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Machine Learning Tools For Optimization Of Fuel Consumption At Signalized Intersections In Connected/Automated Vehicles Environment, Saleh Ragab Mousa
Machine Learning Tools For Optimization Of Fuel Consumption At Signalized Intersections In Connected/Automated Vehicles Environment, Saleh Ragab Mousa
LSU Doctoral Dissertations
Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. …