the second position there is Poland with 15%; in 2008, despite an high value, Poland had only a 7% share of the EU28 total. Following countries are
30 Data comprehends all kinds of road transport, carried on in modality own account and hire or reward and national and international transport. 31 Eastern Europe: Bulgaria, Czech Republic, Estonia, Greece, Croatia, Cyprus, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, Slovakia. Western Europe: Belgium, Denmark, Germany, Ireland, Spain, France, Italy, Luxembourg, Malta, Netherlands, Austria, Portugal, Finland, Sweden, United Kingdom. 32 Source: ELEVANTE from Eurostat Data
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 27
the United Kingdom and Italy. Italy's data is of 7% and it is not negligible: the data is of interest because the International Transport Mission of the project presents the most of the length of its transport mission just in Italy. Another significant participant is Czech Republic which takes a share of the 3%. Already this first introductory analysis shows us a fairly clear situation: on one hand the Western Europe’s countries, despite the downsizing, still see relevant flows but, on the other hand, it should be noted above all the Eastern Europe’s countries’ very fast growth in this specific sector. Trying to go more into detail of the features of each country, it may be useful to analyse the overall vehicle fleet, starting again from the analysis of the EU28 trend.
35.000.000
30.000.000
25.000.000
20.000.000
s
e
i
r r
o Western E. L 15.000.000 Eastern E.
10.000.000
5.000.000
0 2008 2009 2010 2011 2012 2013 Year
Figure 18: EU28, Number of lorries33
Analysing the number of lorries34, we see that the trend is opposite to one concerning Tkm. The reason for this can be found in several causes (e.g. number of lorries in western areas with long tradition in road transport reflects slower growth in recent years, but the companies from those areas are still managing lorries in other regions), still, it is noticeable that even in this case the nations of Eastern Europe – such as Poland, Bulgaria, Romania and
33 Source: ELEVANTE from Eurostat Data 34 Eurostat defines Lorry/truck “rigid road motor vehicle designed, exclusively or primarily, to carry goods”. (Eurostat, Illustrated Glossary for Transport Statistics, 4th edition, 2009). Although this kind of mean is not the only one possible - in these statistics are not included articulated vehicles - it was chosen to approximate the value of the total fleet of vehicles, since this type of data is the most complete in terms of years and countries available.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 28
Slovakia are giving a decisive contribution to the growth, although it should not be overlooked the growth of France. Analysing the share of individual countries, from the data for 2013, which is the last available year, at a first sight it emerges that France and Spain still hold the biggest shares of the European fleet, followed by Italy, which confirms to be a country where road transport is of considerable importance, as seen previously through economic parameters. Again it should be noted the share of Poland, while in relation to the size also share of Greece (4%) appears significant and this figure is of great interest in relation to the International Transport Mission.
Others France 18% 20%
Netherlands 3% Greece 4%
Spain 15% Germany 8%
Poland 9% Italy 12% United Kingdom 11% Figure 19: Share on EU number of lorries35
As mentioned before, the transport missions of optiTruck are focused on three European corridors (Orient/East-Med, Scandinavian-Mediterranean, Mediterranean), as shown in Figure 20. In order to understand whether the selected regions are representative in terms of market demand it is very useful to analyse the elaborations provided in the Final Reports of the EU Transport Corridors36. These reports are highly important in order to analyse the current situation as well as to forecast future scenarios (usually 2030).
35 Source: ELEVANTE from Eurostat Data 36 European Commission, Mediterranean Core Network Corridor Study, Final Report, December 2014; European Commission, Orient/East-Med Core Network Corridor Study, Final Report, December 2014; European Commission, Scandinavian Mediterranean Core Network Corridor Study, Final Report, December 2014.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 29
Figure 20: optiTruck and EU TEN-T Core Network Corridors37
The last segment of the Transport Mission (Verona - Torino) is grafted on the Mediterranean Corridor and as shown in Figure 21, showing the motorization of individual NUTS3 of the corridor, though with not very recent data. This area can be considered baricentric between the relevant and structural presence of the motorization in Western countries (Spain, France) and the growing demand for road transport of the Eastern countries (in this case Hungary).
Figure 21: Motorisation in EU Mediterranean Corridor, Heavy vehicles/1000 inhabitants38
37 Source: ELEVANTE from EC 38 Source: European Commission
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 30
As mentioned the Reports also provide predictive traffic data and as shown in Figure 22, not only the international Transport Mission of optiTruck is located in the EU corridor (already identified as a busy area) but it also runs through two of the segments that the European Commission considers the most busy in 2030, the segments Bologna/Verona and Ancona/Bologna. The segment Bari/Ancona does not seem to be considered relevant by the European Commission because for most part it is not included in any TEN-T corridor (until the area of Foggia) and because also in the vicinity of Bari flows are not considered intense, even in perspective.
Figure 22: Road traffic loads for market sections of ScanMed Corridor in 203039
Finally Table 5 shows the forecast of growth of road transport for the third corridor in 2030 in which will be carried on the transport mission, the Orient/East-Med, in the segment Thessaloniki/Igoumenitsa. All areas will have growths and Greece is no exception, also to a considerable extent as can be noted. But here we want to underline again the high growth values of countries like Czech Republic, Slovakia, Hungary, Bulgaria and Romania.
39 Source: European Commission
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 31
Table 5: Growth of Road transport on OEM corridor (2010-2030) in %40
Recent Eurostat maps (Figure 23 and Figure 24) even if very general and maximum at NUTS 2 level, help us to summarize all: maps show the rate of motorization and the major areas of loading and unloading goods through road transport, and they also have the advantage of presenting the Turkish situation.
40 Source: European Commission - growth of tonnes carried
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 32
Figure 23: Equipment rate for utility vehicles (lorries, road tractors and special vehicles) by NUTS2 Regions, 2014 – number of road freight vehicles per 1.000 inhabitants41
41 Source: Eurostat
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 33
Figure 24: Road freight transport within the EU-28 according to region of loading/unloading by NUTS1 regions, 2014 – million Tkm and %42
42 Source: Eurostat
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 34
3.3 Transport mission perspective 3.3.1 Codognotto fleet management data How is the freight going? In this paragraph some real data of a road haulage operator (the project partner Codognotto) are reported, related to its most frequent transport missions carried out. This step is extremely important since optiTruck transport missions must necessarily be realistic, in order to be representative tests for the future implementations; only the comparison with real data of a road operator can confirm that. First of all, a general overview of overall data is presented, in order to describe and understand the kind of activity of the operator. Table 6 provides the shares for TEN T corridors of the FTL EU transports carried on, in terms of number of voyages, keeping in mind that FTL transports represents, in number, the 76% of the EU transports of the operator.
Table 6: 2016 Share of TEN-T Corridors on total EU FTL transport missions, number of voyages (forecasted)43
EU FTL Transports 183.818 100%
International Mediterranean TEN-T Corridor 56.973 31%
National Italian (Mostly Mediterranean TEN-T Corridor) 40.034 22%
International Baltic - Adriatic TEN-T Corridor 20.412 11%
International Rhine - Alpine TEN-T Corridor 8.121 4%
International Other 58.278 32%
Those kindly provided data are useful to emphasize that beside the Mediterranean corridor, already mentioned and surely involved in the project transport missions, a certain importance is also performed by Baltic - Adriatic and Rhine - Alpine corridors, and this confirms what we will see in the next paragraph: also (and above all) those regions will be beneficially affected by optiTruck effects. Going into a deeper level of data related to transport missions Table 7 shows the maximum lengths of the transport missions usually carried out by the operator, divided into national and international.
43 Source: Codognotto
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 35
Table 7: Max lengths of national and international transport missions of road operator44
National transport International transport mission mission maximum length of the mission (km) 500 2.000 Spain (Catalonia), Main destinations (countries) Italy Poland, UK Mediterranean EU corridors involved Mediterranean Baltic - Adriatic Rhine - Alpine
We know that the chosen lengths of the missions of the project are based on Eurostat average data. As it is possible to understand by the table, chosen length for the Project’s “Transport mission 2” (road length 2.200 km) is of the same order of magnitude of the maximum ones actually carried out by the operator. Transport mission 2, therefore, starting in Turkey and finishing in Italy, has to be considered like a “limit” and extreme case of a real transport mission. After positioning the business of the operator in general terms, now we go into detail of some international Codognotto transport missions, here shown as examples. Each transport mission is linked to each of the EU corridors seen above. The following figure, Figure 25, shows the results of the analysis represented as tables showing five 2016 voyages carried out by Codognotto. For each trip are reported the date and hour of loading, date and hour of unloading, possible second unloading, total length of the route and, especially, the fuel consumption.
44 Source: Codognotto
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 36
Transport Mission no.1
Ten-T Corridor Baltic-Adriatic Transport mode Full Truck LOADING (date and time) 04/11/16 18:00 UNLOADING 1 (date and time) 08/11/16 07:00 UNLOADING 2 (date and time) - Total lenght (km) 579 Fuel consumption (l) 193 Av. fuel consumption (km/l) 3,0 Transport Mission no.245
Ten-T Corridor Baltic-Adriatic Transport mode Full Truck LOADING (date and time) 08/09/16 11:00 UNLOADING 1 (date and time) 12/09/16 12:00 UNLOADING 2 (date and time) 12/09/16 14:30 Total lenght (km) 766 Fuel consumption (l) 255 Av. fuel consumption (km/l) 3,0
45 Source: Codognotto
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 37
Transport Mission no.3
Ten-T Corridor Mediterranean Transport mode Full Truck LOADING (date and time) 10/09/16 14:00 UNLOADING 1 (date and time) 12/09/16 05:00 UNLOADING 2 (date and time) 12/09/16 15:30 Total lenght (km) 1.550 Fuel consumption (l) 516 Av. fuel consumption (km/l) 3,0
Transport Mission no.4
Ten-T Corridor Rhine - Alpine Transport mode Full Truck LOADING (date and time) 10/02/16 14:00 UNLOADING 1 (date and time) 15/02/16 20:00 UNLOADING 2 (date and time) - Total lenght (km) 1.138 Fuel consumption (l) 340 Av. fuel consumption (km/l) 3,3
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 38
Transport Mission no.5
Ten-T Corridor Rhine - Alpine Transport mode Full Truck LOADING (date and time) 01/02/16 11:30 UNLOADING 1 (date and time) 04/02/16 12:00 UNLOADING 2 (date and time) - Total lenght (km) 1.378 Fuel consumption (l) 459 Av. fuel consumption (km/l) 3,0 Figure 25: Exemplifying international Transport Missions of Codognotto46
Those useful data, besides providing interesting information related to the routes chosen (e.g. currently, as it is possible to see by figures, Switzerland is always bypassed), clearly shows us which is the average fuel consumption. The operator let us know that general average fuel consumption is 3 km/l and the factors with the greatest impact are driving style, the season of the mission, the weight carried and the route chosen.
3.3.2 Eliadis fleet management and CAN-Bus data General information about Eliadis fleet data The fleet consist of 15 Volvo trucks, mainly EURO3 technology, owned by the company. All the trucks are equipped with telematics devices that transmit GPS data to the telematics support company server in Athens and then processed and presented via a dedicated platform to the fleet manager. The raw data are also available to Eliadis S.A. and these files have been used for this analysis. However, instead of the usual GPS based telematics, Eliadis S.A. made a step further (not commonly met in the fleet management) and collects CAN-Bus data in order to evaluate the driver behaviour and to find solution for fuel efficiency and driver improvement. CAN-Bus data are not available for all trucks (only for the recent ones). The data are grouped in hourly sets in order to reduce their size. Data sets seems to be quite complete, however an issue that came up after the data analysis is the random interruption in the data series. There have been gaps (not often) or absence of specific data during a route. This is a problem notified to the telematics provider for a future technical solution. It should be noted that Eliadis S.A. is the only company in Greece that collects truck CAN-Bus data and
46 Source: Codognotto
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 39
thus some data inconsistencies were expected, because of lack of experience in acquiring CAN-Bus data from heavy trucks. Data description The data are distinguished into two groups: the GPS and the CAN-Bus ones. Both files are .txt format files referring to a whole day. As is said before, the CAN-Bus file contains hourly aggregated information, as well as raw data, in addition to the GPS files that contain only raw information. The parameters taken into account for the route analysis are described in the table below.
Table 8: Parameters analyzed per route
Acquisition Parameter Comments system Departure date and time GPS (d-m-y & h-m-s)
Arrival date and time GPS (d-m-y & h-m-s)
Travel duration GPS (h-m-s)
Travelled distance GPS (km) The total time the truck was not GPS Stop duration travelling during the specific route
Min speed GPS (km/h)
Max speed GPS (km/h) (km). The answer of this parameter is an approximation of the Road network usage GPS travelled distance on the urban, rural and highway road network for each route Company (tn). The information comes Cargo weight data through the Eliadis archive (lt). The fuel consumption per Fuel consumption CAN-Bus 100km has also been calculated (#). Aggressive acceleration has been set the >1km/h/sec. In this Aggressiveness Acceleration CAN-Bus parameter the total number of Count such accelerations is recorded for every hour of travelling (sec). The total time that the Aggressiveness Acceleration CAN-Bus aggressive accelerations lasted Duration within an hour (#).Aggressive braking has been set Aggressiveness Braking Count CAN-Bus the >2km/h/sec. In this parameter
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 40
Acquisition Parameter Comments system the total number of such braking is recorded for every hour of travelling (sec). The total time that the harsh Aggressiveness Braking CAN-Bus Duration braking lasted within an hour (#). It is the total number of RPM Violation Count CAN-Bus exceedances from the green indicated zone within an hour (sec). The total duration of the RPM Violation Duration CAN-Bus exceedances within an hour (sec). The total duration of running RPM Green Zone Duration CAN-Bus the engine in the green zone within an hour Idling Duration CAN-Bus (sec) (sec). The total duration of driving Driver Behaviour Low ( CAN-Bus the truck with a speed lower or <50km/h) Speed Duration equal to 50km/h with ah hour (sec). The total duration of driving Driver Behaviour High ( CAN-Bus the truck with a speed higher than >50km/h) Speed Duration 50km/h within an hour
Important Note: The above mentioned thresholds, as well as the characterization of “aggressive” acceleration or deceleration have been introduced by Eliaids S.A. for their fleet management purposes. These values are not adopted as representative (or not) by the consortium.
Data analysis -methodology The data sample for the period 01/01/2016-30/09/2016 has been chosen as the most recent one, reflecting the actual driving conditions and the most frequently driven routes in this year. All routes have been depicted on a map per month and the most frequent were filtered for the next step analysis. The next step was to identify whether the trucks that are transmitting CAN-Bus data have done some of the frequent routes within a month. The third step was to select some identical (if possible) routes that correspond to winter-spring- summer season in order to identify any significant variations to fuel consumption, because of the climate variation. This was not possible in all cases, mainly because all “CAN-Bus” trucks did not repeat the same route in the sampling period and because of external issues such the farmer highway blocks in the winter-spring period that not allowed the trucks to do long trips (e.g. Thessaloniki-Athens).
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 41
The end goal of the analysis was to provide transport mission representative detailed data of three route types, as concluded from the fleet manager survey: a short (<150km), a semi- long (200-300km) and a long (>500km) route of nationally operating fleets.
All the aggregate data per route per month (when possible) are presented in Annex 1.
Results The most important findings are summarised in the next bullet points:
The fuel consumption varies approximately between 25-45l/km. There is not a specific trend associated to the length of the route. Even though the highest consumption has been logged during the long route, this is an event clarified by the morphology of the route and not by its length. Trucks were loaded around 15tonnes in average. Use of the highway network is the predominant (as expected) varied between 75-98% of the whole route. The remaining route is mainly driven on an rural road for the semi-long routes and on an urban road for the short routes. The average speed on routes >150km was approximately 75-80km/h and for the shortest routes was recorded around 50-55km/h. The highest numbers of “harsh” acceleration or deceleration were observed during the short routes. The counts were about twice as many an during the longer routes. RPM violation counts and duration during a long route were very high in comparison to the shorter routes. A possible answer to that is the fact the driver is rushing to deliver the cargo in time which in case of a long route the trip might last 7-8 hours. In order to avoid the stops and goes at tolls, the drivers prefer to take an alternative rural D-tour.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 42
3.4 Environmental perspective How efficiently is freight being transported? First of all, a look on overall trends (Figure 26) is presented. In 2014 the Greenhouse Gas (GHG)47 total emissions in the EU-28 declined by a considerable value: absolute reduction of
1.136 million tonnes of CO2-equivalent (22,9% less). EU therefore is already on track to exceed the objectives (2020 reduction by 20% and 2030 by 40% compared to 1990 values).
Index (1990 = 100)
Year
Figure 26: Greenhouse gas emissions (including international aviation and indirect CO2, excluding LULUCF) trend, EU-28, 1990–2014 (1990 = 100)48
Analysing main source sectors (Figure 27), “Fuel combustion and fugitive emissions from fuels (without transport)” is the biggest source, contributing for 55.1% of 2014 EU-28 Total. In 1990 this source sector was even bigger. Transport (including international aviation) is the second most important source with 23.2% in 2014.
47 As reported by Eurostat, the part “covers trends in emissions of all Kyoto greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF6), in all sectors of the inventories, including international aviation, including indirect CO2 emissions and excluding emissions or removals from land use, land use change and forestry (LULUCF), in line with the EU international headline target of 20 % reduction of GHG emissions by 2020”. 48 Source: Eurostat
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 43
Figure 27: Greenhouse gas emissions by source sector, EU-28, 1990 and 2014 (percentage of total)49
In 2012, year for which it is provided the deepening of Figure 28 (referring only to EU28 countries – focus on Turkey is provided in next paragraph) this share was 24.3% being the value emitted by the transport sector amounted to 1.173 million tonnes CO2 equivalent on a total GHG EU28 emissions of 4.824 tonnes. The figure also shows the breakdown by mode of transport and by country.
Internation Civil Internation % Road on Total for Civil Navigation al Bunkers Aviation al Bunkers Road Railways Navigation Other TRANSPORT Aviation (domestic) – Maritime % National Road Emissions Transport (domestic) – Aviation Emissions Transport Emissions on TOTAL Emissions EU-28 4.824 1.173 151 16 135 843 7 163 17 146 9 72% 17,5% DE 973 189 28 2 26 147 1 9 1 8 4 3,1% FR 515 157 21 5 16 125 1 9 1 8 1 2,6% UK 622 156 34 2 32 108 2 11 2 9 1 2,2% IT 475 121 12 2 9 98 0 11 5 6 1 2,0% ES 381 121 17 3 14 74 0 30 3 27 0 1,5% PL 401 49 2 0 2 46 0 1 0 1 1 0,9% NL 245 88 10 0 10 33 0 44 1 44 0,7% BE 140 49 4 0 4 24 0 20 1 20 0 0,5% AT 82 24 2 0 2 21 0 0 0 0 0 0,4% SE 66 27 3 1 2 18 0 6 0 6 0 0,4% CZ 132 18 1 0 1 17 0 0 0 0 0,3% PT 74 22 3 0 3 16 0 2 0 2 0,3% RO 119 16 1 0 0 14 1 0 0 0 0 0,3% EL 121 26 3 1 3 14 0 9 2 7 0 0,3% DK 56 16 3 0 3 11 0 2 1 2 0,2% FI 63 15 2 0 2 11 0 1 1 0 1 0,2% HU 63 11 1 0 1 11 0 0 0 0,2% IE 61 13 2 0 2 10 0 1 0 0 0 0,2% BG 62 9 1 0 1 8 0 0 0 0 1 0,2% LU 13 8 1 0 1 7 0 0 0 0 0,1% SK 43 7 0 0 0 7 0 0 0 0 0 0,1% SI 19 6 0 0 0 6 0 0 0 0 0,1% HR 27 6 0 0 0 5 0 0 0 0,1% LT 22 5 0 0 0 4 0 0 0 0 0 0,1% LV 12 4 0 0 0 3 0 1 0 1 0 0,1% EE 20 3 0 0 0 2 0 1 0 1 0,0% CY 11 4 1 0 1 2 1 1 0,0% MT 7 5 0 0 0 1 4 0 4 0 0,0%
Figure 28: GHG Emissions from Transport by mode and country (million tonnes CO2 equivalent, 2012)50
49 Source: Eurostat
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 44
Road transport affects 72% of the EU28 transport emissions (all modes), as well known therefore is the largest source of emissions of transport sector. If we look at the share on total emissions, road transport contributes for 17.5% for EU28. The top six nations already contribute 12.4% and the data confirm what we saw in the previous section: Germany and the UK; Spain and France; Italy (case study nation); Poland.
3.5 Focus on Turkey Since the optiTruck national transport mission and a part of the optiTruck international transport mission are performed in Turkey, we decided to dedicate a separate section to present a focus on Turkish market and current situation. The involvement of Turkey is a key aspect of the project also because Turkey is an important automotive production base for Europe, causing important heavy truck traffic especially from and to Marmara Region and Europe. This section wants to give a quick overview of the quantities previously seen, related to Turkish context too. Regarding 2016 Turkstat states that “at the end of January, the total number of road motor vehicles registered reached 20.098.994. […] Within the total, cars represented 53%, followed by small trucks 16.3%, motorcycles 14.6%, tractors 8.5%, trucks 4%, minibuses 2.3%, buses 1.1% and special purpose vehicles 0.2%”51. A useful Deloitte report for the investment Support and Promotion Agency of Turkey52, even if not very recent, provide noticeable data. Report says that according to LODER, Turkey’s 2013 logistics industry size is USD 80-100 billion. The Report let us know that in 2013 in Turkey there were more than 23.100 km of operating motorways with over 513 km of on-going construction. Situation may be probably improved by 2035, when 4.130 km of new motorways will be built, as it is possible to see in the map of Figure 29.
50 Source: Elevante from European Environment Agency 51 http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=21601 52 Deloitte, Investment Support and Promotion Agency of Turkey, The Logistics Industry in Turkey, November 2013.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 45
Figure 29: The 2023 and 2035 Targets for the Turkish Highway Network53
The same report let us know the trends of quantity of transport in Turkey, real and forecasted to 2017, as depicted in the Figure 30 and Figure 31 below. Data shows how quantity of transport in Turkey is really relevant, at level of first European countries.
Year Figure 30: Growth Projection of Freight Carried Via Roadways in Turkey, 2013-201754
53 Source: Investment Support and Promotion Agency of Turkey 54 Source: Elaboration on Turkish Ministry of Transport, Maritime Affairs and Communications data, BMI, Deloitte Analysis f: forecast Note: BMI growth rates were used with actual 2012 tonnes-km amount.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 46
Year Figure 31: Total Freight carried via Roadways in Turkey, 2007-201255
Another useful report of ICCT56 let us know that the transport sector accounts for about 15% of greenhouse gas (GHG) emissions, being CO2 the highest contributing emissions category.
Within the transport sector, more than half of the CO2 emissions in Turkey come from heavy-duty vehicles, as it is possible to see by Figure 32. This figure, together with Figure 28, clearly shows that currently environmental issues are not urgent for areas as Turkey, if compared to other European areas. As we will see in next paragraph (see also next Figure 34) the involvement of Turkey is particularly important in perspective, being the forecasted growth very relevant: as reported by ICCT focusing on CO2 emissions only, scenario of emissions in Turkey will increase from about 40 million metric tons (Mt) in 2010 to 79 Mt in 2030.
Figure 32: Greenhouse gas emissions in Turkey (2012), also by vehicle type57
55 Source: Elaboration on Turkish Ministry of Transport, Maritime Affairs and Communications data, BMI, Deloitte Analysis f: forecast Note: BMI growth rates were used with actual 2012 tonnes-km amount. 56 Peter Mock, The automotive sector in Turkey, a baseline analysis of vehicle fleet structure, fuel consumption and emissions, The International Council of Clean Transportation, White Paper, March 2016. 57 Source: UNFCCC, 2013a, own estimates based on ICCT, 2015c.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 47
3.6 Summary of the results The above data highlighted a clear scenario. To summarize it all, for this paragraph has been chosen a graphic “radar” representation which results to be very suitable for this purpose. The graph identifies a number of parameters that gather and describe all the values seen previously. The defined parameters are:
Current infrastructural equipment, related to demand analysis; Current socio-economic scenario, related to demand analysis; Current quantity of transport, related to demand analysis; Trends and forecasted growth, general; Environmental issues, related to GHG emissions. As seen in the previous paragraph it is also possible to identify a number of geographic areas with common characteristics, regarding the relationship with road transport. The identified areas are: A. Western European countries: France, Spain, UK etc., with strong tradition in road transport, especially with regard of social and economic factors; B. Central/North European countries: Germany, Netherlands, Sweden etc. with logistic tradition and where can be founded the most concentration of loadings/unloadings; C. Italy, that represents a case apart, because of its peculiarities; D. Eastern European countries, Central/North: Poland, Czech Republic, Slovakia, Hungary; E. Eastern Sothern Balkan countries; F. Eastern European Countries (Romania, Bulgaria) and Turkey.
The identified areas are schematically shown in Figure 33:
Figure 33: The six proposed geographical areas with common features in road transport field58
58 Source: Elevante
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 48
For each of the identified parameters, then, some indexes for each of the defined areas were assigned, with relative values (6: maximum, 1: minimum). Please note that these indexes are derived exclusively through qualitative evaluations that take into account the total of the quantities analysed in the previous section. The graph obtained is shown in Figure 34 below.
Measure of Current infrastructural importance of index: equipment 6 (high)(high 6 1 (low) 5 4 3 Current socio-economic A Environmental issues 2 scenario B 1 C 0 D E F
Trends and forecasted Current quantity of growth transport
Figure 34: Main parameters related to road transportation and identified areas, radar chart59
The graph highlights how the selected areas of the Transport Mission 2, being settled mostly in Italy (C), can be considered representative of road transport demand, trends, environmental issues, socio economic values since they are placed in a median context between regions with different features. In perspective, the analysis conducted shows that the solutions implemented in optiTruck will hopefully have a great impact also in the other areas: on the one hand, on those areas with a strong tradition as well as the strong presence in the road transport (Western countries such as Spain and France (A); loading countries such as Germany (B)), and, on the other hand and above all, on the Eastern countries, in particular Poland (D), Bulgaria and Turkey (F) where a system of this type can be an optimal solution to limit possible negative environmental impacts due to their evident growing participation in the sector and therefore can be also a mean to a boost and enhance this growth most.
59 Source: Elevante
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 49
4. Key requirements and criteria from users’ perspective
The collection of the user perspectives has been implemented following a specific scheme which permits to guide workshops’ participants and collect useful information to support transport mission definition and to support the deployment of project’s results taking into account these outputs. Codognotto and Eliadis prepared questionnaires in order to collect data and indications from other transport operators and fleet managers, mainly in Italy and Greece, and therefore the output of these activities represent a key input for scenario identification and description.
4.1 Users Workshops
4.1.1 Workshop organized by Codognotto Users survey distributed by Codognotto A dedicated user’s survey is defined and distributed by Codognotto to its main clients. The questionnaire has two main focuses: 1) questions related to basic requirements of transport mission from the users’ perspective (e.g., ETA, FTL, on-time delivery, perfect order fulfilment, …): these are basic elements that represents essential characteristics of the transport mission; 2) questions related to additional requirements of transport mission from users’
perspective (e.g., measurement of emissions, fuel consumption, accuracy of CO2 emissions calculation, environmental planning, …): these are important elements that are not fundamental to fulfil a transport mission but to represent key aspects in order to add value to the logistic and transport service. The questionnaire is divided in three different parts: 1) Company Data This part focuses on the collection of company’s data and description of its main activities. 2) Transport and Logistics Data This part focuses on the collection of transport data, mainly related to freight road transport. For instance, data and indications about the most common transport mission of the companies, average, minimum, maximum length of a representative transport mission, average loading capacity, … are collected. In addition to that, also indication about logistics data and parameters, such as ETA, on time delivery, perfect order fulfilment, … are collected.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 50
3) Value added elements in transport missions This part focuses on the collection of data about the so called value added elements that are related to environmental aspects, fuel consumptions, efficiency, monitoring of transport performances, …. Codognotto Workshop’s results To this purpose, with the support of Unindustria Treviso, Codognotto organized a dedicated workshop on the 18th November 2016.
Table 9: Participants of the workshop organized by Codognotto
Company name Sector Responsible Dimension Antonella Candiotto - Galdi S.r.l. Industrial MEDIUM General Manager Domenico Vettorello - Vettorello S.r.l. Industrial SMALL CEO Cantina Pizzolato Sabrina Pizzolato - Vice Food & beverage SMALL S.r.l. President Colfert S.p.A. Retail Mirco Zanato - CEO MEDIUM Mermec S.p.A. Industrial Business Unit Director BIG
Three of the participating companies are involved in the industrial sector (Galdi S.r.l., Vettorello S.r.l. and Mermec S.p.A). Cantina Pizzolato S.r.l. is a wine producer and Colfert S.p.A. produces components for windows and doors. In terms of specific logistics and transport necessities the companies considerably differ even if a common view has raised in the workshop. Mermec S.p.A. is a worldwide leading company in measuring technologies for transport. Every year they have four/five transport missions organised by project cargos characterised by big volumes commodities. Along the year they regularly transport components by FTL (Full Truck Load) or LTL (Less then Full Truck Load) in 52 countries. Colfert S.p.A. transports hardware by groupage in a 100 km range. Colfert S.p.A. represents a key element in Italian metalware sector. Cantina Pizzolato S.r.l. does not directly manage logistics since their clients organise it. Clients are located in all Europe. Almost the 50% of the production are directed to Sweden travelling almost by intermodality. Only non-programmed transport missions transport missions are managed by LTL or FTL. Vettorello S.r.L. distributes components for industrial machinery in a 100 kms range organising it by LTL. Galdi S.r.l. provides added-value solutions for filling and packaging of milk, dairy products and fruit juice. The average of the length of transport is very difficult to determine since they manage transport in all Europe organising it manly by LTL. All participants consider the most problematic aspects of logistics: costs, bureaucracy, commodity protection and timing. Consistently, all these elements have been ranked as “very important” in the criteria to consider the quality of a transport. “The environmental aspects” of transport is also considered extremely important. Nonetheless, the companies admitted that the environmental aspect is not generally taken into consideration in the choice of the transport operator. We can assume that competitive costs, efficiency in
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 51
bureaucracy management, correct measures for commodity protection and respect of time plans are considered prerequisites while an advanced level of environmental policy is an added value. All the interviewed companies respect specific environmental policies and actively participate in organised groups for the exchange of good practices among entrepreneurs on the topic. Nonetheless, the transport matter is not considered in their assessments and a detailed monitoring policy for transport emission is not in place. The workshop suggests that the four prerequisites cannot be affected by innovative solutions even if they can guarantee an added value. The statement “I would love to adopt sustainable transport solutions if they do not cost me more” can well summarised the common view expressed by the participating companies. A different approach have been pointed out by IKEA in a bilateral meeting organised the 25th of November. IKEA has adopted a very strict environmental policy that directly involves its subcontractors. They are directly called to implement a strategic view with a long term prospective. IKEA’s approach endorses the creation of stable partnerships with transport operators that can strive for innovative and efficient transport solutions deployment. Effective distribution in the entire goods flow from supplier to customer is an important aspect of achieving a low price, and to minimise the environmental impact from transport. Modal shift is adopted where possible, and where this is the more environmentally adapted option. Truck and container loads are optimised to maximise fill rates and minimise the number of transports. Smart packaging is also thought to reduce the environmental impact from goods transport. Thanks to its policy, IKEA increases the awareness along the value chain and represents a driver of innovation for the logistics and transport sector. A strict monitoring plan is performed in order to assure transport operators environmental performances.
4.1.2 Workshop organized by Eliadis Eliadis and CERTH have created a dedicated to optiTruck scope questionnaire that contains 15 questions in line with the survey distributed by Codognotto, but focusing mainly on national transport of goods within the area of Greece. A number of questions refer to the route and its characteristics and there is also a group of questions requesting information about the predominant route conditions. The Eliadis questionnaire can be found in the Annex 2: “ELIADIS Users’ survey details”. The company profile of the seven respondents can be found in the following table.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 52
Table 10: Participants profile of the Eliadis survey
National and Average load Fleet size international per route (tn) Company id (# of vehicles) transport 1 23 National: 100% 24
2 17 National: 100% 23
3 30 National: 100% 20
4 15 National: 100% 23
5 17 National: 87.5% 22 National: 87.5% 6 270 International: 20 12.5% 7 22 National: 100% 20
Survey results The most frequently transported cargo is the container (almost 60%), followed by cargo on pallets and refrigerated products.
1%
Container 21% Pallets Refrigerated products Liquid cargo 20% 58% Balk cargo Other
Figure 35: Cargo type carried by the trucks (percentage of the total cargo carried)
As expected most of the transport is taking place on the highway network (44%). A large percentage is also done via urban road, which should be taken into account as a parameter for fuel consumption reduction.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 53
38% 44% Urban road network Secondary road network Highway
18%
Figure 36: Road type used (percentage of total missions)
The reason for using the urban network is either the origin or the destination of the route. More specifically the port facilities is for 50-70% the reason for entering the urban network, the shipper premises (30-99%), the Customs storage (5-30%) and less frequently the railway station and airport. As far as the traffic density during a route is concerned, the fleet managers answered that most of the time the traffic can be characterized as average (42%). Light and heavy traffic are equally distributed (31% and 27% respectively).
27% 31% Light Average Heavy
42%
Figure 37: Traffic density in the route (percentage average)
It results that sunshine and cloudiness are the most predominant weather conditions (64%), something that was expected taking into account the warm climate in Greece. Rainy conditions account for 21%. An important weather parameter that influences fuel consumption is the wind which participates with 8% during national routes.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 54
2% 5% 8% Sunshine - Cloudiness Rain Fog 21% Snow 64% Strong winds Other
Figure 38: Weather conditions during the route
Looking at the speed of the truck, the interviewees answered about 78 km/h for the highway network, about 38 km/h for the secondary network which can be explained by the variation in the road inclination and about 46 km/h on the urban network, a higher speed than the speed in the rural network mainly due to inner city arterial roads that have been considered as urban road network. The percentage of stops-and-goes due to road facilities (e.g. tolls, traffic lights) during a highway route varies between 2-20%, 10-50% in a rural and 55-90% in urban one. Regarding the length of a short route, the answers vary between 30-150km and the route lasts about 30min-4hours. A long route is considered between 400-560km and its duration is about 6-12hours. The proportion of driving on roads with different altitude (slope) during a route on the secondary road network varies between 35-100%. This is due to the morphology of the Greek inland and the several mountains the trucks have to cross via the rural road network.
90
80 h)
\ 70 60 50 Urban road network 40 30 Secondary road network 20 Highway Vehicle (kmSpeed 10 0 Urban road Secondary Highway network road network
Figure 39: Average speed for each type of road
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 55
The average fuel consumption per road type is depicted below. The highest consumption noticed by the fleet managers occurs during routes on urban network (57 liters/100km), which can be clarified by the frequent stops and goes.
60
50
40
30 Urban road network
(lt/100km) 20 Secondary road network Highway
10 Average Average consumption fuel
0 Urban road Secondary Highway network road network
Figure 40: Average fuel consumption per 100km (liters)
Finally the air-conditioning in the truck cabin was activated for about the ¾ of the length of an average route during warm days. Conclusions The above mentioned results can be summarized into the following table:
Table 11: Profile of transport mission of trucks >40tn
Parameter Value Average load per route 20-24tn Container, pallets, refrigerated Cargo type products Weather conditions Dry, rainy, strong wind Short: 30-150km Distance of average route Long: 400-560km Short: 30min-4hours Duration of average route Long: 6-12hours Traffic density during Average (42%) route Origin or destination Port facilities, shipper premises, when using the urban Customs network Different altitude (slope) during a route on the 35-100% of the routes secondary road network
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 56
Air-conditioning activated ¾ of the length of the route during the warm days Type of road network Urban Rural Highway Network used 38% 18% 44% Average speed (km/h) 46 38 78 Average fuel consumption 57 34 35 (liters/100km) Stop and go in a route 55-90% 10-50% 2-20%
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 57
5. Use cases definition
The present chapter focuses on the definition of project use cases. It starts from the results coming from Chapter 3 and 4 of this document. In Chapter 3, the key elements of representative transport missions have been identified and most representative transport missions are described and characterized on the basis of Codognotto and Eliadis real data. Finally, the optiTruck transport missions are compared to the European areas with common characteristic in terms of current infrastructural equipment, current socio-economic scenario, current quantity of transport, trends and forecasted growth, environmental issues. While, in Chapter 4, through questionnaires, requirements about transport mission are collected directly with the involvement of other users and fleet managers in Italy and in Greece. We selected operators and users belonging to these two European Countries since Greece and Italy are covered by the optiTruck transport missions in the real-world tests and therefore it is important to collect transport data about these Countries. On the basis of such inputs, the present Chapter focuses on the definition of use cases. A use case is a list of steps that defines interactions between actors and systems. The use cases will be defined in terms of transport mission phases (pre-mission, in-mission, post- mission) and will represent the steps and the actions to be carried out in the above mentioned project scenarios. The process applied in this Chapter includes two main phases: - Use cases specification; - Matching use cases with Innovation Elements. The User and System Requirements will be defined in Task 2.3 and Task 2.4 and therefore the matching between use cases and Requirements will be presented in future project deliverables.
5.1 Use case specification Use case is typically represented by a list of steps that defines interactions between actors and systems. Use cases can been defined at different levels, starting from the business perspective through the system engineering up to the software modelling. The use case descriptions have been carried out by filling information in the template table shown in Figure 41.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 58
Unique use Case ID Use Case UCXX
Title Meaningful title for the project users and partners The Title explains a particular case of use Description Text explaining how optiTruck is expected to contribute to the fulfilment of project’s in the scenario. objectives with reference to the phase of the transport mission.
Description of the functionality provided to the main involved actors. Description of
functionality used by each involved actor.
Goal of the use Overall motivation for implementing the Use Case Why the use case is case relevant.
Actors Actor involved in the Use Case: they can be human or modules (SW and HW) of the architecture. Phase of the transport mission: Phase Phase of the transport mission pre-mission, in- mission, post- Link with IE IE/s addressed by the UC mission.
Preconditions · Organisational or technical precondition 1 Link with IE/s, also · Organisational or technical precondition 2 showed in mapping · … in Section 5.4
What should be there before implementing Main Flow 1 Flow of users’ activities – step 1. the use case. 2 Flow of users’ activities – step 2. 3 … What happens, in time sequence.
User Cross-reference to relevant Requirements will be completed in Task 2.3 and Task Cross-reference to Requirements 2.4. relevant Requirements
Figure 41: Use Case specification template
5.1.1 Matching use cases with Innovation Elements Once the specific use cases have been defined, they need to be matched with the Innovation Elements of the optiTruck project. These IEs are 10 innovations proposed and to be developed by the optiTruck project, as described in the DoW. In order to do this, a matrix is prepared as shown in Figure 42. This analysis of very useful in order to verify that all the IEs are tested and covered by at least one UC of the project.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 59
Unique Innovation Element ID
Innovation Element title
Unique Use Case ID
Matrix of matching between Use Cases and optiTruck Innovation Elements
Use Case title
Figure 42: Use case matching matrix with Innovation Elements
5.2 Transport Missions and Scenarios definition The two main Transport Missions of the project have been identified and are defined as follows:
Transport Mission 1: this TM corresponds to a national transport mission. The route starts from a predefined location (terminal or depot) where the truck is loaded with containers, then it will follow a combined motorway and secondary road route, delivers the “cargo” to a specified destination and ends up at the Ford Otosan facilities.
Transport Mission 2: this TM corresponds to an international trip. Two trucks start loaded from Turkey, drive through northern Greece, uncoupling the trailer in Igoumenitsa port. From Igoumenitsa port, the trucks reach the port of Bari (Italy) by boat, couple a new trailer, reaching Verona and Turin (Italy) as a final destination.
On the basis of the two defined Transport Missions (TM) of the project, different scenarios will be defined in Task 6.1 “Verification and testing plan”. From the analysis carried out in the present deliverable we have identified the following type of data that will be identified to properly define test scenarios:
traffic conditions, driver’ behaviour, typical weather conditions, average speed, type of road and path, presence of traffic lights and intersections, presence of tolls, and refuelling and parking
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 60
The defined scenarios have to take into account elements of the most representative transport mission in Europe, as defined in Chapter 3 of the present deliverable. For instance a scenario will focus on a national trip not exceeding more than one working day in duration (200-300km half day mission). Trucks will be loaded with containers with more than 20 tons of cargo, with a weight established by the fleet management company. The scenario can foresee a test during the day or during the night, with weather precipitation or clear weather to investigate the effect of these external factors on the performed transport mission. The scenario can be tested with normal or heavy traffic condition and the number of Kms per type of road (e.g., motorways, secondary route, urban route will be defined.
5.3 Use case definition The following use cases have been identified as relevant for the optiTruck project: UC01: Create new transport mission This UC is linked with step 1 of pre-mission phase described in D2.2. UC02: On-board data collection This UC is linked with step 1 of in-mission phase described in D2.2. UC03: Cloud system data collection This UC is linked with step 3 of pre-mission phase and step 2 of in-mission phase described in D2.2. UC04: Calculation and planning of the best route options This UC is linked with steps 3 and 4 of pre-mission phase described in D2.2. UC05: Detection of deviations from the initial plan and detection of best route related deviations This UC is linked with steps 1 and 2 of in-mission phase described in D2.2. UC06: Cloud optimization This UC is linked with step 3 of in-mission phase described in D2.2. UC07: On-board optimization This UC is linked with steps 4 and 5 of in-mission phase described in D2.2. UC08: Support the driver in real-time This UC is linked with step 6 of in-mission phase described in D2.2. UC09: Measure and evaluate transport mission performances in terms of consumption efficiency This UC is linked with step 2 of post-mission phase described in D2.2. UC10: Post-mission data collection for knowledge-base mission enrichment This UC is linked with step 2 of post-mission phase described in D2.2.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 61
The following Table 12 summarizes the main actors involved in the above listed use cases: Table 12: Definition of the Actors involved in the Use Cases
Actor Description
Company that owns the truck and inserts the TM in
the optiTruck cloud system. Fleet management company
Driver of the truck (human actor). He uses a HMI to receive indications about the TM and has also Driver the possibility to indicate some events.
A component of the cloud system responsible for the storage of data coming from on-board system Data architecture and from external services.
Cloud System (or Cloud Computing System) operates in a Platform-as-a-Service (PaaS) Cloud Cloud system Computing environment.
System on the truck. This system is installed on the vehicle and has the sensors component and On-board system the optimization one.
Component of the optiTruck Global Optimizer that
supervises the optimization on the cloud. Cloud optimizer
One component of the optiTruck Global Optimizer that supervises the optimization on the on-board On-board optimizer system.
Dashboard used to collect transport mission data
from the fleet management company. Mission dashboard
Module that collects data coming from radar and
cameras installed in the vehicle. Radar & Camera Module
Sensors (basic and also additional sensors from the optiTruck project) installed on the equipped optiTruck vehicle.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 62
Actor Description On-board sensors
Services that communicate to the cloud system
external data, e.g., about weather, traffic, … External services
Interface used to communicate with the driver. HMI
Hereafter the use cases are described following the approach presented in Section 5.1.1.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 63
Use Case UC01
Title Create new transport mission
Description A new transport mission is created and transport mission data are inserted by the fleet management company.
Goal of the use To create a new complete transport mission, on the basis of established case origin and destination points.
Actors Fleet management company Cloud computing system (Data architecture component):
Services Data Manager Services Data Storage Mission dashboard
OUTPUT received by the: Cloud system
Services Data Manager
Phase Pre-mission
Link with IE IE10
Preconditions An origin and a destination of a transport mission are established The freight to be transported is set ETA is set Fixed parameters to be respected are indicated by the fleet management company. These indications will be then considered as constraints for the optimization. These parameters concern for instance time of loading/unloading, total cost of the TM, etc., according to the results of the questionnaire described in Chapter 4 of the present Derivable D2.1.
Main Flow 1. Fleet management company operator inserts mission data (starting and ending locations, truck information) into Mission Dashboard 2. When Fleet management company operator confirms inserted data, the Mission Dashboard triggers Services Data Manager and stores mission data. 3. Services Data Manager stores mission data into Services Data Storage, then notifies to the Mission Dashboard that mission data has been
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 64
inserted and best route computation will be processed.
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 43: UC01 diagram: Create new mission
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 65
Use Case UC02
Title On-board data collection
Description In order to enhance the route information and data about vehicle parameters, on-board sensors collect in real-time the value of such parameters. These data are used directly by the on-board optimizer and also communicated to the cloud optimizer.
Goal of the use During the execution of the transport mission, sensors collect data about case the status of the vehicle and its associated parameters.
Actors On-board system (component Sensor Fusion Module) On-board sensors fusion module:
Radar & Camera Module Driver
OUTPUT received by the: On-board optimizer Cloud optimizer
Phase In-mission
Link with IE IE1, IE2, IE3, IE4, IE5, IE6
Preconditions The optimized transport mission is calculated; The transport mission has started; On-board sensors and system are installed in the truck.
Main Flow 1. Collection of data from sensors: engine parameters, exhaust gas aftertreatment, data from auxiliary systems, about changes related to load, temperature, etc. 2. Powertrain parameters are monitored. 3. Calibration data are collected from Control Unit and from simulation model. 4. Data about surrounding vehicles are recorded. 5. In particular cases, such as in case of incident on the road, the driver has the possibility to quickly insert indications of this unexpected event.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 66
6. All the data collected by the on-board system are communicated to the on-board optimizer. 7. Data about vehicle parameters (mass, load, inertia, tire diameter, etc.) are communicated to the cloud system (data architecture component).
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 44: UC02 diagram: On-board data collection
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 67
Use Case UC03
Title Cloud system data collection
Description In order to enhance the route information and data about the transport mission parameters, data from external services are collected in conjunction with data coming from the on-board system. These data are communicated to and used by the cloud optimizer.
Goal of the use During planning and execution of the transport mission, parameters from case external systems are collected in order to update the transport mission data on the data architecture component of the cloud system.
Actors On-board system External services Data architecture component
Services Data Manager Services Data Storage OUTPUT received by the: Cloud system (data architecture)
Planning Data Manager On-board optimizer
Phase Pre-mission and In-mission
Link with IE IE7, IE8
Preconditions The optimized transport mission is calculated; The transport mission has started; On-board sensors and system are installed in the truck.
Main Flow 1. Data from external services are collected in relation to traffic and weather forecasts that have strong impact on the vehicle performances. 2. Data from ADAS services are stored. 3. Data coming from the on-board system are stored in the data architecture component. 4. The collected data are communicated to the optimisation module of the cloud system. 5. The collected data about route parameters, such as wind speed, roads conditions, etc., are communicated to the on-board optimizer.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 68
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 45: UC03 diagram: Cloud system data collection
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 69
Use Case UC04
Title Calculation and planning of the best route options
Description The cloud optimizer calculates the entire transport plan based on a created transport mission.
Goal of the use To calculate of the pre-mission strategy for the best route to be followed case during the transport mission. To optimise the pre-mission route and transport parameters by taking into account traffic and weather data, vehicle performance parameters, and predicted traffic and road environment conditions. Actors Data architecture
Services Data Manager Services Data Storage Planning Data Manager Planning Data Storage Cloud optimizer
Vehicle Longitudinal Model Eco-Route Planning On-board system Fleet management company
OUTPUT received by the: Cloud system Fleet management company On-board optimizer
Pre-mission
Link with IE IE10
Preconditions Destination position is known and ETA is set Access to third party apps (weather, traffic situations) is given Access to maps incl. topography with all possible routes is given Etc. Data about TM are stored in the data architecture component of the cloud system. Data about type of vehicle and payload is given.
Main Flow 1. Services Data Manager triggers Planning Data Manager that a new mission has been submitted and needs to be scheduled.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 70
2. The Eco-Route Planning determines the possible routes from start to destination taking into consideration historical traffic data, road restrictions, road topography, route distance and ETA. 3. The Eco-Route Planning collects live weather and traffic data for possible routes determined in step 2. 4. The Eco-Route Planning feeds possible routes to Vehicle Longitudinal Model to evaluate the possible routes. 5. The Eco-Route Planning performs the ranking of the possible routes evaluated in Step 4. 6. The best route is stored in the cloud system. 7. The best route is communicated to the fleet management company via mission dashboard for final approval. 8. The best route is communicated to the on-board system.
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 46: UC04 diagram: Calculation and planning of the best route options
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 71
Use Case UC05
Title Detection of deviations from the initial plan and detection of best route related deviations
Description The main objective is to monitor changes and modifications related to the real-time data coming from vehicle (powertrain, on-board sensors) and from the external data (e.g. weather conditions, traffic, surrounding vehicles, driver, etc.).
Goal of the use To support the monitoring of the transport mission based on real-time case data from the vehicle and from external data.
Actors Cloud system (data architecture)
Services Data Manager Services Data Storage Planning Data Manager Planning Data Storage Eco Route Planning On-board sensors External Data
OUTPUTS received by: On-board optimizer Cloud optimizer Driver
Phase In-mission
Link with IE IE2, IE3, IE5
Preconditions Agreement in a common data model Standardization of communication protocols Collection of data from external services Implementation of on-board sensors Calculated best route
Main Flow 1. Comparison between sensors data with predicted data. 2. Comparison between external data with data already collected in the cloud system. 3. In case relevant deviations are detected in the vehicle, these events trigger the cloud optimizer and a re-calculation is performed. 4. In case relevant deviations are detected in the cloud system, these
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 72
events trigger the communication to the on-board optimizer and the truck driver is informed. 5. Inform the driver in case of situations in which he has to intervene due to the driving patterns of the surrounding vehicles.
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 47: UC05 diagram: Detection of deviations from the initial plan and detection of best route related deviations
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 73
Use Case UC06
Title Cloud optimization
Description On the basis of the data and parameters coming from external systems, detected deviations and data from vehicle sensors, a new optimization has to be calculated in order to update the initial best plan (see UC04).
Goal of the use To optimise the route and transport mission parameters accordingly to case real-time traffic and weather data, vehicle performance parameters, and predicted traffic and road environment conditions.
Actors Data Feeder Cloud system:
Planning Data Manager Planning Data Storage Services Data Storage Eco Route Planning On-board system HMI
OUTPUT received by: Cloud system: data feeder and storage for building a knowledge database On-board system Driver Fleet operator
Phase In-mission
Link with IE IE7
Preconditions Agreement in a common data model Standardization of communication protocols Collection of data from external sources Data stored in the data architecture of the cloud system Implementation of a user-friendly on-board HMI Main Flow 1. The data feeder checks the availability of new real-time external data and if so, triggers the predictive traffic model to forecast short-term traffic conditions on the remaining routes of the truck. 2. The traffic model provides prediction to Eco-Route Planning, which will determine possible routes between the current location of the truck and its destination (taking account of road topography,
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 74
predicted traffic conditions, weather etc.). 3. The Eco-Route Planning feeds possible routes to Vehicle Longitudinal Model to determine the best route (and second best candidates if needed) which will be sent to the on-board system by the data feeder. 4. The new optimised route is stored in the data architecture component of the cloud system to build a historic knowledge database for future research and data mining. 5. The new optimised route is communicated to the truck driver via HMI.
User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 48: UC06 Diagram: Cloud optimization
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 75
Use Case UC07
Title On-board optimization
Description Predictive route information is used to optimize the operating point of auxiliaries, engine parameters and exhaust gas aftertreatment system. The system is supervised to monitor function effectiveness, validation of operating points, check consistency and detect disturbances.
Goal of the use To optimize the operating parameters of engine as well as auxiliaries case regarding fuel consumption in consideration of the predicted route profile and EURO6 emission legislation.
Actors On-board optimizer
OUTPUT received by: On board system Cloud system (data architecture module)
Services Data Storage Planning Data Storage
Phase In-mission
Link with IE IE1, IE2, IE3, IE4, IE5, IE6
Preconditions Optimized route including: o GPS coordinates for significant points o topography of selected route o speed profile of selected route (based on road limits, traffic conditions) Main Flow 1. Estimation of power demand for route 2. Prediction of engine operating points 3. Estimation of emissions for specific time horizon 4. Optimisation of auxiliary power consumption 5. Monitoring of function effectiveness and consistency
6. Determination of NOx/CO2 trade-off for each parameter 7. Optimisation of parameter settings regarding fuel consumption 8. Evaluation of parameter settings for expected route horizon 9. Utilization of predictive cruise control
User Cross-reference to relevant Requirements will be completed in Task 2.3
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 76
Requirements and Task 2.4.
Figure 49: UC07 diagram: On-board optimization
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 77
Use Case UC08
Title Support the driver in real-time
Description The main objective is to support the driver in following the best calculated route as well in case of unexpected events or deviations.
Goal of the use To support the driver in real-time to support him during the transport case mission.
Actors Cloud system On-board system HMI Driver
Phase In-mission
Link with IE IE9
Preconditions Agreement in a common data model Standardization of communication protocols Implementation of a user-friendly on-board HMI
Main Flow 1. The cloud system communicates via HMI the route and set speed to be followed to the driver. 2. The cloud system informs the driver that something has changed. 3. The on-board system informs the driver on the driving behaviour to be applied in order to respect the performances of fuel emission and savings calculated by the on-board optimizer. 4. The cloud system communicates the driver if he has to intervene directly to manage properly the presence of surrounding vehicles. 5. The cloud system checks if the driver is respecting the indications. 6. The cloud system informs the driver if he has to take the manual control of the vehicle, in case the optimizer does not work. User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 78
Figure 50: UC08 diagram: Support the driver in real time
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 79
Use Case UC09
Title Measure and evaluate transport mission performances in terms of consumption efficiency
Description Transport related status information will be processed in order to detect and report consumptions. This automatic calculation is performed using directly actual data coming from the real transport missions. On the basis of this output the on-board optimizer performances are evaluated.
Goal of the use To measure transport mission performance in terms of efficiency and to case evaluate the on-board optimizer performances. This is done comparing data related to a basic truck (from cloud based model) only considering the cloud optimised route with data coming from the optiTruck truck taking the on-board optimisation of the drive train as well as the cloud optimised route into consideration.
Actors On-board sensors On-board optimizer
OUTPUT received by: On-board system Cloud system Fleet management company
Phase Post-mission
Link with IE IE7
Preconditions Agreement in a common data model Standardization of communication protocols Agreement on a common emissions calculation approach and standard Collection of data with on-board sensors (see UC02)
Main Flow 1 Performance parameters are calculated based on the actual transport mission execution data, on the consumption profiles, and on the data available in real-time. 2 During the transport mission the actual status of the transport is updated and data about energy consumption are collected using the on-board sensors. 3 Energy flow mode operating mode coordinator optimizes the energy level of each energy sink. For example if the temperature gets warmer or there is more sunlight on the road track ahead the cooling
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 80
can be optimized, etc.. Information about fuel consumption and CO2 values related to geographical positions from previous TM on same route (or part of route) and data about truck parameters are compared to the optimization results obtained by the solution provided by the on-board optimizer. 4 Fuel consumption data are generated for each transport mission in relation to calculation of fuel consumption reduction and fuel savings. 5 On termination of the transport mission, the energy consumption data are made available to fleet management company. 6 The emission data collected is used to improve the evaluation made by the cloud optimizer. At operational level: immediate report on delivery data; at strategic level: data recorded in historical database to further planning services. User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
Figure 51: UC09 diagram: Measure and evaluate transport mission performances in terms of consumption efficiency
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 81
Use Case UC10
Title Post-mission data collection for knowledge-base mission enrichment
Description Data from finished transport missions are collected and used to build a dedicated knowledge-base mission in order to enhance the optimization of future transport missions.
Goal of the use To monitor the data and performances of transport missions to improve case optimization capabilities.
Actors Cloud system (data architecture) On-board system
OUTPUT received by: On-board system Cloud system Fleet management company
Phase Post-mission
Link with IE IE7
Preconditions Agreement in a common data model Standardization of communication protocols Agreement on a common emissions calculation approach and standard The transport mission is finished.
Main Flow 1. After the conclusion of the transport mission, status and data about transport and energy consumption are collected. 2. These data are elaborated and aggregated to create a knowledge-base for transport missions. 3. These data are useful to improve the performances and capabilities of the cloud optimizer. 4. These data are made available to the fleet management company. User Cross-reference to relevant Requirements will be completed in Task 2.3 Requirements and Task 2.4.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 82
Figure 52: UC10 diagram: Post-mission data collection for knowledge-base mission enrichment
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 83
5.4 Matching Use Cases with Innovation Elements
The optiTruck Innovation Elements (IE), as described in the DoW, are the following:
- IE1 Optimization of powertrain control and calibration according to real world driving conditions
- IE2 Engine control optimization using predictive and real time model based control approaches
- IE3 Adaptation to dynamic changes in vehicle load and aerodynamic forces
- IE4 Optimized after-treatment controls
- IE5 Predictive management and control of auxiliary systems
- IE6 Predictive management of the cooling system
- IE7 Energy flow operating mode coordinator
- IE8 Driving patterns of surrounding vehicles
- IE9 Driver support information system (ecoNavigation or ecoDriving for trucks)
- IE10 Definition of the transport mission and initial calibration of optimum points (Pre-trip & on-trip)
The following Table 13 shows the matching between the UCs and the IEs.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 84
Table 13: Matching use cases with Innovation Elements
IE1 IE2 IE3 IE4 IE5 IE6 IE7 IE8 IE9 IE10 Optimization of Definition of Engine control Adaptation to powertrain Driver support the transport optimization dynamic Predictive control and Predictive information mission and using predictive changes in Optimized after- management Energy flow Driving patterns Matching between UCs and IEs calibration management of system initial and real time vehicle load treatment and control of operating mode of surrounding according to the cooling (ecoNavigation calibration of model based and controls auxiliary coordinator vehicles real world system or ecoDriving optimum points control aerodynamic systems driving for trucks) (Pre-trip & on- approaches forces conditions trip) Create new transport UC01 X mission
UC02 On-board data collection X X X X X X
Cloud system data UC03 X X collection
Calculation and planning UC04 X of the best route options
Detection of deviations UC05 X X X from initial plan (…)
UC06 Cloud optimization X
UC07 On-board optimization X X X X X X
Support the driver in real- UC08 X time Measure and evaluate UC09 TM performances in X terms of consumption Post-mission data UC10 collection for knowledge- X base mission enrichment
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 85
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 86
6. Conclusions
This Deliverable represents the first output of WP2 and therefore aims at setting and defining some initial core points of the optiTruck project. The presented results started from a deep analysis the most frequent transport missions and associated parameters to find out how the selected optiTruck transport missions are representative of the average EU transport missions. Then, these considerations are enriched with inputs coming directly from other interviewed transport operators and fleet management companies. Finally, the key use cases of the project are identified and defined in order to describe how the components of the optiTruck architecture will be used in the test scenarios of the two project transport missions. To conclude this deliverable D2.1 very shorty we are answering to the following questions:
Which are the main outputs of the analysis? The main output of the analysis concerning the most representative transport mission shows that the solutions implemented in optiTruck will hopefully have a great impact in areas with a strong tradition as well as the strong presence in the road transport (Western countries such as Spain, Italy and France and loading countries such as Germany), and, also, on the Eastern countries, in particular Poland, Bulgaria and Turkey where a system of this type can be an optimal solution to limit possible negative environmental impacts due to their evident growing participation in the sector and therefore can be also a mean to a boost and enhance this growth most. According to the optiTruck architecture, the ten defined use cases (UCs) for pre-mission, in-mission and post-mission phases show how the cloud and on-board system will be used in order to test and measure the impact of the optiTruck Innovation Elements. Specifically, the following use cases have been defined: UC01 - Create new transport mission, UC02 - On-board data collection, UC03 - Cloud system data collection, UC04 - Calculation and planning of the best route options, UC05 - Detection of deviations from the initial plan, UC06 - Cloud optimization, UC07 - On-board optimization, UC08 - Support the driver in real-time, UC09 - Measure transport mission performances in terms consumption efficiency, UC10 - Collect data for knowledge-based mission optimization). These UCs have been mapped with the ten optiTruck Innovation Element (IE) to show how the IEs will be testes and assessed. The mapping demonstrates that all the IEs are covered by at least by one UC.
What is the impact of these results on the next project activities?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 87
The presented results will have impact in the following main part of the project: - In WP2, in T2.3 and T2.4, in relation to the definition of user and system requirements. The requirements will be defined according to the identified use cases and mapped with them to show the relationships. Each requirement will be covered by at least one use case; - In WP3, especially in T3.1, the utilization of the components of the architecture described in the use case will be taken into account; - In WP6, in Task 6.1, the results about the representative transport missions and related parameters will carefully taken into consideration to define the test scenarios of the two project transport missions.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 88
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 89
7. References
Reports and Publications AEA and Ricardo (2011), “Reduction and Testing of Greenhouse Gas (GHG) Emissions from Heavy Duty Vehicles”, Lot 1: Strategy.
ACEA Position Paper (2016), Reducing CO2 Emissions from Heavy-Duty Vehicles, JANUARY 2016. ACEA (2016–2017), The Automobile Industry Pocket Guide.
ACEA (2010), Commercial Vehicles and CO2. Deloitte (2013). The Logistics Industry in Turkey. Ankara: Investment Support and Promotion Agency of Turkey. Delphi (2015-2016). Worldwide Emissions Standards: Heavy Duty and Off-Highway Vehicles. Engeljehringer K., Emission: Heavy Duty and OFF-Road, Emission Test Systems, AVL. Enrico Tosti, Simonetta Zappa (2016). I trasporti internazionali di merci dell’Italia 2015. Banca d’Italia Eurosistema. European Commission (2012). Road Transport: A change of gear. Luxembourg: Publications Office of the European Union. European Commission (2013). Directorate General for Mobility and Transport: The Core Network Corridors TRANS EUROPEAN TRANSPORT NETWORK. European Commission (2014). General Directorate for Mobility and Transport: The Core Network Corridors Progress Report. European Commission (2014). Mediterranean Core Network Corridor Study, Final Report,EU. European Commission(2014). Baltic Adriatic Core Network Corridor Study, Final Report, Fourth Corridor Forum Meeting. European Commission (2014). Mediterranean Core Network Corridor Study, Final Report. European Commission (2014). Orient/East-Med Core Network Corridor Study, Final Report. European Commission (2014). Scandinavian Mediterranean Core Network Corridor Study, Final Report. European Commission (2014). Core Networks Corridor Progress Report of the European Coordinators.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 90
European Commission (2015). Directorate General for Mobility and Transport: Atlantic Work Plan of the European Coordinator Carlo Secchi. European Commission (2015). Directorate General for Mobility and Transport: North Sea Mediterranean Work Plan of the European Coordinator Péter Balázs. European Commission (2015). Directorate General for Mobility and Transport: Rhine Alpine Work Plan of the European Coordinator Ana Palacio, Paweł Wojciechowski. European Commission (2015). Directorate General for Mobility and Transport: Rhine Danube Work Plan of the European Coordinator Karla Peijs. European Commission (2015). Directorate General for Mobility and Transport: Work Plan for North Sea Baltic Corridor Work Plan of the European Coordinator Catherine Trautmann. European Commission (2015). Baltic Adriatic: Work Plan of the European - Coordinator Kurt Bodewig. European Commission (2015). Mediterranean: Work Plan of the European - Coordinator Laurens Jan Brinkhorst. European Commission (2015). Orient East Med: Work Plan of the European Coordinator Mathieu Grosch. European Commission (2015), EUTransport in figures, Statistical pocketbook 2015. Luxembourg: Publications Office of the European Union. European Commission (2016). The Scandinavian Mediterranean Corridor: Second Work Plan of the European Coordinator Pat Cox. Eurostat (2010). Illustrated Glossary for Transport Statistics (4th edition). Luxembourg: Publications Office of the European Union. Fontaras G., Rexeis M., Dilara P., Hausberger S., Anagnostopoulos K. (2013), The Development of a Simulation Tool for Monitoring Heavy-
Duty Vehicle CO2 Emissions and Fuel Consumption in Europe, SAE. ICCT, TNO, TUV Nord (2014). Cost-Benefit Analysis of Options for Certification, Validation and Monitoring and Reporting of HDVs. Christian Kille, Martin Schwemmer, Christian Reichenauer (2015). TOP 100 in European Transport and Logistics Services. Hamburg: DVV Media Group GmbH. Mock P. (2016). The automotive sector in Turkey, a baseline analysis of vehicle fleet structure, fuel consumption and emissions. The International Council of Clean Transportation, White Paper (March 2016) (pag. 39).
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 91
Oberhausen J. (2003), Trends in European Road Freight Transport, Transport Statistics Units, Eurostat, European Commission, Association for European Transport. Osservatorio Contract Logistics School of Management Politecnico di Milano (2015). La logistica fisica chiave di successo del mondo virtuale? Milano. M. Pregl, A. Perujo, P. Bonnel (2008). Technical, operational and logistical parameters influencing emissions of heavy duty vehicles, Based on real-world emission measurements of HDV along the extended Trans-European transport CORRIDOR V. JRC Savvidis, D (2014). Presentation of Vehicle Energy consumption calculation tool. DG Clima, Transport and Ozone. Stakeholder Meeting, Brussels. Savvidis D. (2016). The future role of trucks for energy and environment. European Commission, DG Climate Action, Unit C4: Transport. JRC and IEA Workshop. Brussels. Simcic G. (2012), Implementing eco-driving on a wide scale, Road Safety Day, Cyprus SmartDrive (2013), Fuel Effciency Study: Work Truck Fleets SmartDrive (2011), Trucking Fuel Study TIAX (2011), "European Union Greenhouse Gas Reduction Potential for Heavy-Duty Vehicles"
Websites http://ec.europa.eu/eurostat http://www.eea.europa.eu/ http://www.istat.it/ http://www.turkstat.gov.tr/Start.do http://www.acea.be/
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 92 http://www.ecomove-project.eu/ http://www.transformers-project.eu/ http://europa.eu/rapid/press-release_MEMO-14-366_en.htm http://transportpolicy.net/index.php?title=EU:_Heavy-duty:_Emissions/
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 93
Annex 1: Details of representative transport missions of Eliadis
Route 1a
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K7 11/2/2016 10:17 16:12 298.2 4:21:14 1:35:04 0
Max Road network usage Cargo Total fuel Fuel speed Average Highway weight consumption consumption (km/h) speed (km/h) Urban (km) Rural (km) (km) (tn) (lt) (lt/100km) 3.1 59.4 235.7 94 71 10 74.27 24.90 1% 20% 79%
1st post (11:21) 2nd post (12:51) 3rd post (13:51) Aggressiveness Acceleration Count (#) 31 Aggressiveness Acceleration Count (#) 27 Aggressiveness Acceleration Count (#) 84 Aggressiveness Acceleration Duration (s) 1023.8 Aggressiveness Acceleration Duration (s) 930.8 Aggressiveness Acceleration Duration (s) 1584.6 Aggressiveness Braking Count (#) 36 Aggressiveness Braking Count (#) 30 Aggressiveness Braking Count (#) 112 Aggressiveness Braking Duration (s) 166.8 Aggressiveness Braking Duration (s) 121 Aggressiveness Braking Duration (s) 354.4 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 970 RPM Green Zone Duration (s) 2789 RPM Green Zone Duration (s) 2765 Idling Duration (s) 66 Idling Duration (s) 538 Idling Duration (s) 131 Low Speed Duration (s) 602 Low Speed Duration (s) 379 Low Speed Duration (s) 1438 High Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 2031 Fuel consumption (lt) (10:17-11:17) 7.09 Fuel consumption (lt) (12:30-13:40) 27 Fuel consumption (lt) (13:40-14:40) 30.28
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 94
4th post (15:02) 5th post (16:02) 6th post (16:19) Aggressiveness Acceleration Count (#) 14 Aggressiveness Acceleration Count (#) 57 Aggressiveness Acceleration Count (#) 19 Aggressiveness Acceleration Duration (s) 428 Aggressiveness Acceleration Duration (s) 1153.6 Aggressiveness Acceleration Duration (s) 386.2 Aggressiveness Braking Count (#) 18 Aggressiveness Braking Count (#) 70 Aggressiveness Braking Count (#) 28 Aggressiveness Braking Duration (s) 53.2 Aggressiveness Braking Duration (s) 219.8 Aggressiveness Braking Duration (s) 68.2 RPM Violation Count (#) 0 RPM Violation Count (#) 2 RPM Violation Count (#) 0 RPM Violation Duration (s) 1 RPM Violation Duration (s) 1 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 3558.5 RPM Green Zone Duration (s) 2640 RPM Green Zone Duration (s) 97 Idling Duration (s) N/A Idling Duration (s) 326 Idling Duration (s) 473 Low Speed Duration (s) 0 Low Speed Duration (s) 1011 Low Speed Duration (s) 561 High Speed Duration (s) 3538 High Speed Duration (s) 2263 High Speed Duration (s) 4 Fuel consumption (lt) (14:40-15:15 last 9.9 Fuel consumption (lt) N/A Fuel consumption (lt) N/A CAN BUS post for consumption)
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 95
Route 1b
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K7 20/5/2016 9:32 13:54 237.4 3:10:09 1:07:33 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 2.0 7.1 228.3 90 77 10 84.22 35.47 1% 3% 96%
1st post (9:32) 2nd post (11:37) 3rd post (12:37) Aggressiveness Acceleration Count (#) 35 Aggressiveness Acceleration Count (#) 29 Aggressiveness Acceleration Count (#) 15 Aggressiveness Acceleration Duration (s) 1158.6 Aggressiveness Acceleration Duration(s) 1287 Aggressiveness Acceleration Duration (s) 550.6 Aggressiveness Braking Count (#) 42 Aggressiveness Braking Count (#) 30 Aggressiveness Braking Count (#) 11 Aggressiveness Braking Duration (s) 184.6 Aggressiveness Braking Duration (s) 121 Aggressiveness Braking Duration (s) 18.2 RPM Violation Count (#) 71 RPM Violation Count (#) N/A RPM Violation Count (#) N/A RPM Violation Duration (s) N/A RPM Violation Duration (s) 844 RPM Violation Duration (s) N/A RPM Green Zone Duration (s) 2424.5 RPM Green Zone Duration (s) 854.5 RPM Green Zone Duration (s) 788.5 Idling Duration (s) 538 Idling Duration (s) 1675 Idling Duration (s) 690 Low Speed Duration (s) 375 Low Speed Duration (s) 803 Low Speed Duration (s) 130 High Speed Duration (s) 2687 High Speed Duration (s) 786 High Speed Duration (s) 2780 Fuel consumption (lt) (10:37-11:37) 29.28 Fuel consumption (lt) (9:32-10:37) 8.12 Fuel consumption (lt) (11:37-12:37) 21.82
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 96
4th post (13:37) 5th post (13:50) Aggressiveness Acceleration Count (#) 14 Aggressiveness Acceleration Count (#) 16 Aggressiveness Acceleration Duration (s) 290.6 Aggressiveness Acceleration Duration (s) 435.2 Aggressiveness Braking Count (#) 11 Aggressiveness Braking Count (#) 23 Aggressiveness Braking Duration (s) 41.8 Aggressiveness Braking Duration (s) 102.6 RPM Violation Count (#) N/A RPM Violation Count (#) 86 RPM Violation Duration (s) N/A RPM Violation Duration (s) 9447 RPM Green Zone Duration (s) 65 RPM Green Zone Duration (s) 448 Idling Duration (s) 0 Idling Duration (s) 171 Low Speed Duration (s) 90 Low Speed Duration (s) 447 High Speed Duration (s) 3510 High Speed Duration (s) 195 Fuel consumption (lt) (12:37-13:50) 25 Fuel consumption (lt) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 97
Route 1c
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K3 5/8/2016 11:00 16:49 275.9 3:31:39 2:05:44 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 1.2 2.0 272.6 93 83 10 N/A N/A 0.4% 0.7% 98.8%
1st post (11:16) 2nd post (11:56) 3rd post (14:17) Aggressiveness Acceleration Count (#) 0 Aggressiveness Acceleration Count (#) 31 Aggressiveness Acceleration Count (#) 36 Aggressiveness Acceleration Duration (s) 1012 Aggressiveness Acceleration Duration (s) 1701.6 Aggressiveness Acceleration Duration (s) 0 Aggressiveness Braking Count (#) 32 Aggressiveness Braking Count (#) 25 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Duration (s) 271.6 Aggressiveness Braking Duration (s) 158.2 Aggressiveness Braking Duration (s) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 2 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 1.5 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 892 RPM Green Zone Duration (s) 2865 RPM Green Zone Duration (s) 0 Idling Duration (s) 132 Idling Duration (s) 457 Idling Duration (s) 753 Low Speed Duration (s) 750 Low Speed Duration (s) 404 Low Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 0 Fuel consumption (lt) (10:37-11:37) N/A Fuel consumption (lt) (11:37-12:37) N/A Fuel consumption (lt) (9:32-10:37) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 98
4th post (15:34) 5th post (16:34) Aggressiveness Acceleration Count (#) 14 Aggressiveness Acceleration Count (#) 46 Aggressiveness Acceleration Duration (s) 592.8 Aggressiveness Acceleration Duration (s) 1287.2 9 Aggressiveness Braking Count (#) Aggressiveness Braking Count (#) 35 59 Aggressiveness Braking Duration (s) Aggressiveness Braking Duration (s) 160 0 RPM Violation Count (#) RPM Violation Count (#) 0 0 RPM Violation Duration (s) RPM Violation Duration (s) 0 0 RPM Green Zone Duration (s) RPM Green Zone Duration (s) 3141.5 Idling Duration (s) 0 Idling Duration (s) 0 117 Low Speed Duration (s) Low Speed Duration (s) 476 3482 High Speed Duration (s) High Speed Duration (s) 3122 Fuel consumption (lt) (12:37-13:50) N/A Fuel consumption (lt) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 99
Route 2a
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K8 12/1/2016 14:36 23:36 239.7 3:19:47 5:40:47 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 7.0 51.6 181.0 105 75 16 94.10 39.25 3% 22% 76%
1st post (14:42) 2nd post (15:17) 3rd post (17:13) Aggressiveness Acceleration Count (#) 2 Aggressiveness Acceleration Count (#) 16 Aggressiveness Acceleration Count (#) 0 Aggressiveness Acceleration Duration (s) 16 Aggressiveness Acceleration Duration (s) 396.8 Aggressiveness Acceleration Duration (s) 0 Aggressiveness Braking Count (#) 3 Aggressiveness Braking Count (#) 6 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Duration (s) 72.6 Aggressiveness Braking Duration (s) 56.2 Aggressiveness Braking Duration (s) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 4 RPM Green Zone Duration (s) 412.5 RPM Green Zone Duration (s) 0 Idling Duration (s) 166 Idling Duration (s) 0 Idling Duration (s) 192 Low Speed Duration (s) 136 Low Speed Duration (s) 234 Low Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 326 High Speed Duration (s) 0 Fuel consumption (lt) (14:36-17:36) 12.28 Fuel consumption (lt) (17:36-19:36) 18.5 Fuel consumption (lt) (19:36-20:36) 24.6
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 100
4th post (18:12) 5th post (19:29) 6th post (20:31) Aggressiveness Acceleration Count (#) 48 Aggressiveness Acceleration Count (#) 0 Aggressiveness Acceleration Count (#) 21 Aggressiveness Acceleration Duration (s) 1431.2 Aggressiveness Acceleration Duration (s) 0 Aggressiveness Acceleration Duration (s) 672.2 Aggressiveness Braking Count (#) 24 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 9 Aggressiveness Braking Duration (s) 146.6 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 46.6 RPM Violation Count (#) 1 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 3 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 2873.5 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 2345.5 Idling Duration (s) 176 Idling Duration (s) 712 Idling Duration (s) 940 Low Speed Duration (s) 309 Low Speed Duration (s) 0 Low Speed Duration (s) 162 High Speed Duration (s) 3041 High Speed Duration (s) 0 High Speed Duration (s) 2498 Fuel consumption (lt) (20:36-21:36) 11.81 Fuel consumption (lt) (21:36-23:36) 26.91 Fuel consumption (lt) N/A
7th post (21:01) 8th post (21:08) 9th post (22:28) Aggressiveness Acceleration Count (#) 10 Aggressiveness Acceleration Count (#) 0 Aggressiveness Acceleration Count (#) 90 Aggressiveness Acceleration Duration (s) 1306.2 Aggressiveness Acceleration Duration (s) 0 Aggressiveness Acceleration Duration (s) 2025.6 Aggressiveness Braking Count (#) 45.4 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 388 Aggressiveness Braking Duration (s) 8 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 62 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 53 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 4 RPM Green Zone Duration (s) 1444 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 2758.5 Idling Duration (s) 135 Idling Duration (s) 458 Idling Duration (s) 0 Low Speed Duration (s) 148 Low Speed Duration (s) 0 Low Speed Duration (s) 954 High Speed Duration (s) 1499 High Speed Duration (s) 0 High Speed Duration (s) 2301 Fuel consumption (lt) ( N/A Fuel consumption (lt) N/A Fuel consumption (lt) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 101
10th post (23:43) Aggressiveness Acceleration Count (#) 24 Aggressiveness Acceleration Duration (s) 585.4 Aggressiveness Braking Count (#) 80.2 Aggressiveness Braking Duration (s) 11 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 421.5 Idling Duration (s) 196 Low Speed Duration (s) 818 High Speed Duration (s) 15 Fuel consumption (lt) N/A
Route 2b
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 102
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h)
K3 13/5/2016 3:47 7:10 237.1 3:15:43 0:04:39 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 6.7 36.4 193.8 91 75 16 N/A N/A 3% 15% 82%
1st post (4:39) 2nd post (5:39) 3rd post (6:39) Aggressiveness Acceleration Count (#) 32 Aggressiveness Acceleration Count (#) 12 Aggressiveness Acceleration Count (#) 30 Aggressiveness Acceleration Duration (s) 2797 Aggressiveness Acceleration Duration (s) 1558.4 Aggressiveness Acceleration Duration (s) 3086.2 Aggressiveness Braking Count (#) 22 Aggressiveness Braking Count (#) 5 Aggressiveness Braking Count (#) 6 Aggressiveness Braking Duration (s) 78.6 Aggressiveness Braking Duration (s) 26 Aggressiveness Braking Duration (s) 29.6 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 2036 RPM Green Zone Duration (s) 3424.5 RPM Green Zone Duration (s) 3457.5 Idling Duration (s) 461 Idling Duration (s) 113 Idling Duration (s) 0 Low Speed Duration (s) 702 Low Speed Duration (s) 87 Low Speed Duration (s) 43 High Speed Duration (s) 2437 High Speed Duration (s) 3400 High Speed Duration (s) 3557 Fuel consumption (lt) N/A Fuel consumption (lt) N/A Fuel consumption (lt) N/A
4th post (7:13)
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 103
Aggressiveness Acceleration Count (#) 20 Aggressiveness Acceleration Duration (s) 1392.6 Aggressiveness Braking Count (#) 25 Aggressiveness Braking Duration (s) 128.2 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 1325 Idling Duration (s) 270 Low Speed Duration (s) 533 High Speed Duration (s) 1214 Fuel consumption (lt) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 104
Route 2c
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K7 27/9/2016 12:52 21:16 236.714 3:18:02 5:06:28 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 1.2 17.6 217.8 89 74 16 68.10 28.76 1% 7% 92%
1st post (13:45) 2nd post (14:08) 3rd post (16:23) Aggressiveness Acceleration Count (#) 35 Aggressiveness Acceleration Count (#) 17 Aggressiveness Acceleration Count (#) 6 Aggressiveness Acceleration Duration (s) 2702 Aggressiveness Acceleration Duration (s) 1075.6 Aggressiveness Acceleration Duration (s) 707.2 Aggressiveness Braking Count (#) 28 Aggressiveness Braking Count (#) 16 Aggressiveness Braking Count (#) 8 Aggressiveness Braking Duration (s) 115.6 Aggressiveness Braking Duration (s) 62.6 Aggressiveness Braking Duration (s) 37.6 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 0 Idling Duration (s) 500 Idling Duration (s) 105 Idling Duration (s) 1069 Low Speed Duration (s) 534 Low Speed Duration (s) 133 Low Speed Duration (s) 101 High Speed Duration (s) 2566 High Speed Duration (s) 1090 High Speed Duration (s) 2015 Fuel consumption (lt) (12:52-13:52) 18.88 Fuel consumption (lt) (13:52-15:52) 6.22 Fuel consumption (lt) (15:52-16:52) 15.59
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 105
4th post (17:52) 5th post (20:45) 6th post (21:22) Aggressiveness Acceleration Count (#) 12 Aggressiveness Acceleration Count (#) 29 Aggressiveness Acceleration Count (#) 11 Aggressiveness Acceleration Duration (s) 298.2 Aggressiveness Acceleration Duration (s) 872.2 Aggressiveness Acceleration Duration (s) 409.8 Aggressiveness Braking Count (#) 17 Aggressiveness Braking Count (#) 24 Aggressiveness Braking Count (#) 11 Aggressiveness Braking Duration (s) 62.6 Aggressiveness Braking Duration (s) 82.4 Aggressiveness Braking Duration (s) 66.6 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Count (#) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 0 RPM Green Zone Duration (s) 0 Idling Duration (s) 0 Idling Duration (s) 265 Idling Duration (s) 371 Low Speed Duration (s) 195 Low Speed Duration (s) 656 Low Speed Duration (s) 300 High Speed Duration (s) 627 High Speed Duration (s) 445 High Speed Duration (s) 122 Fuel consumption (lt) 16:52-17:52 23.59 Fuel consumption (lt) 17:52-20:52 3.82 Fuel consumption (lt) 20:49-21:16 1
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 106
Route 3a
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K8 11/2/2016 12:37 14:40 66.8 1:18:01 0:54:15 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 5.4 6.1 55.2 87 56.6 15 26.50 39.61 8% 9% 83%
1st post (13:17) 2nd post (14:28) 3rd post (14:40) Aggressiveness Acceleration Count (#) 42 Aggressiveness Acceleration Count (#) 90 Aggressiveness Acceleration Count (#) 38 Aggressiveness Acceleration Duration (s) 1132 Aggressiveness Acceleration Duration (s) 1508.8 Aggressiveness Acceleration Duration (s) 432.6 Aggressiveness Braking Count (#) 24 Aggressiveness Braking Count (#) 68 Aggressiveness Braking Count (#) 20 Aggressiveness Braking Duration (s) 158.8 Aggressiveness Braking Duration (s) 460 Aggressiveness Braking Duration (s) 124.6 RPM Violation Count (#) 0 RPM Violation Count (#) 1 RPM Violation Count (#) 1 RPM Violation Duration (s) 0 RPM Violation Duration (s) 1 RPM Violation Duration (s) 0.5 RPM Green Zone Duration (s) 1785 RPM Green Zone Duration (s) 1735 RPM Green Zone Duration (s) 432 Idling Duration (s) 1419 Idling Duration (s) 1157 Idling Duration (s) 0 Low Speed Duration (s) 631 Low Speed Duration (s) 1561 Low Speed Duration (s) 472 High Speed Duration (s) 1374 High Speed Duration (s) 882 High Speed Duration (s) 234 Fuel consumption (lt) (12:37-13:28) 11.28 Fuel consumption (lt) (13:28-14:28) 12.81 Fuel consumption (lt) (13:28-14:28) 2.41
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 107
Route 3b
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K8 24/5/2016 10:28 15:11 57.5 1:07:41 3:38:32 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 7.6 5.8 44.0
89 53 15 N/A N/A 10% 77% 13%
1st post (10:48) customs stop 3:20 duration 2nd post (15:01) Aggressiveness Acceleration Count (#) 21 Aggressiveness Acceleration Count (#) 129 Aggressiveness Acceleration Duration (s) 474.2 Aggressiveness Acceleration Duration (s) 1710 Aggressiveness Braking Count (#) 15 Aggressiveness Braking Count (#) 85 Aggressiveness Braking Duration (s) 156.4 Aggressiveness Braking Duration (s) 396.4 RPM Violation Count (#) 0 RPM Violation Count (#) 1 RPM Violation Duration (s) 0 RPM Violation Duration (s) 0.5 RPM Green Zone Duration (s) 509 RPM Green Zone Duration (s) 2940.5 371 Idling Duration (s) 237 Idling Duration (s) Low Speed Duration (s) 656 Low Speed Duration (s) 1279 High Speed Duration (s) 148 High Speed Duration (s) 1950 Fuel consumption (lt) N/A Fuel consumption (lt) N/A
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 108
Route 3c
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K8 8/7/2016 12:04 16:04 57.5 1:16:59 2:43:14 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 7.6 5.8 44.0 90 49.2 15 N/A N/A 13% 10% 77%
1st post (14:41) 2nd post (12:42) customs stop 3rd post (15:56) Aggressiveness Acceleration Count (#) 51 2:28h Aggressiveness Acceleration Count (#) 113 Aggressiveness Acceleration Duration (s) 1492 Aggressiveness Acceleration Count (#) 26.2 Aggressiveness Acceleration Duration (s) 2165.8 Aggressiveness Braking Count (#) 33 Aggressiveness Acceleration Duration (s) 1 Aggressiveness Braking Count (#) 69 Aggressiveness Braking Duration (s) 170.2 Aggressiveness Braking Count (#) 7.2 Aggressiveness Braking Duration (s) 378.4 RPM Violation Count (#) 4 Aggressiveness Braking Duration (s) 1 RPM Violation Count (#) 2 RPM Violation Duration (s) 33 RPM Violation Count (#) 0.5 RPM Violation Duration (s) 1 RPM Green Zone Duration (s) 888 RPM Violation Duration (s) 4.5 RPM Green Zone Duration (s) 2930 Idling Duration (s) 1813 RPM Green Zone Duration (s) 26.2 Idling Duration (s) 355 Low Speed Duration (s) 1644 Idling Duration (s) 115 Low Speed Duration (s) 1178 High Speed Duration (s) 143 Low Speed Duration (s) 53 High Speed Duration (s) 2067 Fuel consumption (lt) N/A High Speed Duration (s) 0 Fuel consumption (lt) N/A Fuel consumption (lt) N/A Route 4a
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 109
Distance Travel Stop Min speed Truck id Date Departure Arrival (km) duration duration (km/h) K7 7/7/2016 7:30 17:28 501.8 7:35:17 2:22:36 0
Max Road network usage Cargo Total fuel Fuel speed Average Rural Highway weight consumption consumption (km/h) speed (km/h) Urban (km) (km) (km) (tn) (lt) (lt/100km) 24.651 61.653 415.59 90 68.2 20 236.59 47.13 5% 12% 83%
1st post (7:58) 1:29hours stop 2nd post (11:18) 3rd post (12:18) Aggressiveness Acceleration Count (#) 37 Aggressiveness Acceleration Count (#) 44 Aggressiveness Acceleration Count (#) 21 Aggressiveness Acceleration Duration (s) 1082.2 Aggressiveness Acceleration Duration (s) 2615.6 Aggressiveness Acceleration Duration (s) 1507.8 Aggressiveness Braking Count (#) 34 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Duration (s) 119.6 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 0 RPM Violation Count (#) 69 RPM Violation Count (#) 42 RPM Violation Count (#) 31 RPM Violation Duration (s) 854 RPM Violation Duration (s) 3255.5 RPM Violation Duration (s) 3311 RPM Green Zone Duration (s) 801.5 RPM Green Zone Duration (s) 343.5 RPM Green Zone Duration (s) 288.5 Idling Duration (s) 154 Idling Duration (s) 3600 Idling Duration (s) 3600 Low Speed Duration (s) 1289 Low Speed Duration (s) 0 Low Speed Duration (s) 0 High Speed Duration (s) 190 High Speed Duration (s) 0 High Speed Duration (s) 0 Fuel consumption (lt) (7:30-7:58) 4.31 Fuel consumption (lt) (9:21-10:21) 25.5 Fuel consumption (lt) (10:21-11:21) 27.88
4th post (13:18) Aggressiveness Acceleration Count (#) 15
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 110
Aggressiveness Acceleration Duration (s) 887 5th post (14:18) 6th post (15:18) Aggressiveness Braking Count (#) 0 Aggressiveness Acceleration Count (#) 36 Aggressiveness Acceleration Count (#) 67 Aggressiveness Braking Duration (s) 0 Aggressiveness Acceleration Duration (s) 2059.4 Aggressiveness Acceleration Duration (s) 2623.2 RPM Violation Count (#) 13 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 0 RPM Violation Duration (s) 2726 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 0 RPM Green Zone Duration (s) 873.5 RPM Violation Count (#) 31 RPM Violation Count (#) N/A Idling Duration (s) 3600 RPM Violation Duration (s) 3248 RPM Violation Duration (s) N/A Low Speed Duration (s) 0 RPM Green Zone Duration (s) 349 RPM Green Zone Duration (s) 577 High Speed Duration (s) 0 Idling Duration (s) 3600 Idling Duration (s) 3600 Fuel consumption (lt) (11:21-12:21) 27.5 Low Speed Duration (s) 0 Low Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 0 Fuel consumption (lt) (12:21-13:21) 25.5 Fuel consumption (lt) (13:21-14:21) 40.72
7th post (16:18) 8th post (17:18) 9th post (17:24) Aggressiveness Acceleration Count (#) 71 Aggressiveness Acceleration Count (#) 74 Aggressiveness Acceleration Count (#) 8 Aggressiveness Acceleration Duration (s) 2896 Aggressiveness Acceleration Duration (s) 1678 Aggressiveness Acceleration Duration (s) 108.8 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Count (#) 0 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 0 Aggressiveness Braking Duration (s) 0 RPM Violation Count (#) N/A RPM Violation Count (#) N/A RPM Violation Count (#) 54 RPM Violation Duration (s) N/A RPM Violation Duration (s) N/A RPM Violation Duration (s) 992 RPM Green Zone Duration (s) 251 RPM Green Zone Duration (s) 1770 RPM Green Zone Duration (s) 77 Idling Duration (s) 3600 Idling Duration (s) 3600 Idling Duration (s) 349 Low Speed Duration (s) 0 Low Speed Duration (s) 0 Low Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 0 High Speed Duration (s) 0 Fuel consumption (lt) (14:21-15:21) 25.59 Fuel consumption (lt) (15:21-16:21) 40.19 Fuel consumption (lt) (16:21-17:28) 19.4
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 111
Annex 2: Codognotto Users’ survey details
COMPANY DATA Company Name ______
Address Street ______
ZIP code ______
City ______
Country ______
Company URL ______
Industry sector ______
% of road transport? Other modes?
Supply chain solutions offered? Logistics services? Internal or external?
Area of clients?
Logistics revenues? Logistics costs?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 112
Employees (full time equivalent) involved in logistics activities? Which activities?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 113
TRANSPORT and LOGISTICS DATA
Transport data – related to freight road transport
T1. Which are the most common transport mission of your company?
T2. Which is the average length of a representative transport mission of your company?
T3. Minimum and maximum length?
T4. How long is a typical transport mission (in terms of driving hours)?
T5. All your missions are FTL? Otherwise in which percentage?
T6. Time requested for terminal and warehousing operations?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 114
T7. Which is the average loading capacity?
T8. Type of clients/industrial sector?
Logistics data
Please indicate the importance of the following logistics parameters:
L1. ETA respected in %?
o least important o less important o indifferent o important o most important
L2. On time delivery
o least important o less important o indifferent o important o most important
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 115
L3. Perfect order fulfilment
o least important o less important o indifferent o important o most important
L4. What does your company use for quality metrics in logistics?
o cost saving o error free o reliability o timely delivery o other (please specify) ______
L5. How often is logistics reviewed critically at your company?
o daily o weekly o monthly o quarterly o other (please specify) ______
L6. How will you rank the influence of the following problem in logistics?
☐
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 116
☐ ☐ ☐ ☐ ☐
ADDED VALUE ELEMENTS IN TRASPORT MISSIONS
Environmental
Calculation of emission?
CO2? Certification system used? Which one?
Value perceived internally?
Value perceived externally (to clients)?
Fuel consumption
Monitoring of fuel consumption based on:
Policy applied in relation to the modifications of fuel prices? Frequency of monitoring? Tools used?
Alternative fuels
Are there alternative fuel vehicles used? If yes, how many? Which fuel?
If not, is it foreseen in the near future? Indicatively when?
If not why?
Efficiency
Empty trucks?
If yes, alternative actions taken into considerations?
To be completed
Monitoring the vehicle utilisation?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 117
Frequency?
Real-time? Which tools?
Monitoring the status of the infrastructure and weather conditions
Frequency?
Real-time?
Which tools?
Administrative constraints in typical transport missions:
Toll stations? Problems?
Border crossing? Problems?
Certified drivers
Investment of their skills?
Monitored differences in term of performances?
Better level offered?
Digitalisation
IT tools used?
GPS positioning and monitoring?
Track & Trace of freight?
New investment foreseen? If yes which ones? Collection of transport data
If yes, which ones?
Purposes? (Internal or external reporting?)
Frequency?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 118
Annex 3: ELIADIS Users’ survey details
1. How many trucks over 40 tn does your fleet have?
… trucks
2. What is the cargo type carried by the trucks? (percentage of the total cargo carried)? • Container: … % • Pallets: … % • Refrigerating products: … % • Liquid cargo: … % • Balk cargo: … % • Other: … %
3. What is the proportion of transport missions at national and international level (percentage of total missions)? • National carriage: … % • International carriage: … %
4. What is the road type used (percentage of total missions)? • Urban road network (including ring road, ports, airport, industrial areas etc.): … % • Secondary road network: … % • Highway (Egnatia Odos S.A., PATHE, vertical axes, etc.): … %
5. What is the reason for driving inside the urban road network (percentage of total transport missions)? • Railway Station: … %
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 119
• Port: … % • Airport: … % • Shipper Premises: … % • Customs Storage: … % • Other: … %
6. What is the traffic density in the route (percentage average)? • Light: … % • Average: … % • Heavy : … %
7. What are the weather conditions during the route (percentage averaging)? • Sunshine – Cloudiness: … % • Rain: … % • Fog: … % • Snow: … % • Strong winds: … % • Other: … %
8. What is the average speed for each type of road? • Urban road network (including ring road, ports, airport, industrial areas etc.): … km/h • Secondary road network: … km/h • Highway (Egnatia Odos S.A., PATHE, vertical axes, etc.): … km/h
9. What is the percentage of stop-and-go facilities (tolls, traffic lights) during a route (percentage % for each type of road)? • Urban road network (including ring road, ports, airport, industrial areas etc.): … %
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 120
• Secondary road network: … % • Highway (Egnatia Odos S.A., PATHE, vertical axes, etc.): … %
10. What is the average distance (km) for each route? • Short route: … km • Long route: … km
11. What is the average duration (in hours) for each route? • Short route: … hours • Long route: … hours
12. What is the proportion of driving on roads with different altitude (slope) during a route on the secondary road network (percentage of total missions)? … % 13. What is the average fuel consumption per 100 km (liters)? • Urban road network (including ring road, ports, airport, industrial areas etc.): …liters • Secondary road network: …liters • Highway (Egnatia Odos S.A., PATHE, vertical axes, etc.): … liters
14. What is the average load (tn) per route?
…tn
15. For what percentage of the route is the Air-conditioning activated during the warm days of the year (duration in hours in average route) ?
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 121
… hours
Thank you for your time.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 122
Annex 4: TEN-T CORRIDORS
1. TEN-T Network Nine core network corridors have been defined, each of them involving between four and nine different Member States and featuring the full range of transport modes. To make sure that the corridors are developed effectively and efficiently, each corridor is led by a European Coordinator who stimulates and coordinates action along the respective corridor. In addition, the European Commission has nominated European Coordinators for two horizontal priorities: the European Rail Traffic Management System (ERTMS) and Motorways of the Sea (MoS). In order to stimulate the development of the TEN-T network, the Connecting Europe Facility has been put in place. A budget of 26 billion EUR has been dedicated for its implementation, notably the core network corridors being a strong implementation instrument of the new transport guidelines.
Figure 53: Ten-T Network
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 123
The 9 Ten-T Corridors are as follows: • Baltic-Adriatic Corridor • North Sea-Baltic Corridor • Mediterranean Corridor • Orient/East-Med Corridor • Scandinavian-Mediterranean Corridor • Rhine-Alpine Corridor • Atlantic Corridor • North Sea-Mediterranean Corridor • Rhine-Danube Corridor
2. Corridors Baltic-Adriatic Corridor Characteristics of the Baltic-Adriatic Corridor The Baltic-Adriatic core network corridor alignment and infrastructure are defined by Regulations (EU) 1315/2013 and 1316/2013. Involving six Member States (Poland, Czech Republic, Slovakia, Austria, Italy and Slovenia), the corridor connects the Baltic ports of Gdynia/Gdańsk and Szczecin/Świnoujście with the Adriatic ports of Trieste, Venezia, Ravenna and Koper. The corridor will thus provide better access to these seaports for the economic centres along the corridor. The 1,800 km long Baltic-Adriatic Corridor allows for more possible itineraries between the Baltic and Adriatic basins: from North to South, either starting in the ports of Szczecin and Świnoujście, via Poznan and Wroclaw, or in the ports of Gdynia and Gdańsk directly to Katowice or through Warszawa and Łódź, the corridor interconnects the Polish core urban and logistics nodes to the ones located in the Czech Republic, Slovakia and Austria, reaching Wien through Bratislava or Ostrava. The corridor road and rail links continue from Austria towards the Adriatic ports of Koper, Trieste, Venezia and Ravenna via Ljubljana in Slovenia or via Udine, also passing through Venezia and Bologna in Italy. The corridor encompasses a total of 13 urban nodes and airports, 10 ports and nearly 30 rail-road terminals. The backbone of the Baltic-Adriatic axis is based on railway and road routes. Indeed, it is one of the few corridors that do not include inland waterways, even though the corridor interconnects with the inland waterway TEN-T core network at various sections. Its railway network corresponds mostly to the Baltic-Adriatic Rail Freight Corridor. This corridor has intersections with five other corridors. In Poland, the corridor is crossed by the North-Sea Baltic Corridor in West-East direction and in the Czech Republic, Austria and Slovakia by the Orient-East Med and Rhine-Danube Corridors. Further South - in Italy and Slovenia - the corridor runs for large parts in parallel to the Mediterranean Corridor. Finally, there is one intersection with the Scandinavian-Mediterranean Corridor between
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 124
Bologna and Faenza along the Bologna-Ravenna rail itinerary, also including the Bologna urban and logistics nodes.
Figure 54: Baltic-Adriatic Corridor
Road Network
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 125
The 3,600 km road infrastructure on the Baltic-Adriatic Corridor does not fully comply with the requirements of the Regulation (EU) 1315/2013 either, i.e. in what concerns the type of infrastructure and parking areas. The situation is particularly relevant for the Polish road network, whereas the corridor infrastructure in Italy and Slovenia is fully compliant. Currently, 19% of the road corridor infrastructure is constituted by ordinary roads which do not comply with the requirements.
Figure 55: Extension of the non-compliant road infrastructure in km and % of the total length
Results of the transport market study
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 126
Road traffic data for international goods show that the dominant relationships in 2010 are between the Czech Republic, Poland and Slovakia. The most important relationship is however the one between Italy and Austria. Italy represents the main destination of road transported freights between the Baltic Adriatic Corridors’ Member States.
Table 14: International Road Freight Flows in 1,000 tonnes
More than 115 M tonnes of freight volumes were transported by road and rail between Poland, Czech Republic, Slovakia, Austria, Italy and Slovenia. The modal share for railways is 42%, with Italy registering the lowest percentages and Austria representing the main destination for rail traffic among the Baltic- Adriatic Corridor concerned Member States. Four main scenarios were developed for the prognosis of the rail and road performance, gradually introducing different assumptions on a step-by-step basis, thus allowing for the separate assessment of their effects. • 2014 (current scenario) – describing the interaction of the current travel and transport demand and the current corridor infrastructure; • 2030T (do-nothing scenario at 2030) – describing the interaction of the travel and transport demand at 2030 with the current corridor infrastructure (as for the 2014 scenario); • 2030WP (work plan scenario at 2030) – describing the interaction of the travel and transport demand at 2030 (as for the 2030T scenario) and with the corridor infrastructure improved based on the major rail and road investments included in the project list annexed to this work plan;
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 127
• 2030RP (rail policy scenario at 2030) – describing the interaction of the travel and transport demand at 2030 with the corridor infrastructure improved based on the major rail and road investments included in the project list annexed to this work plan (as in scenario 2030WP), combined with policy and administrative measures aimed at reducing by 20% the generalised transport cost of the rail mode compared to the road transport (such as the internalisation of the total transport costs, the promotion of more attractive rail services, the effect of the on-going liberalisation process in railways and the fourth railway package, the removal of administrative and operational barriers). This last assumption does not constitute an assessment of the likely impact of these measures, but it is only aimed at providing an indication about the magnitude of the possible modal shift and its implication on the available rail capacity on the corridor.
Figure 56: Performance and modal share of the Baltic-Adriatic transport modes (millions of tons*km/year)
Flows and capacity on the road network Figure 57 shows that current road flows are generally below the critical level - set in this analysis at 20,000 vehicles per day per lane.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 128
Taking into account that road infrastructure can also operate above this traffic level (although with reduced efficiency in terms of congestion), capacity is not a general issue for the corridor. The only section currently above the identified critical level is within the urban area in Bratislava, where projects for a new external by-pass are being developed – although not included in the Baltic-Adriatic corridor alignment.
Figure 57: Intensity of road transport (2014, veh/day/lane)
Figure 58 shows that, as a result of the improvement of the infrastructure, the flows on the road infrastructure are expected to grow significantly in the time plan horizon, although
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 129
this effect might be mitigated by improvements of the rail infrastructure and implementation of modal shift measures.
Figure 58: Intensity of road transport (vehicles/day)
North Sea-Baltic Corridor Characteristics of the North Sea-Baltic Corridor The 3200 km long North Sea-Baltic Corridor is one of these nine core network corridors and the only one to be situated exclusively in the North of Europe. It joins the Baltic Sea Region with the low countries of the North Sea Region by way of Helsinki, the Baltic States, Poland and Germany. While there is strong traffic in the western end of the corridor from the four largest ports in Europe (Rotterdam, Antwerp, Hamburg and Amsterdam) to the hinterland of the Low Countries and Germany up to Berlin, the flow then lessens from Berlin to Warsaw and, for rail at least, the connection with the Baltic States to the North from Poland is underdeveloped, although the maritime connection between Helsinki and Tallinn works efficiently. The North Sea-Baltic Corridor involves eight Member States connecting the Baltic Sea ports of Helsinki (FI), Tallinn (EE), Riga, Ventspils (LV) and Klaipeda (LT) with the North Sea ports of Hamburg (DE), Bremen (DE), Amsterdam, Rotterdam (NL) and Antwerp (BE). The 3200 km long multimodal corridor connects the capitals of all the Member States through which it passes: Helsinki (FI), Tallinn (EE), Riga (LV), Vilnius (LT), Warsaw (PL),
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 130
Berlin (DE), Brussels (BE) and Amsterdam (NL). Among the 17 urban nodes there are 12 multi-corridor urban nodes: Helsinki (2 Corridors), Warsaw (2), Poznan (2), Berlin (3), Hamburg (3), Bremen (3), Hannover (3) Cologne (2) Brussels (3) Antwerp (3) Rotterdam (3) and Amsterdam (3). The corridor has 16 core network airports, 13 maritime ports, 18 inland ports and 17 rail- road terminals. It also involves 40 EU Regions. It has therefore a potential to become one of the most economically diverse Corridors in the European Union.
Figure 59: North Sea-Baltic Corridor
Road Network The main road link in Belgium from Brussels to the German border has six lanes, except for the last 20 km. Also, the motorway link to The Netherlands has six lanes, except for the last 40km from Antwerp to the Dutch border which has four lanes. Although the existing road network in Belgium meets the requirements of Regulation 1315/2013 and the capacity is quite high, there are major concerns with congestion. The Dutch motorway network on the Corridor has four lanes on most sections. Plans exist to widen some more congested sections. The Dutch government gives a high priority to using more Intelligent Transport Solutions. The motorways on the most used sections between
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 131
Amsterdam and Rotterdam are six or eight lanes. Despite the high capacity of the motorway network, congestion is still a major concern. Almost all road sections on the Corridor in Germany are part of the German motorway system. There is a short section of around 10km on the A30 near Bad Oeynhausen where the motorway is missing. Another bottleneck on the German motorway system is the Berlin ring with only four lanes due to temporary capacity problems. The Polish road network from the German border to Warsaw is a new four lane motorway, the A2. From Warsaw towards the Belarus border it is mainly a two lane national road. No further extension is currently foreseen. The connection from Warsaw to Lithuania is also mainly a two-lane national road; an expressway is in the planning stage. The Via Baltica highway is the main artery for North-South traffic between Poland and the Baltic States. At the moment along the future route of the Via Baltica there is a clear shortage of high quality infrastructure which results in congestion. In Lithuania the Via Baltica road has two lanes, except for a section of 20 km North of Kaunas which has four lanes. The East-West connection from Klaipeda port through Kaunas to Vilnius is a four lane conventional road. In Latvia the Via Baltica is a two lane road with capacity problems on the Riga bypass in Baltezers, Iecava and Bauska, where some sections require widening the road from two lanes to four (including construction of bypasses). Bearing in mind the deficiencies of the Riga traffic system –lack of capacity, and a highly fragmented character - new traffic infrastructure must be created in order to have a reliable TEN-T link (last mile) and extend the TEN-T network to Riga port. Except for the Riga ring road, the road to Ventspils port has also two lanes. In Estonia, most of the Via Baltica has two lanes, except the last 25 km to the capital Tallinn. The main problems of Via Baltica relate to road safety. Results of the transport market study For the first time, a transport market study was carried out for the whole Corridor. It assessed transport demand and the resulting traffic flows as well as the capacity of the infrastructure. The current situation and the forecast for 2030 were looked at Table 15.
Table 15: Modal split of corridor-related international freight transport flows by country in 2010
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 132
At the national level there is a very high dominance of road transport (69%) in the countries along the Corridor. For the corridor-related freight traffic, the picture is more balanced - expressed in freight tonnage; inland waterways accounts for the highest volumes, whereas rail traffic is only very limited. Inland waterways are only relevant in the western part of the Corridor, whereas in the Baltic States and Finland short-sea-shipping is by far most important mode of transport. At the national level, rail transport takes the biggest share in the Baltic States and short-sea- shipping is important for Finland, the Baltic States, Belgium and The Netherlands. In Poland, the dominance of road in international traffic is very clear. Rail has a bit bigger share in domestic freight, but also here the dependence on road is very high. Germany has the most balanced modal split for international traffic. The most substantial freight flows on the Corridor are in the western section between Germany, Belgium and The Netherlands. The numbers indicate that almost 70% of the total freight on the corridor is transported by road; only 11% by rail and 10% each for IWW and short sea shipping. 2.2 million tonnes of freight transported by IWW is however a significant amount despite being only a fraction of road transport underlining the fact that IWW forms a developed network from the North Sea coast as far as Berlin and in the Netherlands 30% of freight is transported by IWW and 29% in Belgium. In Poland 83% of total freight is transported by road which is a very high figure indicating the difficulties with rail freight in the country and the development of a modern road network. Road transport is also high in Germany and Finland. Only the three Baltic States have a higher rail freight component than road indicating the large quantities of oil
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 133
and other bulk freight coming from Russia and the east. However, no volumes are currently being transported north-south by rail. Estimates for the year 2030 show an increasing share for IWW, continuing heavy road and rail use in the western part of the corridor and continuing strong east west flows in the Baltic States using primarily rail.
Table 16: Freight transported on the North-Sea Baltic Corridor in 2012 (x 1000 Tonnes)
Table 17: Freight transported on the North Sea – Baltic Corridor in 2012 in %
Mediterranean Corridor Characteristics of the Mediterranean Corridor
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 134
The Mediterranean Corridor is the main east-west axis in the TEN-T network south of the Alps. It runs between the south-western Mediterranean region of Spain and the Ukrainian border with Hungary, following the coastlines of Spain and France and crossing the Alps towards the east through Italy, Slovenia and Croatia and continuing through Hungary up to its eastern border with Ukraine. The main branches of the Mediterranean Corridor are identified in Annex I of Regulation (EU) 1316/2013 as follows: • Algeciras – Bobadilla – Madrid – Zaragoza – Tarragona; • Sevilla – Bobadilla – Murcia; • Cartagena – Murcia – Valencia – Tarragona; • Tarragona – Barcelona – Perpignan – Marseille/Lyon – Torino – Novara – Milano – Verona – Padova – Venezia – Ravenna/Trieste/Koper - Ljubljana – Budapest; • Ljubljana/Rijeka – Zagreb – Budapest – UA border. The Mediterranean Corridor is intersecting with the Atlantic corridor in Spain (Algeciras- Madrid), with the North Sea-Mediterranean Corridor in France (Marseille-Lyon), with the Rhine-Alpine Corridor in Italy (Novara/Milano), with the Scandinavian-Mediterranean Corridor in Italy (Verona), with the Baltic-Adriatic Corridor in Italy and Slovenia, with the Rhine-Danube Corridor in Croatia and Hungary and with the Orient-East Med Corridor in Hungary.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 135
Figure 60: Mediterranean Corridor
Road Network The total length of the road network included in the Mediterranean corridor is about 5500 km, with Spain covering more than 50% of the entire corridor. As regards the parameter “Motorway or Express roads”, all countries are compliant. More specifically, only a few sections are not motorways: the western part of Spain (ex. Motril – Playa Cambriles, Motril-Nerja) and the Hungarian section close to the Ukrainian border. The Italian border sections with Slovenia and France are express roads. Results of the transport market study The six corridor countries exchanged nearly 160 million tons of goods in 2010. The main flows are between Spain and France (45 million tons), and between France and Italy (36 million tons). These two flows represent 60% of the goods exchanged between the six corridor countries (in terms of weight). As shown in the Table 18 below the overall modal split for international freight flows between these countries is 66% for road, 9% for rail and 25% for maritime transport. More than two thirds of the goods exchanged between Spain and Italy are transported by sea.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 136
Table 18: Total freight demand between corridor countries in 2010
In the “market area” of the corridor the freight flows (excluding maritime transport) for 2010 are shown in Table 19.
Table 19: Freight flows in the corridor’s market area in 2010 (1000 tons / year)
The freight flows in the “market area” of 150 million tons are of the same order as the freight flows within the corridor. The rail share is slightly higher in the market area as compared to the freight flows between the corridor countries, but remains at a relatively low level when compared to other international flows in Europe. An analysis of the trade flows shows that • Corridor countries have strong cross-border exchange flows at regional level with each other and with the rest of Europe; in particular Catalonia and Lombardy appear as the predominant generators of trade flows;
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 137
• Road is the dominant mode for flows between corridor regions, while rail takes a higher share in cross-Alpine freight (in a north – south direction) and in the eastern part of the corridor. According to the Corridor Study the total demand in the market area of the corridor would increase from 151 million tons in 2010 to 267 million tons in 2030, with an average annual growth rate of 2,9%. With the full implementation of the corridor, the rail market share could potentially increase up to 27%, reaching about 72 million tons a year. Table 20 summarizes the forecasting results for the corridor's market area.
Table 20: Forecasting results for the corridor's market area
Orient/East-Med Corridor Characteristics of the Orient/East-Med Corridor The Orient/East-Med Corridor is a long north west – south eastern corridor which connects Central Europe with the maritime interfaces of the North, Baltic, Black and Mediterranean seas. It runs from the German ports of Bremen, Hamburg and Rostock via the Czech Republic and Slovakia, with a branch through Austria, further via Hungary and Romania to the Bulgarian port of Burgas, with a link to Turkey, to the Greek ports of Thessaloniki and Piraeus and a "Motorway of the Sea" link to Cyprus. It comprises rail, road, airports, ports, rail-road terminals and the Elbe river inland waterway. The 9 Member States involved are (in alphabetical order): Austria, Bulgaria, Cyprus, Czech Republic, Germany, Greece, Hungary, Romania, and Slovak Republic. In Cyprus, no rail infrastructure is deployed. Maritime infrastructure exists in 4 countries, namely Bulgaria, Cyprus, Germany and Greece. According the Regulation No. 1316/20131 the Orient / East- Med corridor (OEM corridor) and clarifications agreed with the Member States consists of the following parts: • Rostock - Berlin • Brunsbüttel – Hamburg – Berlin – Dresden • Bremerhaven / Wilhelmshaven – Magdeburg – Leipzig / Falkenberg – Dresden • Dresden – Ústí nad Labem – Mělník/Praha – Kolín
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 138
• Kolín – Pardubice – Brno / Přerov – Wien/Bratislava – Győr – Budapest – Arad – Timişoara – Craiova – Calafat – Vidin – Sofia • Sofia – Plovdiv – Burgas • Plovdiv – Svilengrad - BG/TR border • Sofia – Thessaloniki – Athina – Piraeus • Athina – Patra / Igoumenitsa • Thessaloniki / Palaiofarsalos – Igoumenitsa • Piraeus – Heraklion – Lemesos – Lefkosia - Larnaka The length of the corridor infrastructure sums up to approximately 5.900 km (rail), 5.600 km (road) and 1.600 km of IWW. The number of core urban nodes along the Orient/East Med corridor is 15, with the majority located in Germany (5) and Greece (3), as well as one per other Member State. The same number applies for core airports, from which 6 are dedicated airports to be connected with high-ranking rail and road connections until 2050. Furthermore, 10 Inland ports and 12 Maritime ports are assigned to the corridor, as well as 25 Road-Rail terminals. Several segments of the Orient/East Med Core Network Corridor are coinciding with other of the 9 Core network corridors, such as the Rhine-Danube Corridor (approx. 1000 km) and on shorter sections, the North Sea / Baltic corridor, the Scandinavian-Mediterranean corridor and the Baltic Adriatic corridor.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 139
Figure 61: Orient/East-Med Corridor
Road Network The road infrastructure covers all the nine OEM countries with a total distance between Wilhelmshaven and Lefkosia of 4682 km on average and a total length of road network of approximately 5644 km. The majority of the road sections are of Motorways / Express roads class (82%). The main bottlenecks identified along the OEM Road network are those related to non-compliant road classes, namely roads without level-free junctions (mainly single lane). These include small sections in the Czech Republic and Austria; whereas the issue is particularly prominent in Romania, Bulgaria, and to a lesser extent in Greece and Cyprus (Lefkosia south orbital). It should be noted, based on the outcome of the Corridor study that there are several sections, where construction works are under way and part of the identified bottlenecks will be alleviated in the 2014-2015 period. The average weighted daily number of trucks per OEM corridor road section is about 3,150 and the respective number of cars is 19,000. The most freight traffic intensive sections are located in the German and Hungarian territory. Road sections near urban agglomerations that carry high number of passengers are located in Greece, Germany, Czech Republic and
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 140
Hungary. The overall average capacity utilisation ratio for the OEM corridor sections, for which data are available, is about 44.5%. As a general characteristic of the entire road corridor, there is a high level of utilisation of the existing road capacity in and around the large cities. Results of the transport market study The first level of corridor traffic, that is, transport within the Corridor catchment area, has been described for the base year 2010. For freight transport, the domestic transport has been included. Notably for road transport the domestic transport is carried out on short distances. This is one of the reasons why the volumes for road are relatively high. The short distance transport by road is explained by a high share of building materials, foodstuffs, agricultural products and final products. This also concerns the last- or first mile transport related to long distance transport by rail or inland waterways, for example container transport. In the description and analysis the short distance transport has been separated from long distance transport. On the longer distance there is more competition between road versus rail and inland waterways. The second level (origin and destination in the corridor) and the third level (transit) of corridor traffic for rail and road transport have been considered, in both, tonnes and tonne- kilometres.. For rail, the first level traffic is subdivided in domestic and international traffic, and the second level in imports and exports. For road, the first level domestic traffic has been further split into domestic short distance and domestic long distance. The short distance transport is in general applicable for distances shorter than 80 kilometres. Also for inland waterways and maritime transport, forecasts for 2030 have been presented for land-land flows in the OEM corridor. For inland waterways, in total a growth of 25% is expected in the period 2010-2030, and for maritime transport of 14%. In the Table 21 the results for the forecasts are summarized.
Table 21: Freight transport volume between the OEM regions for 2010, 2030 reference scenario; in 1,000 tonnes
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 141
In the European reference scenario, the share for rail is expected to grow from 27.1% in 2010 to 30.8% in 2030, whilst the share of inland waterways is expected to decrease from 2.7% in 2010 to 1.9% in 2030. In view of the decrease for inland waterway transport in the reference scenario, particular attention needs to be given to support this mode of transport. These percentages increases are relative and represent the share of the global volume increasingly transported. If full compliance with TEN-T standards is achieved by 2030, the share of rail and inland waterways may be expected to increase.
Scandinavian-Mediterranean Corridor Characteristics of the Scandinavian-Mediterranean Corridor The Scan-Med Corridor is a crucial axis for the European economy, crossing almost the whole continent from North to South. It encompasses seven EU Member States (Finland, Sweden, Denmark, Germany, Austria, Italy and Malta) and one Member State of the European Economic Area, Norway. It is the largest of the corridors in terms of core network length - with more than 9300 km of core rail and greater than 6300 km of core road network – together with 25 core ports, 19 core airports and 44 core rail-road terminals and 18 core urban nodes. The Scan-Med Corridor links the major urban centres in Germany and Italy to Scandinavia (Oslo, København, Stockholm and Helsinki) and the Mediterranean (Italian seaports, Sicily and Malta). It covers seven EU Member States and Norway and represents a crucial axis for the European economy, crossing almost the whole continent from North to South. "Linear" modes of transport that are assigned to the corridor are mainly rail and road. A few sections of the alignment, in particular the connections Finland – Sweden - Germany and Italy - Malta, cross the sea. The other dimension of the corridor is composed of "punctual" infrastructure: airports, seaports and rail-road terminals of the core network. For modal interconnection as well as the connection of the trans-European transport network with infrastructure for local and regional traffic, "urban nodes" are of specific importance.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 142
Figure 62: Scandinavian-Mediterranean Corridor
Road Network There are significant congestion problems on the road network around most large cities during peak-periods and these are generally taken into account in the national and regional plans for each country. Inter-urban roads have generally less congestion problems. The motivation for measures to improve the road infrastructure is not only based on the availability of physical capacity but also to ensure e.g. the smooth flow of traffic, to increase traffic safety or to avoid sensitive populated or environmental areas. In some cases, such as the Fehmarn Belt Fixed Link, there will be significant time-saving compared with the ferry alternatives or the longer route through Jutland. Other important measures which are not related to road infrastructure directly, such as regulations, technological improvements or improved vehicle capacity unitisation are also important. To address these measures cooperation is necessary between all interested partners involved, public as well as private. It is unlikely that the public sector will itself finance all necessary infrastructures (safe parking areas, filling stations etc.) but it can be active in the use of infrastructure and/or vehicle regulation in order to encourage or discourage transport choices by the infrastructure users. For private organisations, there needs to be a financial
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 143
benefit both in the long- and short-term in order to get involved. This is a complicated process that will require concentrated action. Results of the transport market study In the year 2010, the latest year for which disaggregated data could be retrieved, the international freight traffic on the corridor accounts for 129.0 million tons by sea, of which 59.9 million tons are between core ports, 50.3 million tons by road and 36.0 million tons by rail. As regards international road freight flows, the relations Denmark – Germany, Italy – Germany and Finland – Sweden (in both ways) are dominant with a share of almost 70 %. The structure of flows illustrates a broader spatial distribution of important relations on the corridor, locating the "gravity centre" of road freight volumes in the southern part of the corridor and to a lesser extent in the far northern part. The multi-modal transport market study pursues the goal to provide a “big picture” of the present and future situation of the transport market for the Scan-Med Corridor. According to this objective, a comprehensive overview including all relevant transport modes and infrastructure was presented. The basis for this general perspective is an extensive review of numerous studies, reports and forecasts investigating market sections and nodes of the corridor stemming from the existing databases and additional data provided by infrastructure managers, Ministries and other stakeholders. This reveals a comprehensive amount of data, subsequently gathered, edited and included into a large scale view on the traffic development of the Scan-Med Corridor. With this approach it was possible to identify core network areas with highest transport volume expected in the year 2030. With respect to rail, both passenger and freight, these are: Mjölby – Malmö, Göteborg – Malmö, Malmö – København – Taulov, Bremen/Hamburg – Hannover – Würzburg, München – Innsbruck, Bologna – Firenze – Roma – Napoli. With respect to road these are: Lübeck – Hamburg/Bremen – Hannover, Würzburg – Nürnberg – München, Firenze – Roma. The comparison of the expected traffic volumes and network loads in the year 2030 facilitates the identification of possible capacity constraints (bottlenecks). The overview for capacity constraints and capacity utilization provides the valuable indication that, even after the construction of new infrastructure (in particular Fehmarn Belt Fixed Link, Brenner Base Tunnel and their access lines), there will remain some bottlenecks along the Scan-Med Corridor that may impede future growth of passenger and freight transport. These can be found most notably • In Finland, for rail: Kouvola – HaminaKotka, Luumäki – Vainikkala, Helsinki, node, Helsinki – Turku; and for road: regions of Turku and Helsinki and the section Kotka–Hamina– Vaalimaa; • In Sweden, for rail: Stockholm and Göteborg node, Hässleholm – Lund, Trelleborg – Malmö (- København); • In Denmark, for rail: (Malmö-) København region;
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 144
• In Germany, for rail: Bremen/Hamburg - Hannover, Würzburg - Nürnberg, München area; and for road: regions of Hamburg, Hannover, Berlin and München as well as the section Würzburg – Nürnberg; • In Italy for rail, based on information provided by RFI: Verona - Ponte Gardena until the completion of the entire access lines to Brenner Base Tunnel; Firenze - Livorno/La Spezia related to the ports' traffic development; additionally there will be some constraints in the traffic of urban areas; • In Malta for the connection between the port of Marsaxlokk, the airport and the capital city with its port. • In Austria, no capacity problems are reported, after the infrastructure projects will have been completed. The most important and corridor relevant road freight flows in 2010 account for more than 70% of all international road freight flows, comprising nearly 49 M tonnes. The relations DK – DE, IT – DE and FI – SE, in both ways, are dominant. The first conclusion that can be drawn from the tables above is that – with the only exception of road flow between Sweden and Finland – all the other dominant trade lanes are related to Germany, thus Germany can be seen as the “turn table” for the whole corridor. On the basis of the available studies and forecasts it can be concluded that the Fehmarn Belt fixed link and the Brenner Base Tunnel are of outstanding importance for the functioning of the corridor in the future.
Table 22: International rail freight flows covering ScanMed corridor countries in 2010
Rhine-Alpine Corridor Characteristics of the Rhine-Alpine Corridor The Rhine-Alpine Corridor runs through the so-called “Blue banana,” which includes major EU economic centres such as Brussels and Antwerp in Belgium, the Randstad region in The Netherlands, the German Rhine-Ruhr and Rhine-Neckar regions, the Basel and Zürich regions in Switzerland, and the Milan and Genoa regions in Northern Italy. The Corridor
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 145
encompasses some of world's largest ports, like: Rotterdam, Amsterdam, Antwerp and Zeebrugge, which function as entry and exit points to the corridor and stand at the crossroads for multiple modes. Along the Rhine-Alpine Corridor, over 1 billion tonnes of freight are transported annually, resulting in a corridor GDP of more than 2.700 billion Euros (data from 2010). With 13% of EU's population, the Corridor regions generate 19% of the EU's GDP.
Figure 63: Rhine-Alpine Corridor
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 146
The Rhine-Alpine Corridor stretches from the northern seaports in The Netherlands and Belgium to the Mediterranean basin in Genoa, through the most important and economically strongest urban regions of Europe. Countries directly involved are: The Netherlands, Belgium, Germany, Switzerland, Italy, France (Strasbourg area) and Luxembourg (Moselle). The main branches of the Rhine-Alpine Corridor are: • Genova – Milano – Lugano – Basel (480 km); • Genova – Novara – Brig – Bern – Basel (500 km) • Basel – Karlsruhe – Mannheim – Mainz – Koblenz – Köln (490 km); • Köln – Düsseldorf – Duisburg – Nijmegen/Arnhem – Utrecht – Amsterdam (270 km); • Nijmegen – Rotterdam – Vlissingen (200 km); • Köln – Liège – Brussels – Ghent (285 km); • Liège – Antwerp – Ghent – Zeebrugge (230 km). The Corridor has 13 core urban nodes, spread over the five European Member States and Switzerland involved (Table 23).
Table 23: Overview of corridor nodes
Table 24 shows the mode and country specific lengths on the Rhine-Alpine Corridor (inland waterways include the rivers Moselle and Neckar on German-Luxembourgish territory). With about 3,225km, rail is the backbone of the corridor (with the highest share in Germany). Road has 26% of the length share, inland waterway (IWW) has a share of 25% of the total network. Germany has the largest share (49%) on all modes on the Rhine-Alpine Corridor. The respective shares of total network length of Belgium, The Netherlands, Italy and Switzerland vary between 9% and 17% for all transport modes.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 147
Table 24: Lengths per mode along the Rhine-Alpine Corridor by country
Road Network The corridor road alignment in The Netherlands follows the A 15/E31 from Rotterdam to the Dutch/German Border. The port of Vlissingen is connected to Rotterdam via the A58(E312)/A16. In Belgium, the infrastructure follows the E403 from Zeebrugge to Gent, the A10/E40 from Gent to Brussels and the A3/E40 from Brussels to the Belgium/German Border. In Germany, the highways A3/E35 (from Emmerich via Köln to Frankfurt), the A 4/E40 from Aachen to Köln and the A5/E35 from Frankfurt to Basel are part of the core road network. In Switzerland, the road alignment follows the A2/E35 from Basel to Chiasso. In Italy, the corridor comprises the A9/E35 from Chiasso to Milano and the A7/E62 from Milano to Genoa. Results of the transport market study Current market characteristics show that for cross-border traffic within the Rhine-Alpine Corridor rail has a share of 12%, road 34%, and inland waterways 54%. The cross-border traffic volume was estimated at 372 million tonnes in 2010. This covers 37% of the total estimated demand in the catchment area including all traffic flows (international and domestic). Total demand is estimated slightly above 1 billion tonnes. The main flows of the corridor are between Germany, The Netherlands and Belgium. These flows add up to 307.2 million tonnes, 83% of the total international freight activity. The highest import and export flows are between Germany and The Netherlands (103 million tonnes, representing 13% of the total corridor demand) and from The Netherlands to
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 148
Germany (152 million tonnes, representing 28%). The German-Dutch bidirectional flows amount to 41% of total cross-border corridor freight demand. The main cross-border commodities are: machinery and transport equipment, fuel products (liquid and dry bulk), building material and ores. The favoured mode of transport for these commodities (hinterland transport) is inland waterways followed by road, which has been confirmed by individual port statistics.
Table 25: Existing international freight transport flows (2010) (in thousand tonnes)
Atlantic Corridor Characteristics of the Atlantic Corridor The Atlantic corridor connects • the Iberian Peninsula, • the Atlantic façade of the continent, and • the centre of the EU through western France to Paris and Normandy and further east to Strasbourg/Mannheim. A large part of the corridor’s EU added value stems from the access it ensures to the Core Ports of the Atlantic façade from Gibraltar Strait to the Seine river (namely, Algeciras, Sines, Lisbon, Leixões (Porto), Bilbao, Bordeaux, Le Havre, Rouen), and the inland ports of Paris, Mannheim and Strasbourg. The maritime connectivity along the Atlantic Coastline of Europe is a key component of the corridor. The corridor provides both inland and maritime connections between the Iberian
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 149
Peninsula with France and Germany and more broadly with central Europe. Motorways of the Sea among the corridor's ports (and feeder ports) linking Spain and Portugal to France and beyond (towards Belgium, the Netherlands, United Kingdom, Ireland up to the Baltic Sea) are already developed, but their potential is still largely untapped. The inland backbone of the corridor delivering transport efficiency and sustainability is constituted by the Atlantic Rail Freight Corridor (former Rail Freight Corridor n. 4, enlarged to Germany), still endowed with large capacity on various sections. The corridor alignment is defined by Regulation 1316/2013 in its annex as follows: • Algeciras – Bobadilla – Madrid • Sines / Lisboa – Madrid – Valladolid • Lisbon – Aveiro – Leixões/Porto • Aveiro – Valladolid – Vitoria – Bergara – Bilbao/Bordeaux – Paris – Le Havre/Metz – Mannheim / Strasbourg The Paris – Rouen - Le Havre branch is three-modal, involving Rail, road, and the Seine – IWW; the connection links the North Sea to the Corridor The Atlantic Corridor has 4 cross border section: • DE-FR: Metz – Mannheim (Forbach-Saarbrucken) • ES-FR: Vitoria-Dax (San Sebastian – Bayonne) • PT-ES: Évora-Mérida • PT-ES: Aveiro-Salamanca The corridor does not have a road component in Germany.
Figure 64: Atlantic Corridor
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 150
Road Network
Figure 65: Atlantic Corridor Road Network
Results of the transport market study The transport market study has been developed by consultants through 2014 on the basis of existing trade data and recent modal market analyses, developed by different stakeholders. The study has been carried out with a macroeconomic multimodal approach and it shall be considered the first step towards an accurate estimation of the impact on transport market generated by completion of core network and the Atlantic Corridor. Two different scenarios have been conceived: • The "baseline scenario", based on existing forecasts on macroeconomic indicators • The "policy scenario" assuming the completion of the core network (and the Atlantic Corridor) together with the implementation of EU policy regulatory measures and standards.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 151
Concretely the study estimates international traffic flows through a model origin and destination both at national and regional level. Macroeconomic data as well as more specific data such as cross border traffic flows, modal split, details of transported goods are analysed by the model. Trade flows generated from the model includes both intra-EU flows as well international traffic flows.
Table 26: Model results (billion tonne-kms)
Further elaboration of the model allowed detecting the impact of maritime transport on cross border flows, where it is competing with other transport modalities, but not at national level, where maritime transport is complementary to other transport mode. The results are listed in Table 27.
Table 27: Model results
Some caveat shall be taken in consideration when assessing the results of the model, in order to set the ambitions for the Corridor potential:
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 152
• The model does not consider transport-related policy measures which are likely to affect the transit of international traffic flows. This is the case for instance of trends in maritime transport, such as the completion of Panama Canal. • Modal competition and changes in modal split which may be generated by the early completion of certain links or by the impact of certain technologies (i.e. further growth on ships dimension or trucks) or the impact of certain policy measures, such as the promotion of intermodal transport. • The model does not allow to fully display in the traffic assignment certain flows such the maritime or and the air nor to identify which flows are contributing more to the growth. • In any case, the outcome of the transport market study will be monitored and reviewed as the corridor development takes place. From the transport market studies at present, the following conclusions can be drawn: • On the long run, the full implementation of the Atlantic Corridor together with the related policy measures such as electrification, standard UIC gauge will lead to a fast growth of railway transport, both at national (+87%) and at a corridor level (95%), while road transport will grow at a slower pace (+53%) at national and at Corridor level (67%). Effects are slightly different on 2030 perspectives but still railway transport increases at a faster pace than road both at national (68% and 32% respectively) and at corridor level (43% and 40%, respectively). The competitiveness of railway will be increased both by a reduction of relative costs and by higher quality of the services. • The impact of the implementation of the core network and the related policy measures boast also the inland waterway transport both at national - limited to France and Germany – and at corridor level (France). By 2050 a growth of 57% is expected at national level, and even higher along the corridor, more than twice the current traffic. Without the completion of the core network and the related policy measures the potential for inland transport is much lower. • The growth of railway and inland waterways would have been even higher, as the policy scenario does not fully consider policy measures, such as incentives or internalisation of external costs and other intermodal policies, which will lead to the development of intermodal transport as well as well-connected inland terminals. Some success case has been already proved in Germany and Switzerland, totalling a majority share of railways in spite of the difficult morphology thanks to the enforcement of the polluter pays principle. • Cross-border maritime flows along the corridor will growth at a slower pace than other competing modes (its share will decrease from 30.1% to 28.6%). This may depends on the assumption made by the models which do not allow to fully capturing the magnitude of maritime transport. • Predictions for ports along the Corridor show an increase of the throughput between 30% and 90%. However, among other factors, the magnitude of this growth as well as the impact on other modes depends on the implementation of effective multimodal connections to the ports.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 153
• The presence of adequate terminals ensuring Port capacity
North Sea-Mediterranean Corridor Characteristics of the North Sea-Mediterranean Corridor The North Sea Mediterranean corridor stretches from Glasgow, Edinburgh and Belfast in the north to Cork in the west and to Paris and Lille in the centre, to Marseille in the south, and extending north-east through Luxembourg, Belgium and the The Netherlands towards Amsterdam. It covers six Member States, namely Belgium, Ireland, France, Luxembourg, The Netherlands and the United Kingdom, as well as leading to the Swiss and German borders in Basel. The North Sea Mediterranean Corridor will establish high capacity and multimodal transport connections in one of the most densely populated areas of Europe, connecting six important Member States. This is an area of extremely intensive economic activities including high density transport activities. The progressive implementation of the many projects listed in the Annex will result in additional growth potential generating new employment opportunities. I strongly believe that if all concerned Member States actively participate in this work and make use of the European added value, this will increase capacity and strengthen the international competitiveness of our ports, road, railways and other connections to internal and external markets. This North Sea Mediterranean corridor groups together the former Priority Projects 2, 9, 13, 14, 24, 26, 28, 30, ERTMS Corridor C and Rail Freight Corridor 2 now the Rail freight Corridor North-Sea-Mediterranean. All modes of transport are covered within the North-Sea Mediterranean corridor; air, sea, road, rail, inland waterway, and even transport by pipeline. Key infrastructure assets include the Channel Tunnel, three of Europe’s top-five airports and four of Europe’s top-ten seaports. Waterborne transport, inland and maritime, is strongly emphasised in the corridor. This corridor is defined as a series of interlinked sections, with many short-sea connections between the United Kingdom, Ireland and the mainland Europe. It overlaps with the North Sea Baltic and Rhine-Alpine Corridors in The Netherlands and Belgium, the Atlantic Corridor in Northern France and the Mediterranean Corridor in Southern France, and it is the only core network corridor reaching the United Kingdom and Ireland. It is therefore an extensive and complex corridor containing densely populated regions of long-standing economic importance and with a high degree of urbanisation, along with more peripheral and less densely populated regions in the west and north. It is also characterised by important crossings, interlinkages and mutual capacity effects.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 154
Figure 66: North Sea-Mediterranean Corridor
Road Network Technical requirements for road refer mainly to safety and sustainability issues, as well as the implementation of interoperable tolling schemes where applicable. Core links are required to be either motorways or express roads. In the North-Sea Mediterranean Corridor, virtually all of the core links comply with this standard, but there are certain last mile connections to seaports, including Zeebrugge and Cork, where current road standards are not adequate for the level of traffic. Results of the transport market study In overview, the North Sea Mediterranean corridor covers a large number of the most economically active cities and regions in Europe, as well as being the location of many of Europe’s largest gateway ports. It has a clearly defined central area (London-Paris- Amsterdam). Base year data for the corridor shows high levels of activity, with intra-corridor freight flows amounting to 1.029 billion tonnes. These are heavily concentrated within the central part of the corridor, meaning Southeast England, Northeast France, Belgium (especially the Flemish region) and The Netherlands. Volumes in the corridor represent a disproportionately high share of EU27 volumes. For example, total port throughput in corridor countries is 1.629 billion tonnes, more than 40%
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 155
of the EU27 total. Corridor (core network) ports handle 1.256 billion tonnes of cargo, including both short-sea and deep-sea traffics. They handle 31.468 million TEUs, and 34.1 million passengers. Airports in the corridor handle 56% of EU27 air cargo.
Table 28: Corridor Traffic Shares of EU27 Volumes
The analysis of future flows has focused on examining demand-side issues for both passengers and freight, including available official forecasts that have been produced by or for the Member States. Market analysis indicates that although headline activity indicators such as population and economic growth are at modest levels for the EU as a whole, there is substantial absolute growth expected within the North Sea - Mediterranean Corridor, linked to the attractiveness of the major cities, and the faster-than-average growth in long-distance traffic, especially inter-continental container traffic with East Asia which naturally feeds directly into the corridor’s networks. Economic and demographic data shows that there is essentially a clustering of economic activity within the centre of the corridor, creating population growth around the major cities, and transport growth, linked also to the establishment of global hubs at the major container ports and airports. Economies of scale associated with the use of large container ships result in maritime internal and external transport costs being much lower (per tonne- km) than inland costs, so shipping lines who face intense competitive pressures therefore focus their activities upon the ports that give them nearby access to these population centres. In this context it means shipping lines are bringing the largest volumes of
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 156
containers into the range of ports between Le Havre and Hamburg on the continental side and between Southampton and Felixstowe on the United Kingdom side. The degree to which demographic and economic clustering stimulates transport volume growth creates a high potential risk for the corridor, which is still highly dependent upon road transport for inland transport. However, all of the core continental seaports are actively developing facilities and programmes to develop multimodal hinterland networks, and there is sufficient critical mass of cargo to make this feasible. Such initiatives need to be helped by providing the necessary rail and waterway networks to raise the shares of these inland modes to levels observed, for example in the parallel corridor between the Dutch and Flemish ports and the German Ruhr area. Forecasts currently published by the corridor ports typically indicate expectations of throughput increasing by 50% or even 100% by 2030, with the container sector growing the fastest. Available national forecasts suggest that corridor port throughput has the potential to increase by an additional billion tonnes, of which around 60% would be distributed inland via the hinterland networks belonging to the corridor. If all ports can achieve waterway shares similar to Rotterdam, Amsterdam and Antwerp, and rail shares similar to Zeebrugge or Hamburg, much of the expected growth can be absorbed ‘off-road’. Largely this depends upon solving bottlenecks inland, raising the performance of the inland rail and waterway networks south and west of the Rhine, where non-road modal shares are still low, and developing networks of inland multimodal platforms as logistics hubs. In the continental part of the corridor, attention must therefore focus on improving rail and waterway transport. For waterways, market shares in the corridor are low overall (around 7% of total transport) and falling. Moreover, volumes are heavily concentrated on sections leading towards the Rhine, so there is a need to develop other parts of the network. Routes on the Maas/Meuse, the Albert Canal, the Escaut/Scheldt including the Canal Seine Nord Europe, and Lys/Leie waterways still require upgrades to remove bottlenecks, and the French waterway basins along the Seine, Oise, Marne, and Saône/Rhône are essentially cut off from the Dutch and Belgian networks. In the case of rail freight, traffic shares for cross-border are also low inside the corridor, in comparison with either national traffics or on parallel routes e.g. from Germany or between the Alpine countries. There is a particular need to address rail bottlenecks in France e.g. Lyon, Lille, Metz, Strasbourg and Paris and to solve loading gauge problems in order to allow the two main axes (Paris-Amsterdam, and Marseille-Luxembourg-North Sea as well as Rotterdam-Antwerp-Basel) to reach their full potential. Achieving the technically feasible 740m train length in Belgium for a greater number of train paths is also necessary. In contrast to the situation on the Continent, the market issues in Ireland and regions of the United Kingdom including Northern Ireland focus on peripherality, cohesion and accessibility. In Ireland, the development of the public transport system, particular the DART Underground Programme and its sub-projects will contribute towards alievating the isolated nature of Ireland's economy. The Interconnector/Dart Underground programme will substantially improve connectivity, linkages and integration between the island of Ireland and other Member States. While there is a risk of Europe’s economic centres crowding out development in more peripheral areas, there is a need to support the recovery of
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 157
economies which have been severely hit by the Eurozone crises. Ireland and Northern Ireland depend to a great extent upon short-sea container services for trade with continental Europe and via hubs to the rest of the world, and upon ferry services for trade with Great Britain and the continent. The combination of depressed demand and the potential impact of higher transport costs arising from the need to cross the SECA area, create the potential for fewer services services, lower service frequencies and higher freight rates between the more peripheral areas and the core areas of the corridor. Unlike many regions in the corridor, Ireland and Northern Ireland depend on feeder container services to connect its ports to global container networks, so there is a need to offset this disadvantage. Improving inland (road) and maritime (including Motorways of the Sea) access to core ports is therefore a step towards achieving greater cohesion. For the mainland United Kingdom, issues of accessibility and cohesion are also important, but to a lesser degree because of the critical mass of economic activity especially around London and the South East. Traffic analysis shows that there has a been strong trend for transport flows with the continent to become concentrated on the North-Sea Mediterranean Corridor links via the Short Straits, strengthened by the construction of the Channel Tunnel. Apart from the notable exception of Eurostar passenger rail services, most of this growth has led to greater numbers of lorries and cars using long distance motorway connections, via the M25 around London and bottlenecks such as the Dartford Crossing, to reach the port of Dover and the Eurotunnel terminal (near Folkestone). Both the Dover-Calais and Dover- Dunkerque route suffer from RORO capacity issues due to the growth of cross-Channel traffic, which also leads to road congestion in France between the A16 motorway and the Dunkerque RORO terminal. In the short term this puts additional pressure on RORO port capacity in Dover, Calais and Dunkerque, but it also signals the need for longer term solutions such as boosting North Sea routes (United Kingdom East Coast to The Netherlands and Belgium), increasing the amount of through-rail freight via the Channel Tunnel, and the consideration of measures to add capacity to the Thames road crossings. In the United Kingdom container sector, which covers both global and European connections, growth has focused around the two main ports of Felixstowe and Southampton. In addition, a new container port has been developed at London Gateway on the Thames. These factors have tended to draw traffic towards the south-east corner of Great Britain. However, the Port of Liverpool, with a more central location in Great Britain on the west coast, is developing a new container terminal on the River Mersey with the objective of securing additional traffic via a container port in the north of England. So, therefore, while the United Kingdom is heavily dependent on North-Sea Mediterranean Corridor sections to maintain the efficiency of its networks, it also has the potential to develop parallel or East-West routes involving longer sea crossings and shorter inland road or rail hauls, as well as long distance rail freight through the Channel Tunnel.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 158
Rhine-Danube Corridor Characteristics of the Rhine-Danube Corridor The Rhine-Danube Corridor is the main east-west link between continental European countries connecting France and Germany, Austria, Slovakia, Hungary, Croatia, Romania and Bulgaria all along the Main and Danube rivers to the Black Sea by improving (high speed) rail and inland waterway interconnections. It includes sections of Priority Projects 7, 17, 18 and 22. The parts in the Czech Republic and Slovakia are also covered by the Rail Freight corridor 9. Bulgaria and Croatia are only included in the corridor as regards Inland Waterways. This concerns ports and inland waterways of the Danube and Sava Rivers. Also, non-EU neighbouring countries are included in the analysis of the waterway corridor. In detail this means the sections below are included in the analysis: • Serbia: related to inland waterways (Danube, Sava) and two ports (Belgrade, Novi Sad) • Bosnia and Herzegovina: related to inland waterways (Sava) • Moldova: related to one port (Giurgiulesti) • Ukraine: related to inland waterways (Danube). The alignment of the corridor consists of the following main connections, as reported in the maps of the core and comprehensive network of the TEN-T Guidelines (Regulation 1315/2013) and according to Annex 1 of the CEF (Regulation 1316/2013): • Strasbourg – Stuttgart – München – Wels/Linz ; • Strasbourg – Mannheim – Frankfurt – Würzburg – Nurenberg – Regensburg – Passau – Wels/Linz ; • Munich/Nuremberg – Praha – Ostrava/Přerov – Žilina – Košice – UA border • Wels/Linz – Wien – Bratislava – Budapest – Vukovar; • Wien/Bratislava – Budapest – Arad – Brasov/Craiova – Bucuresti – Constanta – Sulina. The Corridor can be roughly split into two branches: the “Black Sea” branch and the Czech- Slovak “CS” branch in the north. The Black Sea branch has two different routes in Germany and Romania. For Germany there is a northern route via Frankfurt/Nurnberg and a southern route via Stuttgart/Munich/Salzburg. In Romania, the Corridor routes via Sebes, as well as via Craiova. The section C of the Black Sea branch is exclusively dedicated to inland waterways (i.e. Danube and Sava). The alignment of inland waterways is clearly defined. It includes the Main River starting with the confluence with the Rhine, which is connected to the Danube by the Main-Danube Canal at Kelheim. The CEF Regulation includes a pre-identified project on Sava up to the port of Sisak, which is defined as a comprehensive port.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 159
The CS Branch has two starting points (Munich and Nurnberg) and runs via Plzen and Prague towards Přerov in the Czech Republic. Beyond Přerov at Hranice na Morave the Corridor splits into the line via Ostrava, which is mainly dedicated for passenger traffic, and the direct line via Púchov and Zilina in Slovakia is manly used by freight traffic.
Figure 67: Rhine-Danube Corridor
Road Network The road corridor has a total length of 4.470km for both branches, the one across Czech Republic and Slovakia and the one along the river Danube. It crosses France, Germany, Czech Republic, Slovakia, Austria, Hungary and Romania. The road infrastructures follow in the northern branch from Frankfurt to Nuremberg and Regensburg the BAB 3 and in the southern part from Karlsruhe via Stuttgart, Munich the BAB 8. Along the Danube basin, the road corridor starts in Strasbourg (FR) with the motorway A35 and follows the motorway N4 to the crossing of the Rhine – at Kehl (DE) entering into the motorway A5 at Appenweier up to Frankfurt. The road alignment of the corridor shows some similarity to the rail alignment, particularly with respect to line variation between Karlsruhe and Frankfurt. In Austria the motorways A8 (from Suben to Haid) and A1 (Salzburg – Vienna) passing by Vienna on the A21 and the express way S1 south of Vienna and the A4 from Wien towards the Hungarian border form the road corridor along the river Danube. A connection from the A4 east of Bruckneudorf to the D2 at Jarovce (SK) is the motorway A6. In Hungary the M1 from the Austrian/Hungarian border to Budapest, the short section of M15 between the
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 160
Slovakian/Hungarian border and the M1, the M0 around Budapest and the M5 to Szeged and from there the M43 form the main route towards Romania. In Romania the main branch is from Arad via Sebes to Pitesti and Bucuresti and the second branch via Craiova to Bucuresti. The route continues then from Bucuresti to Constanta. The route from Drobeta Turnu Severin towards Craiova is indicated on the maps of the TEN-T Regulation via Calafat and its port (belonging to the core network, whereas the direct route from Drobeta Turnu Severin via Filiasi to Craiova is indicated as comprehensive network). However this is an overlapping section with the Orient-East Med Corridor (OEM). The total average distance of the Black Sea branch is about 2,300 km, thus almost exactly the same value as rail. Hungary, Czech Republic and Slovakia have realised an ambitious construction programme on their motorway network in the last years. Also Romania has carried out a number of road projects on sections of the main corridor route between Nadlac – Bucuresti – Constanta (Northern parts). On this, there are operational motorway sections: Arad – Timisoara; Pitesti – Bucuresti – Cernavoda- Constanta; Saliste -Sibiu by-pass (Selimbar); Constanta Ring road; Deva (Soimus)- Orastie-Sebes-Sebes by-pass- Cunta; Balint –Dumbrava; Lugoj-Balint (A6). As the schematic figure shows the main difference to the rail corridor is the missing part of the CS branch via Munich and Regensburg as well as from Regensburg to Plzen. For this reason, the average road length of the CS branch is about 100 km shorter compared to rail. The road alignment differs from the rail alignment mainly in the Czech Republic, since there is a road connection to Brno. From Brno a connection to Přerov and Ostrava is recommended, thus following the modification of the RFC 9 between Přerov to Ostrava. The section between Hulin and Ostrava is part of the Baltic-Adriatic Corridor (BAC). The road corridor continues then south of Přerov from Zlin to Zilina, Prešov, Košice to the Ukraine border.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 161
Figure 68: Road alignment of the Rhine-Danube Corridor and assigned infrastructure
Results of the transport market study The transport market study (TMS) has been carried out in 2014 and presents drivers for scenarios, which mainly consist of socio-economic data and are well described in national sources and the European data project ETISplus. National forecasts are used to describe the demand and supply side of the TMS. Additionally ETISplus data is used, which contains detailed information on transport demand and infrastructure characteristics. Rail data is also present in the international rail studies of Rail Freight Corridors 7 & 9 and Priority Project 22. The source of information for Inland Waterways Transport is medium and long term perspectives for IWT. Regional data of roads, ports and airports are presented in detail in the appropriate section of the TMS. The TMS's main conclusions are presented below: Population on the corridor is centred on the corridor urban nodes. In the future this geographical pattern is not expected to change. Population decline is forecasted in Germany, Hungary, Romania and Slovakia. This will not affect the strongly urbanised areas. Modest population growth is expected in Austria, Strasbourg area and the Czech Republic. In terms of economics, the existing GDP difference/gap between roughly the eastern and western part of the corridor will not be shifted by 2030 by forecasts. The forecasted economic growth rates of the countries on the corridor are not too different from each other. Economic growth is roughly between 1 and 2% per year in terms of GDP in current prices.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 162
The existing transport pattern indicates that road is the most used cross-border transport mode for passenger and freight. In total 68.4 million international passenger demand trips were made in 2010 within the corridor study area. Between Austria and Germany 17.2 million trips were observed, representing 25% of the total trips. The second highest flow is the bidirectional traffic between Austria and Slovakia: 12.2 million trips, 18% of the total. The most represented mode is road, covering 83% of the total trips, followed by rail with 13% and air with 4%. For the individual modes the bidirectional traffic flow between Austria and Germany is again the most important traffic flow, except for rail. For rail the most import flow is between Austria and Hungary. For road the bidirectional traffic flow between Austria and Slovakia is the second highest. The single French region on the corridor has a high number of road traffic. For rail the highest intensity is the flow between Austria and Germany, and for air the flow between Germany and Hungary. International Freight demand transport is concentrated on the western part of the corridor. The transport in between the areas of Austria, Germany, Czech Republic and Slovakia accounts for 82% per cent of the total corridor transport. Between the Czech Republic and Slovakia more than 18 million tonnes are transported. Austria – Germany accounts for 14 million tonnes. The transport volume for road within the RD corridor is twice as big as for rail, and four times as big as for inland waterway. Or in percentages: 58% for road, 28% rail and 14% IWT. The Czech Republic has the highest rail and highest road volume of the corridor countries. The enlargement of the corridor catchment area and comprehensive ports in the TMS as agreed in the second corridor forum makes the modal split more favourable towards IWT. The relevant IWT countries of Bulgaria, Croatia, Serbia and Ukraine rank lowest in terms of volume. Romania ranks highest, not in the least due to the expanded third countries, especially Serbia has a high volume of import and export to Romania, mostly due to the port of Constanta. For rail, the connection between the Czech Republic and Slovakia transport more than 9 million tons. The Czech-Slovak connection therefore accounts for about 34% of the volume. The total international rail volume of Bulgaria with respect to the corridor is 1 million tonnes. The conclusion on the demand side is that road transport will be dominant in the future market in the baseline scenario. Currently road is dominant and the position is expected to strengthen practically corridor wide in the baseline situation. This is the case for international and national traffic, passenger and freight. In a number of cases the growth rates are higher for alternative modes of transport, but the net volume growth is generally highest for road. Passengers are forecasted to have more individual wealth, more car ownership and in a limited number of countries face deteriorating public transport. In the baseline freight scenarios a continued trend is generally assumed, which is beneficial for road because if a mode shift has not taken place in the past years, no future mode shift is forecasted in some models. Still in scenarios of higher road costs and improved alternatives, road is still expected to remain dominant. This leads to the conclusion that there is a need to strengthen the rail and inland waterway transport modes on the corridor to take over future transport volumes through the improvement of the rail and the inland waterway
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 163
network and not to stop there to support modal shift. International traffic, import, exports and transit is expected to grow in all forecasts. This helps to create a larger playing field for intermodal operations. The traffic of the Eastern part of the corridor will grow at a higher rate. However the Member States of Austria, Czech Republic, Germany and entry/exit node France (Strasbourg) on the corridor are expected to maintain the high transport demand by 2025.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 164
Annex 5: Heavy Duty EU Emission Legislation/Regulations
History Europe first introduced heavy-duty vehicle emission standards in 1988. The "Euro" track was established beginning in 1992 with increasingly stringent standards implemented every few years. The heavy-duty Euro standards are numbered using Roman numerals (e.g. Euro I, II...V), whereas light-duty standards use Arabic numbers (e.g. Euro 1, 2...5). Testing is performed only on engines, rather than on complete vehicles, and limit values are expressed in terms of grams per kilowatt-hour (g/kWh) rather than grams per kilometer travelled (g/km). Many countries have since developed regulations that are aligned in large part with the European standards; the following are some of the most important rulemaking steps in the heavy-duty engine regulations: Euro I and II Euro I standards were introduced in 1992, followed by the introduction of Euro II regulations in 1996. These standards applied to both truck engines and urban buses; the urban bus standards, however, were voluntary.
Figure 69: Exhaust emissions of C.I. engines for vehicles > 25 km/h for EURO I and EURO II standards60
60 Source: Delphi, Worldwide Emissions Standards Booklets, Dir. 88/77/EEC amended by Dir. 91/542/EEC and Dir. 96/1/EEC
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 165
Euro III, IV, and V The EU adopted Directive 1999/96/EC in 1999, which introduced Euro III standards (2000), as well as the Euro IV/V standards (2005/2008). The directive set voluntary emission limits that are slightly more stringent than Euro V standards for “enhanced environmentally friendly vehicles” or EEVs. The following directives were important additions or amendments to the original standards:
Defeat Devices – In 2001, the European Commission adopted Directive 2001/27/EC which prohibits the use of emission “defeat devices” and “irrational” emission control strategies, which would reduce the efficiency of emission control systems when vehicles operate under normal driving conditions to levels below those determined during the emission testing procedure. Amended Euro IV and V – Directive 2005/55/EC adopted by the EU Parliament in 2005 introduced durability and on-board diagnostics (OBD) requirements, as well as re-stated the emission limits for Euro IV and Euro V which were originally published in 1999/96/EC. In a “split-level” regulatory approach, the technical requirements pertaining to durability and OBD—including provisions for emission systems that use consumable reagents—have been described by the Commission in Directive 2005/78/EC. When Euro IV and Euro V standards were adopted, regulators expected the stringent PM emission standards to require the use of DPFs (Diesel Particulate Filters) in commercial heavy-duty vehicles. However, by tuning their engines for high-NOx, high-fuel economy and relatively low PM emissions, manufacturers are able to comply with the Euro IV and V emission standards without the use of DPFs. These manufacturers use selective catalytic reduction to lower tailpipe NOx emissions to meet Euro IV and Euro V standards. However, this compliance strategy does not reduce emissions of the smallest and most hazardous particles to the same degree as DPFs.
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 166
Figure 70: Limit values – ESC, ELR and ETC Tests for EURO III standards61
Figure 71: Limit values – ESC, ELR and ETC Tests for EURO IV standards62
Figure 72: Limit values – ESC, ELR and ETC Tests for EURO V standards63
Euro VI Euro VI emission standards were introduced by Regulation No 595/2009, which was published on 18 July 2009 (with a Corrigenda of 31 July 2009). The new emission limits,
61 Source: Delphi, Worldwide Emissions Standards Booklets, Dir. 88/77/EC as amended by Dir. 1999/96/EC and Dir. 2001/27/EC 62 Source: Delphi, Worldwide Emissions Standards Booklets, Dir. 88/77/EC as amended by Dir. 1999/96/EC and Dir. 2005/55/EC, Dir. 2005/78/EC and Dir. 2006/51/EC 63 Source: Delphi, Worldwide Emissions Standards Booklets, Dir. 2005/55/EC and Dir. 2005/78/EC amended by Dir. 2006/51/EC and Dir. 2008/74/EC
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 167
comparable in stringency to the US 2010 standards, became effective in 2013 for new type approvals and for all registrations in 2014. Additional provisions of the Euro VI regulation include:
An ammonia (NH3) concentration limit of 10 ppm applied to diesel (WHSC + WHTC) and gas (WHTC) engines. A maximum limit for the NO2 component of NOx emissions may be defined in the implementing regulation. New testing requirements for off-cycle emissions (OCE) and in-service conformity (in-use testing). EU Member States are allowed to use tax incentives in order to stimulate marketing and sales of vehicles meeting new standards ahead of the regulatory deadlines. However, the use of incentives is contingent upon the following:
They apply to all new vehicles offered for sale on the market of EU Member States that comply in advance with the mandatory limit values set out by the directive. They terminate when the new limit values come into effect. They must not exceed the additional cost of the technical solutions introduced for each type of vehicle. This is to ensure compliance with the limit values.
Figure 73: EURO VI emission limits64
Development of Test Cycles that are used for HD Emission Regulations Test Cycles Euro I and II / ECE R49 or 13 Mode Cycle It is a steady state diesel engine test cycle used for TA emission testing of HD highway engines up to Euro II standards. Effective October 2000, the R49 cycle was replaced by the ESC cycle. This cycle is operated through a sequence of 13 speed and load conditions. The final result weighted average of the 13 nodes.
64 Source: Delphi, Worldwide Emissions Standards Booklets, Reg. EC No: 595/2009, Reg. EU No 582/2011 and 64/2012
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 168
Figure 74: Test cycles EURO I and II/ ECE R49 OR 13 MODE CYCLE65
Test Cycles Euro III and later defined by Dir 88/77/EC as amended by Dir 2001/27/EC 1) European Steady-State Cycle ESC The test cycle consists of a number of speed and power modes which cover the typical operating range of diesel engines. It is so determined by 13 steady and 3 random modes. Emission values are obtained with the weighted mean of emissions on each of the 13 modes.
The 3 random points are random-tested in a control area. In this random test, only NOx emissions are measured. They must not exceed the interpolated value of the 4 nearest modes plus 10%. This NOx control check ensures effectiveness of the emission control of the engine within the typical engine operation range.
Figure 75: Test Cycles EURO III and later66
65 Source: Delphi, Worldwide Emissions Standards Booklets, Dir 88/77/EC as amended by Dir 2001/27/EC 66 Source: Delphi, Worldwide Emissions Standards Booklets, Dir. 88/77/EC as amended by Dir. 2001/27/EC
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 169
2) European Transient Cycle ETC This cycle consists of a second-by-second sequence of transient modes. It is based on road- type-specific driving patterns of HD engines installed in trucks and buses. It is divided in 3 parts: 1/3 urban roads, 1/3 rural roads, 1/3 motorways.
Figure 76: Sequence of the European Transient Cycle Test - ETC67
3) European Load Response – ELR Only diesel smoke is measured. ELR cycle is defined by fixed speed sampling and a random sampling. The random sampling is represented by a random speed and by a random initial load. Smoke measurements during the sampling must not exceed 20% of the highest value of close speeds or more than 5% of limit value. The biggest one is selected.
67 Source: Delphi – Worldwide Emissions Standards Booklets; Dir. 88/77/EEC amended by Dir. 1999/96/EC
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 170
Figure 77: Sequence of ELR Test68
Test Cycles Euro VI WWHD – Worldwide Harmonised Heavy Duty Two representative test cycles have been created and cover typical driving conditions in the European Union, USA, Japan and Australia: - WHTC – World Harmonised Test Cycle - WHSC – World Harmonised Steady-State Test Cycle
Exhaust emissions to be measured : CO, HC, NMHC, NOx, PM, CO2 expressed in g/kWh. 1) WHTC – World Harmonised Test Cycle It is a second by second sequence of normalised speed and torque values. It consists of 2 tests 1 cold and 1 hot with weightings of 14% for cold and 86% for hot test.
Figure 78: Sequence of World Heavy Duty Transient Cycle – WHTC second by second sequence of normalized speed and torque values69
68 Source: Delphi, Worldwide Emissions Standards Booklets; Dir. 1999/96/EC
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 171
2) WHTC – World Harmonised Steady State Cycle It consists of a number of speed and power modes which cover the typical operating range of HD engines. The engine is conditioned in mode 9 for 10 minutes before the test.
Figure 79: World Heavy Duty Steady-State Cycle70
OFF Cycle Emissions (OCE) The OFF cycle emissions includes two components: firstly to prohibit the use of defeat strategies and secondly to introduce the World-harmonised Not to Exceed (WNTE) methodology in order to limit off-cycle emissions under a broad range of engine and ambient operating conditions. Off-Cycle that was introduced with Euro VI regulation and that was performed during the type approval testing, follows the NTE (not-to-exceed) limit approach. A control area is defined on the engine map (there are two definitions, one for engines with a rated speed < 3000 rpm, and another for engines with a rated speed ≥ 3000 rpm). The control area is divided into grids. The testing involves random selection of three grid cells and emission measurement at 5 points per cell, and it is applied over a wide range of operating and ambient conditions.
69 Source: Delphi, Worldwide Emissions Standards Booklets 70 Source: Delphi – Worldwide Emissions Standards Booklets
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 172
Figure 80: Off Cycle Emissions (OCE)71
In Service Emissions Conformity (Compliance) Euro VI regulation also introduced in-use testing requirements that involve “Real Life” test field measurements using Portable Emission Measurement System (PEMS). The testing is conducted over a mix of urban (0-50 km/h), rural (50-75 km/h) and motorway (> 75 km/h) conditions, with exact percentages of these conditions depending on vehicle category. First in-use test should be conducted at the time of type approval testing.
71 Source: AVL – Emission: Heavy Duty and Off-Road Emission Test Systems
D2.1: Definition of the Use Case - v1.0, 31/01/2017 Page 173
For more information:
optiTruck Project Coordinator Mr Jean Charles Pandazis ERTICO – ITS European Avenue Louise 326 1050 Brussels, Belgium
www.optitruck.eu
Disclaimer: This document reflects the views of the author(s) alone. The European Union is not liable for any use that may be made of the information herein contained.