Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment

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Abstract

Autonomous vehicle is able to facilitate road safety and traffic efficiency and has become a promising trend of future development. With a focus on highways, existing literatures studied the feasibility of autonomous vehicle in continuous traffic flows and the controllability of cooperative driving. However, rare efforts have been made to investigate the traffic control strategies in autonomous vehicle environment on urban roads, especially in urban intersections. In autonomous vehicle environment, it is possible to achieve cooperative driving with V2V and V2I wireless communication. Without signal control, conflicted traffic flows could pass intersections through mutual cooperative, which is a remarkable improvement to existing traffic control methods. This paper established a cellular automata model with greedy algorithm for the traffic control of intersections in autonomous vehicle environment, with autonomous vehicle platoon as the optimization object. NetLogo multiagent simulation platform model was employed to simulate the proposed model. The simulation results are compared with the traffic control programs in conventional Synchro optimization. The findings suggest that, on the premises of ensuring traffic safety, the control strategy of the proposed model significantly reduces average delays and number of stops as well as increasing traffic capacity.

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Wu, W., Liu, Y., Xu, Y., Wei, Q., & Zhang, Y. (2017). Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment. Journal of Sensors, 2017. https://doi.org/10.1155/2017/9436054

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