Characteristics of Mixed Traffic Flow in Two-lane Scenario Based on Cooperative Gaming Method
Journal article, 2019

This paper aims to explore the impacts of connected and automated vehicles (CAV) on traffic flow efficiency based on in-depth microscopic simulation studies using cooperative gaming method. First, the Gipps car-following models were integrated into an improved cellular automata model to mimic the regular vehicle's driving behavior. Then, cooperative gaming method integrated with enhanced Q-learning was employed as the modeling platform for CAV, to strengthen the capability of the simulation system in realistically reproducing CAV lane changing and car following behavior. Finally, a 2-lane freeway stretch was applied to our simulations, and with extensive simulation studies we obtained some promising results. The study results suggest that the impacts of CAV are quite positive. The inclusion of CAV considerably improves traffic flow, mean speed, and traffic capacity. Such understanding is essential for research concerning CAV as well as the CAV implication for future traffic management and control.

Reinforcement learning

Mixed traffic flow

Cooperative gaming

Cellular automata

Author

Jingqiu Guo

Tongji University

Shouen Fang

Tongji University

Xiaobo Qu

Chalmers, Architecture and Civil Engineering, GeoEngineering

Yibing Wang

Zhejiang University

Yangzexi Liu

Tongji University

Tongji Daxue Xuebao/Journal of Tongji University

0253-374X (ISSN)

Vol. 47 7 976-983

Subject Categories

Transport Systems and Logistics

Infrastructure Engineering

Vehicle Engineering

DOI

10.11908/j.issn.0253-374x.2019.07.009

More information

Latest update

11/12/2019