Optimal Lane Management Model for Mixed Traffic Flow With Connected Automated Vehicles on Freeways
Zhihong Yao, Chengxin Fu, Yi Wang, Yunxia Wu, Yangsheng Jiang, Bin Ran
Abstract
A mixed traffic flow with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) will persist for a long time. It is essential to utilize the limited road resources of freeways to improve traffic efficiency in the traffic flow environment. To address this issue, this paper proposes an optimization model for freeway lane management strategies. Firstly, three lane management strategies, dedicated lane (DL), general lane (GL), and mixed lane (ML), are introduced. Secondly, the capacity model of different lane management strategies is derived based on headway and its probability. Then, an optimization model for freeway lane management is developed. The objective function is freeway throughput, and the constraints are traffic demand, heterogeneous traffic flow distribution balance, and fair allocation of road resources for heterogeneous traffic flow. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The results show that (1) the throughput of freeway segments increases with the penetration rate (PR) of CAVs; (2) when the PR reaches a certain level, the formation of CAVs platoons has a significant impact on the throughput of freeways; (3) different lane management strategies exhibit significant differences in the throughput of freeways. Given the traffic demand and PR, there is an optimal combination of lane management strategies. Therefore, the proposed model can theoretically support lane management of freeways in the mixed traffic environment.