Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection
AbstractThis research proposed an eco-approach and departure system for left-turn vehicles at a fixed-time signalized intersection. This system gives higher priority to enhancing traffic safety than improving mobility and fuel efficiency, and optimizes the entire traffic consisted of connected and automated vehicles (CAVs) and conventional human-driven vehicles by providing ecological speed trajectories for left-turn CAVs. All the ecological speed trajectories are offline optimized before the implementation of system. The speed trajectory optimization is constructed in Pontryagin’s Minimum Principle structure. The before and after evaluation of the proposed system shows the percentage of vehicles that drive pass the intersection at safe speed increases by 2.14% to 45.65%, fuel consumption benefits range 0.53% to 18.44%, emission benefits range from 0.57% to 15.69%, no significant throughput benefits is observed. The proposed system significantly enhances the traffic safety and improves the fuel efficiency and emission reduction of left-turn vehicles with no adverse effect on mobility, and has a good robustness against the randomness of traffic. The investigation also indicates that the computation time of proposed system is greatly reduced compared to previous eco-driving system with online speed optimization. The computation time is up to 0.01 s. The proposed system is ready for real-time application. View Full-Text
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Jiang, H.; An, S.; Wang, J.; Cui, J. Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection. Sustainability 2018, 10, 273.
Jiang H, An S, Wang J, Cui J. Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection. Sustainability. 2018; 10(1):273.Chicago/Turabian Style
Jiang, Huifu; An, Shi; Wang, Jian; Cui, Jianxun. 2018. "Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection." Sustainability 10, no. 1: 273.
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