Project description DEENESFRITPL Paving the way for traffic flow efficiency It is estimated that the extension and maintenance of traffic hotspots will require investments of billions of euros by 2025. Increasing traffic in urban areas creates new challenges that existing systems cannot fulfil. As a consequence, CO2 emissions and fuel consumption are high due to the time drivers spend behind the traffic hotspots. The EU-funded CEGUM project will create a method that takes into consideration both traffic flow efficiency and vehicle speed. CEGUM will evaluate and test the method taking into account the number of traffic hotspots, fuel consumption and pollution. The project aims to support an effective, safe and environmentally friendly urban mobility by coordinating all the traffic signals. Show the project objective Hide the project objective Objective Traffic hotspots in Europe are expected to generate over 203 billion Euro of economic cost by 2025. Traffic congestion in cities raises serious concerns on the level of CO2 emissions, fuel consumption and discomfort to drivers due to prolonged travel time and large idle time behind the red lights. Unnecessary acceleration and deceleration towards traffic junctions can also highly contribute to extensive use of fuel, increase in emissions and engine and brake wear. Current traffic signal control approaches are not adequate for tackling the problem because they focus on traffic flow efficiency and disregard the individual drivers’ interests. The aim of this project is to develop a method that does not only coordinate all the traffic signals in an urban network, but also takes the vehicle speeds in to account and effectuates a coordination between the traffic lights and vehicle speeds alike. Such a coordination will make the most of the infrastructure and technological developments for reaching an efficient, eco-friendly, and safe urban mobility. Due to the complexity and stochastic nature of the traffic system, the coordination problem will be addressed by integrating model-based distributed control techniques with reinforcement learning-based algorithms. Comprehensive analysis and benchmarking will be performed to obtain a complete evaluation of the idea and prove its effectiveness in improving urban mobility with respect to different indices like fuel consumption, CO2 emissions and number of stops. Adaptive cruise control systems already installed in a large number of cars in European countries. Automakers are announcing the marketing of autonomous cars in the coming years. Soon European cities should be ready to make the most of these technological developments to provide more efficient, cleaner and safer urban mobility and this project is a strong step towards this direction. Fields of science engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systemsengineering and technologyenvironmental engineeringenergy and fuels Keywords Traffic signal control vehicle speed control distributed model predictive control reinforcement learning Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2018 - Individual Fellowships Call for proposal H2020-MSCA-IF-2018 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU Net EU contribution € 214 158,72 Address Hogskoleringen 1 7491 Trondheim Norway See on map Region Norge Trøndelag Trøndelag Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
