A dynamic optimization method for adaptive signal control in a connected vehicle environment

Abstract

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner.

Keywords:

• Adaptive signal control
• connected vehicle
• genetic algorithm
• dynamic platoon dispersion model
• rolling horizon
• penetration rate

Acknowledgments

The authors are grateful to the three anonymous reviewers for sharing their research insights and providing helpful comments to improve the quality of the paper.

Additional information

Funding

The paper received research funding support from the Open Fund Project of Chongqing Key Laboratory of Traffic and Transportation (2018TE01), Chinese National Natural Science Fund (5157765, 61703064, 71771190), Chengdu Science and Technology Project (No. 2017-RK00-00362-ZF), Chongqing Research Program of Basic Research and Frontier Technology(cstc2017jcyjAX0473), and Southwest Jiaotong University Graduate Academic Training and Promotion Program (2019KCJS46).