Signal Control Method of Adaptive Traffic Events in Connected Traffic Environment

doi:10.12141/j.issn.1000-565X.240533

Abstract

Abstract:

The existing signal control methods for traffic incidents often fail to account for the impact of traffic flow redistribution caused by current intersection control scheme adjustments on adjacent intersections. This oversight essentially shifts traffic problems to adjacent intersections rather than resolving congestion effectively. In view of this, this study proposes an adaptive traffic incident signal control method (SCM-ATE) by leveraging the testability of networked traffic, the controllability of connected autonomous vehicles (CAVs), and the inducibility of connected human-driven vehicles. The SCM-ATE method uses the shortest path algorithm to plan the diversion path of obstructed traffic flow based on the determination of lanes and traffic flow caused by events, as well as the sufficient traffic capacity of adjacent intersections. The optimization objective is to minimize the average delay of vehicles at all intersections on the diversion path. A dynamic programming approach is then applied to jointly optimize signal timings and trajectories of connected vehicles along the designated route, thereby mitigating the adverse effects of incidents. The simulation results show that under low, medium, and high traffic loads, compared with traditional signal control methods, the SCM-ATE method reduces the average delay of vehicles by 12.56%, 20.34%, and 5.29%, respectively. Compared with the single-layer method using single intersection traffic signals and collaborative vehicle trajectory joint optimization (JOTS-CVT), the average delay of vehicles is reduced by 13.27%, 10.40%, and 1.25%, respectively. These outcomes confirm the effectiveness of SCM-ATE in enhancing traffic efficiency. Further research shows that the intersection traffic load and the penetration rate of networked autonomous vehicle have a significant impact on the optimization effect of SCM-ATE and the SCM-ATE method is more suitable for traffic scenarios where the penetration rate of networked autonomous vehicle is ≥ 0.3 and the lane flow rate ratio at the intersection is ≤ 0.7.

Key words: connected traffic, traffic diversion, signal control method, adaptive traffic events, average delay of vehicles

CLC Number:

U491

JIANG Xiancai, WU Zhanling, SAN Jingqi. Signal Control Method of Adaptive Traffic Events in Connected Traffic Environment[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(12): 46-60.

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参数	取值
道路限速/（m·s^-1）	16
交叉口限速/（m·s^-1）	11
最低速度/（m·s^-1）	4
最大加速度/（m·s^-2）	4
期望加速度/（m·s^-2）	2.5
连续车流之间的安全距离/m	3.6
直行及右转车流最短绿灯时间/s	20
直行及右转车流最长绿灯时间/s	60
左转车流最短绿灯时间/s	12
左转车流最长绿灯时间/s	36
最小周期长度/m	45
车长/m	4
CAV反应时间/s	0.1
CHV反应时间/s	1.0
排队时相邻车辆安全距离/m	0.5
CAV-CAV和CHV-CAV饱和车头时距/s	1
CHV-CHV和CAV-CHV饱和车头时距/s	2
直行及右转车道饱和流率/（pcu·h^-1）	1 805
左转车道饱和流率/（pcu·h^-1）	1 200
CHV平均速度/（m·s^-1）	9
仿真时长/s	3 600
车道宽度/m	3.5

优化方法	信号优化	轨迹优化	信号与轨迹优化协同
方案1	√	√	×
方案2（JOTS-CVT）	√	√	√
方案3（SCM-ATE）	√	√	√

交叉口流率比	车均延误/s			分流车的平均行程时间/s
交叉口流率比	方案1	方案2	方案3	方案1	方案2	方案3
0.60	25.224	25.430	22.056	174.556	166.350	163.852
0.75	33.985	30.216	27.073	196.397	186.218	171.680
0.85	42.667	40.925	40.412	248.140	241.794	228.567

方案	均值/s	标准差/s	极差/s
SCM-ATE	39.5	9.25	55
JOTS-CVT	42.7	15.08	76

方案	均值/s	标准差/s	变异系数	t检验
SCM-ATE	27.96	7.848	0.281	2.16
JOTS-CVT	30.26	12.852	0.425	2.16