TY - JOUR
PY - 2021//
TI - Trajectory optimization of CAVs in freeway work zone considering car-following behaviors using online multiagent reinforcement learning
JO - Journal of advanced transportation
A1 - Zhu, Tong
A1 - Li, Xiaohu
A1 - Fan, Wei
A1 - Wang, Changshuai
A1 - Liu, Haoxue
A1 - Zhao, Runqing
A1 - Chen, Xinqiang
SP - e9805560
EP - e9805560
VL - 2021
IS -
N2 - Work zone areas are frequent congested sections considered as the freeway bottleneck. Connected and autonomous vehicle (CAV) trajectory optimization can improve the operating efficiency in bottleneck areas by harmonizing vehicles’ manipulations. This study presents a joint trajectory optimization of cooperative lane changing, merging, and car-following actions for CAV control at a local merging point together with upstream points. The multiagent reinforcement learning (MARL) method is applied in this system, with one agent providing a merging advisory service at the merging point and controlling the inner-lane vehicles’ headway for smooth outer-lane vehicle merging, while other agents provide lane-changing advisory services at advance lane-changing points to control how vehicles make lane changes in advance and perform corresponding headway adjustment, similar to and jointly with the merging advisory service. Uniting all agents, the coordination graph (CG) method is applied to seek the global optimum, overcoming the exponential growth problem in MARL. Using MATLAB and the VISSIM COM interface, an online simulation platform is established. The simulation results show that MARL is effective for online computation with in-timing response. More importantly, comparisons of the results obtained in various scenarios demonstrate that the proposed system obtained smoother vehicle trajectories in all controlled sections, rather than only in the merging area, indicating that it can achieve better traffic conditions in freeway work zone areas. Language: en
LA - en
SN - 0197-6729
UR - http://dx.doi.org/10.1155/2021/9805560
ID - ref1
ER -