Characterising Green Light Optimal Speed Advisory trajectories for platoon-based optimisation |
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| Institution: | 1. School of Civil Engineering, University of Queensland, St Lucia, Queensland 4072, Australia;2. Intelligent Transport Systems Lab, Swinburne University of Technology, John St, Hawthorn, Victoria 3122, Australia;1. School of Transportation Science and Engineering, Harbin Institute of Technology, China;2. Office of Operation Research and Development, Federal Highway Administration, United States;3. Department of Transport & Planning and Department of BioMechanical Engineering, Delft University of Technology, Netherlands;4. Department of Civil and Environmental Engineering, University of Virginia, United States;1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China;2. Xuchang University, Xuchang, Henan Province 461000, China;3. China Southwest Architectural Design and Research Institute Co., Ltd., Chengdu, Sichuan Province 610041, China;4. Texas Southern University, 3100 Cleburne Avenue, Houston, TX 77004, USA;1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China;2. School of Automobile Engineering, Harbin Institute of Technology at Weihai, China;1. King Saud University, Riyadh, Saudi Arabia;2. Center for Sustainable Mobility, Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA;3. Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Tech, Center for Sustainable Mobility, Virginia Tech Transportation Institute, 3500 Transportation Research Plaza, Blacksburg, VA 24061, USA;4. Université de Tunis El Manar, Ecole Nationale d''ingénieur de Tunis, LR11ES16 Laboratoire de matériaux, d''optimisation et d''environnement pour la durabilité, B.P. 37 Le Belvédére, 1002 Tunis, Tunisia;5. Ain-Shams University, Cairo, Egypt |
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| Abstract: | Conceptually, a Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles, allowing them to pass through an intersection during the green interval. In previous papers, a single speed is computed for each vehicle in a range between acceptable minimum and maximum values (for example between standstill and the speed limit). This speed is assumed to be constant until the beginning of the green interval, and sent as advice to the vehicle. The goal is to optimise for a particular objective, whether it be minimisation of emissions (for environmental reasons), fuel usage or delay. This paper generalises the advice given to a vehicle, by optimising for delay over the entire trajectory instead of suggesting an individual speed, regardless of initial conditions – time until green, distance to intersection and initial speed. This may require multiple acceleration manoeuvres, so the advice is sent as a suggested acceleration at each time step. Such advice also takes into account a suitable safety constraint, ensuring that vehicles are always able to stop before the intersection during a red interval, thus safeguarding against last-minute signal control schedule changes. While the algorithms developed primarily minimise delay, they also help to reduce fuel usage and emissions by conserving kinetic energy. Since vehicles travel in platoons, the effectiveness of a GLOSA system is heavily reliant on correctly identifying the leading vehicle that is the first to be given trajectory advice for each cycle. Vehicles naturally form a platoon behind this leading vehicle. A time loop technique is proposed which allows accurate identification of the leader even when there are complex interactions between preceding vehicles. The developed algorithms are ideal for connected autonomous vehicle environments, because computer control allows vehicles’ trajectories to be managed with greater accuracy and ease. However, the advice algorithms can also be used in conjunction with manual control provided Vehicle-to-Infrastructure (V2I) communication is available. |
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| Keywords: | Green Light Optimal Speed Advisory Vehicle-to-infrastructure communication Connected vehicles Trajectory control |
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