2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

Research and innovation in smart mobility and services in Europe: An assessment based on the Transport Research and Innovation Monitoring and Information System (TRIMIS)

For smart mobility to be cost-efficient and ready for future needs, adequate research and innovation (R&I) in this field is necessary. This report provides a comprehensive analysis of R&I in smart mobility and services in Europe. The assessment follows the methodology developed by the European Commission’s Transport Research and Innovation Monitoring and Information System (TRIMIS). The report critically assesses research by thematic area and technologies, highlighting recent developments and future needs.JRC.C.4-Sustainable Transpor

Indirect structural health monitoring (iSHM) of transport infrastructure in the digital age

Workshop reportCopyright © Joint Research Centre (European Commission). The existing European motorway infrastructure network is prone to ageing and subject to natural events (e.g. climate change) and hazards (e.g. earthquakes), necessitating immediate actions for its maintenance and safety. Within this context, the structural health monitoring (SHM) framework allows a quantitative assessment of the structural integrity, serviceability and performance, facilitating better-informed decisions for the management of the existing infrastructure. The European Commission Joint Research Centre (JRC) established the exploratory research project MITICA (Monitoring Transport Infrastructures with Connected and Automated vehicles) to investigate the opportunity to use novel methods for infrastructure motoring, aiming at the efficient maintenance of the European aging road infrastructure. This report summarizes the discussion and the outcomes of a workshop held at the JRC in Ispra (Italy) on June 6-7 2022, as part of the MITICA project. Considering the EU priority “A Europe fit for the digital age”, the workshop was dedicated to SHM and its application to civil infrastructure, focusing on innovative indirect structural health monitoring (iSHM) approaches that rely on the vehicle-bridge interaction and the deployment of sensor-equipped vehicles for the monitoring of the existing bridge infrastructure. The report aims to become a reference document in the area of iSHM using passing vehicles, for both scholars and policy makers

Applying Reinforcement Learning to Optimize Traffic Light Cycles

Manual optimization of traffic light cycles is a complex and time-consuming task, necessitating the development of automated solutions. In this paper, we propose the application of reinforcement learning to optimize traffic light cycles in real-time. We present a case study using the Simulation Urban Mobility simulator to train a Deep Q-Network algorithm. The experimental results showed 44.16% decrease in the average number of Emergency stops, showing the potential of our approach to reduce traffic congestion and improve traffic flow. Furthermore, we discuss avenues for future research and enhancements to the reinforcement learning model

An analysis of possible socio-economic effects of a Cooperative, Connected and Automated Mobility (CCAM) in Europe

A Cooperative, Connected and Automated Mobility (CCAM) is likely to have significant impacts on our economy and society. It is expected that CCAM unveils new and unprecedented mobility opportunities that hold the potential to unlock a range of safety, environmental and efficiency benefits. At the same time, it is anticipated that it will bring deep changes in the labour market, progressively making some occupations and skills less relevant, while at the same time opening up new opportunities for different businesses and requiring new and more advanced skills. With Europe accounting for 23% of global motor vehicle production (Acea Statistics, 2016) and almost 72% of inland freight transported by road in Europe (European Commission, 2017a), the full deployment of Connected and Automated Vehicle (CAV) technologies is expected to have a substantial impact on the European economy. The economic impacts of CAVs will go far beyond the automotive industry, into sectors like insurance, maintenance and repair or health, among others. While it is clear that CAVs could offer unique opportunities for value creation, it is also essential to acknowledge that they might imply a substantial transformation of our industries and our social and living systems. The study is aimed at analysing the value at stake for both industry and society as a result of a transition towards a CCAM mobility in Europe. It aims at identifying the economic sectors that are most likely to be affected by CCAM as well as the influencing factors driving future changes in each sector. The ultimate goal is to estimate ranges of potential effects for the main affected sectors, with the support of a set of scenarios. The study also aims at analysing the potential effects of CCAM on the workforce and pursues the identification of skills that need to be addressed in the mobility transition. The focus of the study is exclusively paid on road transport and covers both passenger and freight transport.JRC.C.4-Sustainable Transpor

Building a large-scale micro-simulation transport scenario using big data

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources

Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems

This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models in reproducing measured trajectories of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics-based extensions: namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. This framework has been applied to the Intelligent Driver Model (IDM), Gipps’ model, and to three basic ACC algorithms. These are linear controllers which are coupled with a constant time-headway spacing policy, and with two other policies derived from the traffic flow theory: the IDM desired distance function, and Gipps’ equilibrium distance-speed function. The ninety models resulting from the combination of the five base models with the aforementioned extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of vehicles driven by ACC systems. Overall, the study has shown that physics-based extensions provide limited improvements to the accuracy of existing models. In addition, if an investigation against measured data is not carried out, it is not possible to argue which extension is the most suited for a specific model. The linear controller with Gipps’ spacing policy has resulted the most accurate model, while the IDM the most robust to different input trajectories. Eventually, all models have failed to capture the behaviour of some car brands – just as models fail with some human drivers. Therefore, the choice of the “best” model is independent of the car brand to simulate