Intelligent Mobility

@IMPETUS_UK

©SHUTTERSTOCK

IMPETUS Innovate Research Technology Centre INTELLIGENT (Room B03) University Park Nottingham MOBILITY NG7 2RD

Telephone: +44 (0)115 748 6068 THE OPTIMISED Email: [email protected] MOVEMENT www.nottingham.ac.uk/impetus OF GOODS AND PEOPLE

About IMPETUS 4

PROSPECT – proactive safety for pedestrians and cyclists 6

“Encompassing everything from Estimating subsidence risk across an entire rail network from space 7 autonomous vehicles to seamless Telematics based predictive maintenance system for commercial vehicles 8 journey systems and multi-modal The effect of urban infrastructure on wireless network performance 10 modelling software, Intelligent Mobility uses emerging technologies Dynamic Modelling of Demand (D-MoD) 11 to enable the smarter, greener and Limits of the predictability of human travel 12 more efficient movement of people and goods around the world” Mapping obscuration of GNSS in urban landscapes (MOGUL) 13

PASSME (Personalised Airport Sytems for Seamless Mobility and Experience) 14 Transport Systems Catapult A Petri-Net modelling appraoch to railway bridge asset management 16

Variation of pollutant concentrations with road infrastructure interventions 17

Estimating public transport topology from existing supply data 18

Twitter and Passenger Disruption (TaPD) 19

Real time structural health monitoring 20

How the extremes of wireless network size affect data delivery 22

A Hansel and Gretel approach to cooperative vehicle positioning 23

Community and drone mapping in Tanzania 24

TFL buses as sentinels 25

The Smart Campus project 26

Satellite monitoring of ground motion at airports in green sites 27

Transforming transport planning operations at 3T Logistics 28

Contact 31

2 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 3 About IMPETUS An innovative partnership

Professor Sarah Sharples Dr Paul Zanelli Associate Pro-Vice-Chancellor for Chief Technical Officer Research & Knowledge Exchange, Transport Systems Catapult Professor of Human Factors, Faculty of Engineering, The University of Nottingham

The IMPETUS partnership brings together The University Intelligent Infrastructure describes how sensor technologies Integral to the mission of the Catapult network, as set out IMPETUS emerged as a consequential partnership of Nottingham (UoN) and the University of Leicester (UoL) and data analytic techniques can be used to monitor the in Hauser’s 2010 report, is the relationship between UK between the Universities of Nottingham and Leicester, with the Transport Systems Catapult, in order to deliver state of our transport system – from bridges to wireless Catapult Centres and academic research institutions. with whom TSC has collaborated with to deliver many world-class research into Intelligent Mobility. Examples network performance, moving us towards an integrated of the aforementioned activities. This partnership has of this work can be found in this document in a selection data approach, which will enable us to have a real time and Intelligent Mobility (IM) harnesses new and emerging enabled world-leading researchers to utilise TSC’s of case studies, each demonstrating how work is taking proactive view of multimodal transport systems. technologies to achieve integrated, efficient and expertise and industry connections to support research place directly with research funders, transport providers sustainable transport systems. Transport Systems Catapult and pave potential routes to commercialisation. and technology developers to influence the transport Future Transport Systems examines everything from (TSC) believes that the UK will capture a share of the technologies and travel behaviours of the future. drones, to the way that people move around and behave global IM market through cutting-edge research and the The following case studies demonstrate the benefits of on our university campuses, helping us to understand ability to transform ideas into high-value products academic-industry collaborations. The work of IMPETUS spans three themes: how we can deploy advanced transportation technologies and services. effectively and harness the data that is produced to support future smart cities. The University Partner Programme (UPP) was set up in JOURNEY EXPERIENCE 2014 as a three-year project to kick start our academic INTELLIGENT INFRASTRUCTURE Intelligent Mobility is not only key to enabling individuals engagement activities with thirteen universities. Our to travel effectively, it considers how goods and ultimate aim was to increase the likelihood of new ideas FUTURE TRANSPORT SYSTEMS infrastructure systems should be designed both now and and innovations from academia reaching the market as in the future to positively impact on our economy. By finished products and services. This will maximise the Journey Experience considers how people choose and working with industry and policy makers, in partnership economic impact of research currently being undertaken plan their journeys, and their priorities in making decisions with the Transport Systems Catapult, we aim to be at the in the UK. We do this by running various studentships, about transport mode and timings. Work in this area forefront of designing future transport technologies and placements, workshops, conferences, sandpits and submit ranges from the use of customer data, including social informing the ways they are designed and deployed. joint proposals with the universities. media, to predict and plan for traveller behaviour, to the design of interfaces in future automated vehicles.

4 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 5 CAPABILITIES CAPABILITIES

• Human factors • Earth observation • Human-machine interface • Detecting and monitoring • Autonomous vehicles ground deformation relevant PROSPECT – proactive Estimating subsidence to infrastructure projects safety for pedestrians risk across an entire rail and cyclists network from space

Facilities Project contacts Facilities Project contacts A variety of ‘state of the art’ Human Factors Simulators Dr Gary Burnett – Associate Professor The Punnet software, developed by The University of Professor Stuart Marsh – Professor of Geospatial (medium to high fidelity), tailor-made to investigate Human Factors Research Group, Faculty of Engineering, Nottingham, which extracts ground motion over both Engineering, Faculty of Engineering, human-machine interface (HMI) design/evaluation issues The University of Nottingham rural and urban land cover from stacks of satellite radar The University of Nottingham – including driving simulators and bicycle simulators. [email protected] observations over extended time periods. [email protected]

Collaborators Dr Catherine Harvey – Research Fellow, UoN Collaborators Dr Andy Sowter – Assistant Professor, UoN IDIADA (ES), TNO (NL), Audi (DE), Volvo (SE), BMW Dr David Large – Research Fellow, UoN British Geological Survey, Network Rail, Strukton. (DE), Daimler (DE), Toyota (BE), VTI (SE), Budapest Uni (HU), IFSTTAR (FR), Chalmers Uni (SE), Continental (DE), Bosch (DE), Uni Amsterdam (NL).

PROJECT DESCRIPTION OUTCOME AND IMPACT PROJECT DESCRIPTION Severe damage PROSPECT is a European funded project which aims to It is hoped that collision avoidance systems developed by Although most UK coal mines have been closed for (compression) provide a better understanding of accidents related to PROSPECT will significantly reduce the number of deaths decades, the underground voids and structures that were and buckling vulnerable road users such as pedestrians and cyclists. for pedestrians and cyclists across the EU and worldwide. left behind can still result in serious subsidence incidents of a rail track caused by fault that extend over very large areas. This project identifies re-activation. The programme aims to develop, demonstrate and test subsidence risk and therefore enables rail asset managers innovative, safety systems for vehicles for protecting road In 2010, more than one quarter of to more precisely focus maintenance resources on those users. The project will develop next generation collision the world’s road traffic deaths were parts of the network that need it. avoidance systems which automatically brake/steer based on predictive algorithms concerning the potential path of suffered by pedestrians (22%) and The system combines satellite Earth observation road users. cyclists (5%), according to the World measurements with a novel data processing technique Health Organisation (WHO). developed by the Nottingham Geospatial Institute, providing Map of The University of Nottingham’s role is to lead on “Accident a low-cost solution to hazard assessment across the whole deformation Analysis and User Needs” and to conduct research into rail infrastructure network. The solution is designed to exploit (above) and HS2 accidents involving vulnerable road users. This means that approximately 335,000 non-motorised the new European Sentinel-1 satellite, which will support track profiles vulnerable road users die annually worldwide; furthermore, radar interferometric measurements of ground motion on a (left) of a section In addition, the team will investigate how a human- according to WHO (2004), there are at least 20 times as weekly basis across the whole of Europe. of track in the East Midlands machine interface should be designed to facilitate trust and many non-fatal injuries in road traffic accidents as fatalities. acceptance for automation technology within a vehicle. detected using ENVISAT data. OUTCOME AND IMPACT The grid boxes are 5km x 5km A demonstration over a section of the proposed High in extent. Speed Two (HS2) route in the East Midlands showed that there was some significant differential motion that may affect safety along the track.

Human factors driving simulators Rates of ground motion have been detected that could exceed the

6 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 7 CAPABILITIES

• Automated scheduling • Predictive maintenance • Advanced data modelling Telematics based predictive maintenance system for commercial vehicles

Facilities Project contacts Prognostic and Health Management System, Dr Dario Landa-Silva – Associate Professor Microlise Ltd. in Computer Science, ASAP Research Group, School of Computer Science, Collaborators The University of Nottingham Microlise Ltd. [email protected] www.asap.cs.nott.ac.uk

PROJECT DESCRIPTION OUTCOME AND IMPACT

The aim was to develop predictive maintenance systems Battery related failures are in the top list of common (see figure 1) for commercial vehicles through the use of commercial vehicle failures hence a Battery Monitory data analytics and computational intelligence techniques. Subsytem (BMS) was developed. The BMS is based on Improving maintenance management helps to reduce the pattern recognition, it uses historical failure data in the usually high maintenance cost commercial vehicles. The learning phase and real-time data to identify battery objective is to monitor the condition and state-of-health of abnormal behaviour and detect future problems. different components of the vehicle, to indicate faults and impending failures based on continuous health assessment made from direct or indirect observations via telematics. Initial tests showed that the BMS can The challenge was to employ a range of computational generate warnings between one week techniques such as data analytics, data mining, fuzzy logic, heuristic search and pattern recognition to process the and eight weeks before battery failure. huge amounts of historical and real-time telematics data This became the first component collected per vehicle and then anticipate failures in the FUTURE TRANSPORT SYSTEMS vehicle. This was a Knowledge Transfer Partnership (KTP) of Microlise Prognostic and Health project in partnership with Microlise Ltd a leading provider Management (MPHM) System. of telematics.

Figure 1: Extensive Model of Above: Professor Robert John Predictive Maintenance. from The University of Nottingham and Matt Hague, Product Strategy Director at Microlise. JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE |

8 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 9 CAPABILITIES CAPABILITIES

• Radio propagation simulation • Human factors and measurement • Distributed systems • Measurement of network • Advanced transport performance data modelling The effect of urban Dynamic Modelling of • Autonomy infrastructure on wireless Demand (D-MOD) network performance

Facilities Project contacts Facilities Project contacts Simulation (including high-performance computing) Dr Alan Stocker – Senior Lecturer, Rail signalling simulators. Lise Delamare – KTP Associate, Faculty of Engineering, and measurement capabilities (for example, 40 wireless Department of Engineering, University of Leicester The University of Nottingham / HICSE, Bradford-on-Avon network nodes and a high quality signal strength and [email protected] Collaborators [email protected] Doppler measuring system). Hitachi Information Control System Europe. Dr David Siddle – Lecturer, UoL Dr David Golightly – Senior Research Fellow, UoN Collaborators Professor Mike Warrington, UoL HORIBA, MIRA. Jerome Clayton – PhD student, UoL Tim Edwards, HORIBA MIRA

PROJECT DESCRIPTION OUTCOME AND IMPACT PROJECT DESCRIPTION OUTCOME AND IMPACT

The propagation environment can have a substantial The urban radio propagation environment is complex, Rail industry stakeholders have a need to predict the effect on the performance of wireless network systems. but it is important to be able to properly assess the effect demands on the operational staff regulating traffic. This D-MOD has resulted in the For example, the range over which a device can operate is on radio systems in order to predict the impact on their means understanding both how operational conditions much less in built-up areas than it is in open terrain. operation. now might influence demand, and also the effect of development of a working demand future technology change such as European Train Control estimation tool, which is based on the This project measured and simulated the effect of the System (ETC) and Traffic Management. To achieve this, The study found that network radio propagation on network performance for a number Hitachi Information Control System Europe - HICSE have industry standard ODEC (Operational of road configurations ranging from built-up (i.e. with performance decreases with distance partnered with the Centre for Rail Human Factors at The Demand Evaluation Checklist). buildings either side) to more open areas (i.e. with from an intersection. University of Nottingham. buildings only one side). Together, the D-MOD project is using HICSE’s simulation In addition, an advanced estimation tool reflecting When the device is more than about 40 metres from the platform to deliver quantitative estimates of traffic demand signalling automation and traffic management HMI (Human junction, the performance is very poor. This limits the range based on dynamic traffic patterns. This will allow rail Machine Interface) is due for delivery later this year. over which two vehicles can communicate with each other stakeholders to deliver demand estimates in a more cost and may reduce the effectiveness of safety critical systems. effective manner, and with greater sensitive and flexibility New models of demand quantification for signalling and than has previously been possible. other similar tasks, such as Air Traffic Control, are also TRANSPORT SYSTEMS FUTURE being developed. D-MOD is part of a KTP programme (Knowledge Transfer Partnership) funded by Innovate UK and EPSRC, to reinforce the links between academic research and industry.

Simulated signalling Workstation and workstation assessor desk

Above: Measured packet delivery ratio (PDR) in an 802.11p system either

| FUTURE TRANSPORT SYSTEMS | INTELLIGENT INFRASTRUCTURE | FUTURE side of a T-junction.

Right: Simulated signal strength of a 802.11p transmitter located near a T-junction. JOURNEY EXPERIENCE JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE |

10 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 11 CAPABILITIES CAPABILITIES

• Prediction of mobility patterns • Urban modelling • Urban development • GNSS obscuration mapping • OD matrix development • Geographical information systems Limits of the predictability Mapping Obscuration of • Inertial navigation sensor integration of human travel GNSS in Urban Landscapes (MOGUL)

Facilities Project contacts Facilities Project contacts High Performance Computing Cluster. Dr James Goulding – Data Science Research Fellow, Transport Systems Catapult, Nottingham City Council. Dr Simon Roberts – Research Fellow, Horizon Digital Economy Research, Nottingham Geospatial Institute, Collaborators The University of Nottingham Collaborators Faculty of Engineering, Millicom, World Bank. [email protected] Nottingham Geospatial Institute Spirent GNSS The University of Nottingham simulator, mobile laboratory fitted with low cost to [email protected] Dr Gavin Smith – Geospatial Research Fellow, UoN survey grade positioning sensors, roof top test track for GNSS testing and simulation.

PROJECT DESCRIPTION OUTCOME AND IMPACT PROJECT DESCRIPTION OUTCOME AND IMPACT

It has recently been claimed that human travel is The project reconsidered the mathematical derivation Within urban areas the poor performance of global In addition to the algorithm described, the techniques theoretically ‘highly predictable’ with an upper bound of the upper bound to human movement predictability. navigation satellite system (GNSS) has a deleterious effect developed form an integral part of projects going forward, of 93% predictability being shown. If true, this has By considering real-world topological constraints, an in the accuracy and solution availability for positioning notably i-MOTORS, which has received £1.7m funding huge import to transport services, in everything from approach to computing a tighter upper bound was and navigation. This at a time when the European Union from Innovate Uk (£450k for University of Nottingham destination prediction to congestion forecasting. Such developed. The results showed that the upper bound is is advocating the development of advanced intelligent Geospatial Institute and Human Factors Research Group) upper bounds provide the theoretical limits to modelling actually between 11-24% less than previously claimed. transportation systems (ITS), that in part rely on GNSS and RHINOS (£250k for NGI). human movement, that we simply cannot push past. Such to direct vehicles, reduce congestion and prioritise the information is not only theoretically valuable, but provides This is particularly evident at fine-grained spatial and movement of emergency vehicles and public transport. The experience gained and the connections made with constraints for designers of location based services to work temporal quantization, where a significant number of the Transport Systems Catapult (TSC), are central to in, whether through ubiquitous advertising, navigation practical applications lie. developing further projects in intelligent mobility services or intelligent agents. This value is only useful, MOGUL has developed a novel at the NGI. however, if the upper bound is tight, i.e. it is close to the true limit of movement predictability. low-cost algorithm for deriving This work raises serious questions about obscuration maps that predict loss This project has shown that regularity in human movement the use of location information alone for

FUTURE TRANSPORT SYSTEMS FUTURE of GNSS signal in urban canyons. was significantly over-estimated – by integrating predicting travel behaviour, indicating topological constraints it has been able to show that human The algorithm has been tested using that we must integrate further travel patterns are much less predictable than first thought. real world data and the Nottingham contextual variables if we are to meet Geospatial Institute’s (NGI) the promise of pervasive computing and GNSS simulator. location based applications.

12 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 13 CAPABILITIES

• Human factors • Passenger experience • Personal (psychological) data • Human computer interaction PASSME – Personalised • Data security • Multi-sensor integration • Optimisation and Airport Systems for Seamless decision-making Mobility and Experience

Facilities Project contacts Usability and Ergonomics Laboratory at Dr Genovefa Kefalidou – Principal Human Factors The University of Nottingham. Research Fellow, Faculty of Engineering, The University of Nottingham Collaborators [email protected] The Netherlands Aerospace Centre NLR , Amsterdam Professor Sarah Sharples – Associate Pro-Vice-Chancellor Airport Schiphol, Hamburg Airport, the German for Research & Knowledge Exchange – UoN Aerospace Centre DLR, the Technical University of Dr Neil De Joux – Research Fellow, UoN Hamburg, the Institute of Communication and Computer Dr Mirabelle D’Cruz – Director of European Research Systems, Almadesign and Carr Communications. (HFRG), UoN

PROJECT DESCRIPTION OUTCOME AND IMPACT

PASSME researchers will investigate critical bottle necks in the airport experience, including luggage, security, PASSME’s outcomes and impact include: boarding, overall passenger flow and passengers’ mobility including for those with reduced mobility. Through this •• reducing door-to-door airport travel time for research, PASSME aims to make the airport experience passengers in Europe by 60 minutes less stressful and more enjoyable for the entire aviation improving luggage handling processes community, from airports and airlines to passengers and •• baggage handlers. •• forecasting passengers’ flow and reducing queues providing passengers with real-time personalised The project’s twelve partners will combine their expertise •• information via a personalised device and smartphone to address this aviation challenge together. application

•• reducing stress levels while at airport and enhancing Funded by the EU’s Horizon 2020 overall passengers’ travel experience while at airports programme, PASSME brings •• improving airports’ and aircraft interiors to enhance passengers’ experience. together leaders in the fields of

aviation, transport, academia, design, ISTOCK © encrier technology and communications. ISTOCK © AMR Image | INTELLIGENT INFRASTRUCTURE | FUTURE TRANSPORT SYSTEMS JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | FUTURE

14 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 15 CAPABILITIES CAPABILITIES

• Infrastructure asset • Advanced data modelling management • Open source data sensors • Advanced data analysis • Geographic information • Modelling and simulation system (GIS) A Petri-Net modelling Variation of pollutant • Air pollution approach to railway bridge concentrations with road asset management infrastructure interventions

Collaborators Project contacts Facilities Project contacts Network Rail. Panayioti Yianni – PhD student, Faculty of Engineering, Leicestershire City County traffic SCOOT (Split Cycle Dr Teresa Raventos – Air Quality Specialist, The University of Nottingham Offset Optimisation Technique) and counters, analysis School of Chemistry, [email protected] facilities servers, cloud and model data. University of Leicester [email protected] Dr Luis Neves – Assistant Professor, UoN Collaborators Dr Dovile Rama – Research Fellow, UoN Leicestershire County Council, Leicester City Council, Professor Paul Monks – Professor in Atmospheric Professor John Andrews – Professor of Infrastructure Environmental Instruments Ltd and Transport Chemistry, UoL Asset Management, UoN Systems Catapult. Dr Zoe Fleming – NCAS researcher, UoL

PROJECT DESCRIPTION PROJECT DESCRIPTION OUTCOME AND IMPACT

The UK railway infrastructure is integral to economic output The influence of mobility upon pollution, or vice versa, is Air quality data acquired from this study is overlaid and growth. Managing such an extensive network however, an area of research that can inform the local authorities on Geographic Information Systems to relate sources poses many issues. The topic of this work is railway and decision makers. The involvement of different of emissions and areas with high levels of pollution. structures, in particular railway bridges. Management of methodologies for the study of complex datasets including The project has mainly focused on nitrogen dioxide such a vast portfolio of structures can be difficult. in-situ monitoring, which have many influencing variables, concentration as the level of air quality, assuming is key to understanding the anthropogenic impact of air road transport is the main source. Using open source Each bridge will have different construction materials and pollution levels in urban areas. approaches has proved useful in understanding the data methods as well as different operational circumstances and over long periods of measurements within particular maintenance regimes. A comprehensive model has been areas of research. created using a state of the art modelling approach. A study was undertaken in North West Leicestershire, where a policy Concentrations of NO2 average in days (Openair). scheme for the improvement of traffic This model incorporates the flow distribution and air quality, complex behavioural processes

led by Leicester City Council and TRANSPORT SYSTEMS FUTURE of deterioration, inspection and Leicestershire County Council, was maintenance. implemented.

The model can be simulated to provide railway bridge managers with predictions of condition, overall cost and The geospatial distribution of works undertaken created a maintenance actions required over the next 100 years. challenge to assess the impact of traffic improvements and required a suitable set of monitoring equipment, which needed to be installed on the road infrastructure, avoiding OUTCOME AND IMPACT disturbances and providing reproducible results. Small sensors are installed throughout the urban network where Understanding the behavioural nature of assets is very road interventions are undertaken to improve traffic flow. important. A study was undertaken into the environmental

| FUTURE TRANSPORT SYSTEMS INTELLIGENT INFRASTRUCTURE | FUTURE factors that affect bridge deterioration, including the bridge type and configuration, the line speed on which the bridge resides and the proximity to the coast were investigated.

This is a breakthrough in bridge asset management. When the results were presented to the collaborating organisation, they were quickly adopted into their in-house modelling method. They are already being used to make the organisations predictions more accurate. JOURNEY EXPERIENCE | JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE |

16 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 17 CAPABILITIES CAPABILITIES

• Advanced transport data • Social media modelling • Disruption management • Personal data Estimating public transport Twitter and Passenger topology from existing Disruption (TaPD) supply data

Facilities Project contacts Facilities Project contacts Access to Integrated Transport Planning’s internal Dr Mark Dimond – KTP Research Associate, Access to a range of University research facilities. Dr David Golightly – Senior Research Fellow, General Transit Feed Specification data assets Integrated Transport Planning Ltd, and Faculty of Engineering, and project teams. The University of Nottingham Collaborators The University of Nottingham [email protected] Transport Systems Catapult, Association of Train [email protected] Collaborators Operating Companies, Network Rail and train Dr Rob Houghton – The University of Nottingham operating companies. Professor Sarah Sharples – The University of Nottingham

PROJECT DESCRIPTION PROJECT DESCRIPTION OUTCOME AND IMPACT

The General Transit Feed Specification (GTFS) has become Twitter and Passenger Disruption (TaPD) is feasibility a standard in recent years for describing public transport project to model how Twitter is used by passengers and The project has resulted in for cities and regions. However, data in this format has transport operators during rail disruption. The aim is to not been exploited to the fullest extent for transport inform cross-industry strategy and processes for passenger engagement with key partners intelligence such as understanding how the components information during disruption events. The project including ATOC as project sponsors, of a network relate to each other, and to the underlying will also deliver an understanding of future research road network. and technology requirements for transport disruption Network Rail and train operating management where social media plays a role. companies. This project aims to extract a topology (a model of the relationships) for transport routes and stops, using well- TaPD combines qualitative interviews of those who known geographic information approaches based upon operate rail Twitter accounts, with analyses of Twitter map matching. The model captures the relationship traffic between train operating companies and passengers Models of the Twitter strategy for rail operators have been between each section of a transport route and the resulting from disruption. developed and compared to support consistency across local road network, which in this case is drawn from train operating companies. OpenStreetMap.

FUTURE TRANSPORT SYSTEMS FUTURE As a result of the project, good practice has been Mapping background © identified and recommendations for Twitter strategies Through a Knowledge Transfer Stamen Design and for future cross train operating companies have been Partnership, Integrated Transport OpenStreetMap contributors. developed which will be included in the next iteration of Planning Ltd and The University of Passenger Information During Disruption (PIDD). Nottingham are developing this The project has also contributed to the Sentiment solution to maximise the value of Mapping Project which the Transport Systems Catapult and Department for Transport have been undertaking. already-collected transport supply data for international transport agency clients.

OUTCOME AND IMPACT

Preliminary testing has been successful and has proven the ability to extract a high-fidelity transport model from the existing GTFS data. The map-matching algorithm used achieves a high level of accuracy with respect to the true behaviour of the transport network, and can be corrected by a knowledgeable user in an online interface. In-house testing on live modelling projects commenced in February ISTOCK © SolStock

JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | 2016. TRANSPORT SYSTEMS JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | FUTURE

18 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 19 20 JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | FUTURE TRANSPORT SYSTEMS Transport Scotland. Leica Geosystems,GVL,AECOM,ARUPand UbiPOS UKLtd.,AMEY, ChinaRailway, Collaborators systems andfieldtests. software platformandaProbe Van forintegratedsensor Institute, latestlocationsensors,anEarthobservation GNSS signalsimulatorattheNottinghamGeospatial Facilities engineering works,landslip,miningandindustrialactivity. threats causedbyenvironmental conditions,landmotion, of thestructure initschanging landscape,identifying loading conditions.Italsoprovides acompletepicture measurements andabnormal oftheirassets duringnormal observation technologiestoprovide userswithreal-time GeoSHM systemintegratesGNSSandEarth maintenance ofdifferent typesofasset. technologies tooffer anintegrated solutionforthe system thatintegratesGNSSandEarthObservation Health Monitoring)isaStructuralMonitoring(SHM) GeoSHM (GNSSandEarthobservationforStructural PROJECT DESCRIPTION monitoring Real timestructural health ISTOCK © BlackJack3D [email protected] The UniversityofNottingham Faculty ofEngineering, Director oftheSino-UKGeospatialEngineeringCentre, Dr XiaolinMeng–AssociateProfessor, Project contacts passenger safety. required theclosure ofthebridgeinorder toensure loadingandadverseweather combination ofabnormal AMEY (thecompanythatmaintainsthebridge), Based ontheseobservations,itwaspossibletoadvise up to 80MpH. up to80MpH. experiencing extreme weather conditionswithwind loading.Atthesametime,area abnormal was maintenanceworks,subjectingthebridgeto to perform girders oftheForthRoad Bridge.Onelanewasclosed In December2015,cracksappeared inoneofthetower OUTCOME ANDIMPACT

IMPETUS THE OPTIMISED MOVEMENT OFGOODS& PEOPLE ISTOCK © Empato of theForthRoadBridgeinreal time. which wasusedtoviewthebehaviour Nottingham GeospatialInstitute, monitoring server, basedat The GeoSHMproject hasadedicated • • management • • GNSS • CAPABILITIES spectral analysis characteristicsand Vibration Structural healthmonitoring Infrastructure asset Earth observation

• Wireless network • Positioning and navigation performance technologies simulation and • GNSS measurement • High-precision, high integrity How the extremes of A Hansel and Gretel positioning • Cooperative positioning wireless network size affect approach to cooperative • Autonomous vehicles data delivery vehicle positioning

Facilities Project contacts Facilities Project contacts Simulation (including high-performance computing) and Dr Alan Stocker – Senior Lecturer, Fully instrumented test vehicle and rail/locomotive Professor Terry Moore – Director of the Nottingham measurement capabilities, including a network with up Department of Engineering, University of Leicester test train track. Both facilities have on-board Geospatial Institute, Faculty of Engineering, to 40 nodes. [email protected] ground-truth systems. The University of Nottingham [email protected] Collaborators Dr David Siddle – Lecturer, UoL Collaborators Approaches from potential collaborators are welcomed. Professor Mike Warrington, UoL MIRA, Leica Geosystems. Dr Xiaolin Meng – Associate Professor, UoN Trevor Wright – NGI Business Innovation Manager, UoN

PROJECT DESCRIPTION PROJECT DESCRIPTION

The study found that the number of nodes in a network An important aspect of Intelligent Transportation Systems can have a significant impact on the performance of that and self-driving cars is the use of sensors to allow network. This project has been examining the extremes of vehicles to follow a safe and legal route of travel. One network size, i.e. for ‘dense’ networks when the number approach to this issue is the use of cooperative vehicle of nodes in a geographical region is high and ‘sparse’ communications allowing data from one vehicle to be networks in areas lacking conventional infrastructure (for passed to another to provide more accurate and real-time example, mobile telephone), when the number of nodes vehicle position information. is sufficiently small that they will often not be able contact each other. The Nottingham Geospatial Institute (NGI) has developed a system based on this approach using carrier-phase GNSS (such as GPS) technology and allowing for corrections to

ISTOCK © phive2015 Real Time Kinematic positioning. Changes to the routing in these networks may be able to reduce The simulated This is combined with a Hansel & packet delivery

the impact of the network size on TRANSPORT SYSTEMS FUTURE ratio (PDR) for a Gretel marker approach of dropping performance. wi-fi ad-hoc in ‘bread crumbs’ along the route – a a geographical area 3x2 km as trail of packets of information that can a function of the number of be used by other nearby vehicles for OUTCOME AND IMPACT nodes with two a short period. propogation Congestion in wireless networks can be a serious problem modesl, two ray as the number of users in a geographical area increases. ground, (TRG) and ITU-R. P.1411 The system was tested by the use of NGI’s own For safety critical systems (for example, collaborative instrumented test vehicle and a second vehicle on a driving), packet loss will present serious problems. This roof-based test track which effectively provided a truthing project has indicated that it is possible to optimise routing system to test the concept. protocols to reduce the packet loss. The PDR and delivery time for a ZigBee ‘epidemic’ (or OUTCOME AND IMPACT ‘gossip’) network in a 200x200 km The tests clearly outlined the potential benefit of vehicle region. to vehicle communication to improve positioning accuracy based on the use of one vehicle’s knowledge of absolute position to assist a second vehicle using established GNSS techniques. The system was shown to provide a potential positioning accuracy of 20 -30 centimetres, with a message latency of less than 30 seconds. | INTELLIGENT INFRASTRUCTURE | FUTURE TRANSPORT SYSTEMS JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | FUTURE JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE |

22 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 23 CAPABILITIES CAPABILITIES

• Unmanned aerial vehicles • GNSS data analysis (UAVs) • Algorithm development • Urban modelling • Data integration and • Demographic prediction visualisation Community and drone TFL buses as sentinels • GIS (Geographical information systems) mapping in Tanzania

Facilities Project contacts Facilities Project contacts Access to a range of University research facilities. Dr James Goulding – Data Science Research Fellow, The Nottingham Geospatial Institute (NGI) has a roof Dr Oluropo Ogundipe – Research Fellow, Horizon Digital Economy Research, based dynamic test platform, Spirent GSS8000 multi- Faculty of Engineering, The University of Nottingham The University of Nottingham GNSS simulator and other facilities. [email protected] Collaborators [email protected] World Bank, Tanzanian commission for science and Collaborators technology (COSTECH), IBM Research Kenya. Dr Mark Iliffe – Business School Research Fellow, UoN Transport Systems Catapult and Transport for London.

PROJECT DESCRIPTION OUTCOME AND IMPACT PROJECT DESCRIPTION OUTCOME AND IMPACT

Even basic maps are scarce in many developing nations. Open, aerial imagery coverage has now been produced London is a busy and active metropolis with a complex GPS bus data and other road data was collected for the Unmanned aerial vehicles or drones are emerging as a tool as part of this project for over 1.5 million residents of Dar transport network. The aim of the project was to assess Wembley area, for the week before and during the FA Cup for rapid remote imaging in a much more cost efficient es Salaam, data that has not existed previously. This can whether GPS data from the 8,500+ London buses could football final. The feasibility study enabled an assessment manner than using satellite imagery. be used to investigate and overlay data on transport, be used to derive insights from the bus movement to be made about the amount of work required in building epidemiology and natural hazards, such as flooding. This characteristics, with respect to the driving and influencing a predictive analysis platform using the GPS bus data and has been instrumental in cross-supporting Department for forces in the city and how they might impact on traffic other data sources. International Development (DFID) research investigating congestion, people movements and resource deployment. In pioneering the use of drones in behaviour during flood events. The use of drones has The project showed that the traffic pattern had numerous emerging nations, such as Tanzania, also kicked started discussion within Tanzania on policy Data from other sources was collected and correlated. This drivers, some which were clearly evident and others that the EPSRC Neo-demographics project development on how to fully exploit drone technology for included information on sporting events, road closures or were difficult to isolate. It also identified the issue of data social good. roadworks and other activities. quality, as significant effort was spent in data cleaning at The University of Nottingham is prior to any data analysis and visualisation. The project supporting the World Bank in order introduced a new approach to the Transport For London team, for analysing the GPS bus data. to fly and analyse the results of drone Pattern analysis of this nature, FUTURE TRANSPORT SYSTEMS flights over Dar es Salaam. drawing on other available data feeds, is widely expected to inform The team is investigating how the resulting imagery can predictive analyses and autonomous be used to derive data on infrastructure, such as roads and system controls that will underpin buildings, mapping Dar es Salaam (Africa’s fastest growing city), in ways that have not been done before. intelligent mobility. | FUTURE TRANSPORT SYSTEMS INTELLIGENT INFRASTRUCTURE | FUTURE JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | JOURNEY EXPERIENCE |

24 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 25 CAPABILITIES CAPABILITIES

• Location-based technologies • Earth observation • Traveller behaviour/mode shift • Detection and monitoring of • Mobility as a service ground deformation relevant • Personal data to large infrastructure The Smart Campus project Satellite monitoring of projects ground motion at airports in green sites

Facilities Project contacts Facilities Project contacts Hydrogen refuelling facilities; fleet of Electric Vehicles Dr Nancy Hughes – Research Fellow, Human Factors The Punnet software, developed by The University of Professor Stuart Marsh – Professor of Geospatial with GPS; free intercampus bus service with on-board Research Group, Faculty of Engineering Nottingham, extracts ground motion over both rural Engineering, Faculty of Engineering, Eduroam (i.e. providing greater intelligence on mobility The University of Nottingham and urban land cover from stacks of satellite radar The University of Nottingham patterns); bike hire and car share schemes; 7 Creative [email protected] observations over extended time periods. [email protected] Energy Homes; renewable energy infrastructure; extensive building management system. Professor Sarah Sharples – Associate Pro-Vice-Chancellor for Collaborators Dr Andy Sowter – Assistant Professor, UoN Research & Knowledge Exchange, UoN Arup Group Limited. Collaborators Andy Nolan – Director of Sustainability, UoN Nottingham City Council, Transport Systems Catapult, Emma Kemp – Senior Environmental Officer, UoN Satellite Systems Catapult.

PROJECT DESCRIPTION PROJECT DESCRIPTION

The aim of The University of Nottingham’s Smart Campus When planning the construction of a vital piece of project is to develop a campus that is an efficient, safe, infrastructure, such as an airport, it is important to identify, sustainable, responsive and enjoyable place to study, work and mitigate against, hazards due to land subsidence. In and live, underpinned and enhanced by digital / internet rural areas, subsidence is difficult, time-consuming and based technologies. costly to detect when using ground-based measurements and space-based alternatives. This will be achieved by using the campus as a ‘vehicle’ through which to research, develop and evaluate user The NGI (Nottingham Geospatial Institute) have behaviours (staff, students and visitors) and trial emerging developed a satellite data processing solution that can Smart Campus concepts and demonstrators, primarily comprehensively describe land motion, particularly over within the area of intra /inter campus mobility. In addition, rural and vegetated terrain, something that is difficult for by being embedded within an existing city, multi-site and more conventional approaches. encircling a major hospital, the campus also serves as an ideal test-bed for related Smart City initiatives, which can The solution has been demonstrated using radar be evidenced in a meaningful yet more manageable way. data from the new European Sentinel-1 satellite over Mexico City, where previously unseen patterns of land deformation have been revealed over this primarily rural OUTCOME AND IMPACT new airport site.

The reality of developing a Smart Campus is highly complex and challenging, involving a wide range of OUTCOME AND IMPACT stakeholders, with a diversity of motivations and goals. For this reason, and as indicated above, the project will initially focus on understanding campus/user mobility patterns, by The demonstration showed that analysing positional (mobile phone) data in conjunction with evidence gathered through focus groups, interviews significant land motion was detected and diary logs. For example, inter-campus bus usage will over the complete city, including the be examined, as the basis for more efficient, cost effective, demand-led provision. mainly rural airport site where other | FUTURE TRANSPORT SYSTEMS INTELLIGENT INFRASTRUCTURE | FUTURE

FUTURE TRANSPORT SYSTEMS | INTELLIGENT INFRASTRUCTURE | FUTURE methods have failed to make any

measurements at all. This understanding in turn will help to

shape future information, timetabling Extent and magnitude agree with observations over the and service provision, whilst also city area, geotechnical models and groundwater data. enhancing the overall user experience. JOURNEY EXPERIENCE JOURNEY EXPERIENCE |

26 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 27 28 JOURNEY EXPERIENCE | INTELLIGENT INFRASTRUCTURE | FUTURE TRANSPORT SYSTEMS transport managementservices. (KTP) project incollaborationwith3T, aprovider of search routines. ThiswasaKnowledgeTransfer Partnership incorporated clusteringalgorithmsandtailored heuristic be implementedinpractice.Thedevelopedoptimiser generate high-qualitytransportationplanssuitableto employ computationalintelligentsearch toautomatically The challengewastodevelopoptimisationmodelsand optimisation andcomputationalintelligencetechniques. plans forheavygoodsvehicles(HGVs),through theuseof component toautomatethegenerationoftransportation The aimwastodevelopanoptimisationenginesoftware PROJECT DESCRIPTION 3T LogisticsLtd. Collaborators 3T SOLOSystem,Group. Facilities 3T Logistics planning operations at Transforming transport

ISTOCK © pumpapictures transport logisticoperations. different locationsindynamic the transportationofgoodsbetween pickup anddeliverytimes,etc.for of vehicleloading,routing, transportation planswithdetails The objectiveistoproduce efficient www.cs.nott.ac.uk/~jds [email protected] The UniversityofNottingham School ofComputerScience ASAP Research Group Dr DarioLanda-Silva Project contacts • incorporated intotheoptimiserincluding: delivery operations.Severalresearch outcomeswere of-the-art computeralgorithmsforplanningshipment process withanautomated systemthatutilisesstate- company, replacing amostlymanualtransportplanning thecore solo/), whichhelpedtotransform businessofthe transport managementsystem(http://web.3t-europe.com/ Optimizer moduleinto3T’s SOLO,aworld-classclass The optimisationenginedevelopedwasdeployedasthe OUTCOME ANDIMPACT

• • infeasibility •

ac.uk/~pszjds/research/files/dls_mic2011.pdf See publishedrelated article:http://www.cs.nott. IMPETUS THE OPTIMISED MOVEMENT OFGOODS& PEOPLE • • • • multi-objective optimisationtotacklecostreduction, vehicle utilisationincrease, andsatisfactionofdelivery heuristic search transitions betweenfeasibilityand time windows specialised heuristicsearch operatorsfortheefficient self-adaptive feedbackheuristicsearch forreacting to generation andimprovement ofvehicleloads the qualityofsolutionsandadaptcomputation time accordingly. • • • CAPABILITIES Advanced datamodelling Algorithm development Operations andlogistics

ISTOCK © Bet_Noire 29 For more information about IMPETUS, contact: [email protected] +44 (0)115 951 4196 or [email protected] +44 (0)115 748 6068

www.nottingham.ac.uk/impetus

30 IMPETUS THE OPTIMISED MOVEMENT OF GOODS & PEOPLE 31