期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 70, 期 5, 页码 4225-4237出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3070445
关键词
Optimization; Roads; Acceleration; Trajectory; Fuel economy; Biological system modeling; Vehicle dynamics; Eco-driving; signal priority; urban arterial roads; cooperative vehicle infrastructure systems
资金
- National Natural Science Foundation of China [61573077, U1808205]
This paper investigates a cooperative optimization framework for eco-driving and signal priority for freight vehicles on signalized arterial roads. By optimizing speed trajectories and signal timing, the goal is to minimize fuel consumption of freight vehicles and queueing delay of passenger vehicles at intersections, with the aim of achieving freight-priority. A sub-optimal strategy is proposed to address the non-convex optimization problem caused by constraints from queues and traffic signals. Comparison studies show significant improvement in traffic efficiency and fuel economy with faster calculation.
This paper investigates a cooperative optimization framework of eco-driving and signal priority for freight vehicles in signalized arterial road. Considering the congestion of passenger vehicles at the signalized intersections, the problem is modeled as a multi-stage mixed integer optimization problem containing continuous variables (speed trajectory) and discrete ones (signal timing). The optimal speed trajectory is obtained with the objective of minimizing the fuel consumption of freight vehicles. Meanwhile, freight-priority optimal signal timing is achieved by minimizing the queueing delay of passenger vehicles at the intersections, while ensuring freight vehicles to avoid stopping and idling. The cooperative optimization problem is non-convex due to constraints from the queues and traffic signals in the network; therefore, a sub-optimal strategy restoring convexity is proposed to solve the problem. Comparison studies of the result and some benchmark methods are performed via numerical simulations, showing significant improvement of traffic efficiency and fuel economy with faster calculation.
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