Signal timing at an isolated intersection under mixed traffic environment with self‐organizing connected and automated vehicles

Abstract

This study provides a signal timing model for isolated intersections under the mixed traffic environment consisting of connected and human‐driven vehicles (CHVs) and connected and automated vehicles (CAVs). Different from existing studies, CAVs are not controlled by traffic controllers and conduct trajectory planning themselves, which are called self‐organizing CAVs (SOCAVs). The specific trajectory planning strategies of SOCAVs are not accessible to traffic controllers either. The signal optimization problem is formulated as a mixed integer linear programming (MILP) model for total vehicle delay minimization. The states of SOCAVs and CHVs passing the stop bar are predicted without prior information of the trajectory planning strategies of SOCAVs. SOCAVs can lead approaching platoons to pass the intersection effectively, and such “leading effects” of SOCAVs are utilized. Phase sequence and duration are optimized with the “structure‐free” phasing scheme. A parallel particle swarm optimization algorithm with a grouping strategy is designed to solve the optimization model at a reduced scale for computational efficiency. Numerical studies validate that (1) the proposed algorithm significantly outperforms the benchmark method, which directly solves the proposed MILP model using the solver Gurobi 9.0, under medium and high traffic demand; and (2) the proposed model significantly outperforms fixed‐time and vehicle‐actuated signal control in terms of vehicle delay and throughput. Sensitivity analysis shows that the SOCAV penetration rate of 30% is sufficient to guarantee satisfactory performance of the proposed signal timing model.

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

“…Owing to the large data volume of point clouds, M23 is time‐consuming even though the parallel computing technique is applied (Ma et al. 2023).…”

“…The assumption about connected environment brings about many new potentials. Ding et al (2022) and Ma, Yu, et al (2023) optimized traffic signals at isolated intersections in a connected traffic environment. Based on the high-quality and fine-grained path data provided by connected and autonomous vehicles (CAV), and Shi, Nie, et al (2022) proposed deep reinforcement learning methods to control CAV and buses, respectively, in a distributed manner.…”

“…(2022) and Ma, Yu, et al. (2023) optimized traffic signals at isolated intersections in a connected traffic environment. Based on the high‐quality and fine‐grained path data provided by connected and autonomous vehicles (CAV), Shi, Zhou, et al.…”

“…Specifically, the real‐world scenes are commonly more complex than those in the simulation platform. In this regard, the simulation‐based optimization results may be unreliable because of domain shift (Huch et al., 2023; Ma, Yu, et al., 2023).…”

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

“…Numerous control methods have emerged, with a common goal of enhancing signal operations through the consideration of approaching vehicle trajectories (Feng et al., 2015; W. Li & Ban, 2019; D. Li et al., 2023; Liang et al., 2020; W. Ma et al., 2020; Wang et al., 2021) or planning vehicle trajectories based on signal status (He et al., 2015; S. E. Li et al., 2015; Stebbins et al., 2017; X. Wu et al., 2015; Zhou et al., 2017). Furthermore, the other studies focus on the joint optimization of traffic signals and vehicle trajectories, known as signal‐vehicle coupled control (SVCC; Feng et al., 2018; Y. Guo et al., 2019; Z. Li et al., 2014; Liu et al., 2022; C. Ma et al., 2023; Soleimaniamiri et al., 2020; Yu et al., 2018). These studies have substantiated the benefits of CAVs in improving mobility, fuel efficiency, and safety at signalized intersections.…”