The fixed green wave speed and staged statistical flow used in arterial signal coordination are not adaptable to the fluctuations in vehicle travel speed and traffic flow on roads, resulting in a mismatch between the signal scheme and the optimal green wave speed and traffic flow demand. This discrepancy negatively impacts the efficiency of intersection traffic. In traditional signal control systems, the cycle and green light timing are typically set independently. However, such a setting method poses problems in practical operation. In this paper, we combine vehicle arrival and vehicle location information, and consider the interaction of speed guidance and dynamic signal optimization to construct a model. This study is developed along the following steps: in the vehicle-road coordination environment, based on the MAXBAND model, a global coordination scheme is obtained, incorporating the speed guidance method; then, based on the vehicle saturation of the inlet lane of the arterial intersection, a multi-objective optimization model for arterial signal coordination under vehicle speed guidance is established based on global coordination with the maximum green wave bandwidth and the minimum delay of arterial vehicles, the minimum number of arterial stops and the minimum delay in the minor direction road as the optimization objectives. Based on global coordination, adopting an integrated control mechanism of cycle and green light timing allows for dynamic adjustments according to real-time traffic conditions. The improved multi-objective particle swarm algorithm is chosen to solve the model, and the simulation environment is built based on the COM interface of VISSIM software and C# platform. Three adjacent intersections of Ganjiang Middle Road in Nanchang are selected as case studies, and the methods in this paper are compared with the current timing scheme, the MAXBAND method and the optimization scheme under speed guidance only, respectively. The results show that the model proposed in this paper achieves significant optimization effects on the indicators of arterial delay, arterial stopping times and the delay of minor roads.

中文翻译：

---

基于感应控制和车速引导的多目标干线协调控制方法

干线信号协调采用的固定绿波速度和阶段性统计流量不能适应道路上车辆行驶速度和车流的波动，导致信号方案与最佳绿波速度和交通流量需求不匹配。这种差异会对十字路口交通的效率产生负面影响。在传统的信号控制系统中，周期和绿灯定时通常是独立设置的。然而，这样的设置方法在实际操作中存在问题。本文结合车辆到达和车辆位置信息，考虑速度引导和动态信号优化的相互作用来构建模型。本研究按照以下步骤展开：在车路协调环境下，基于MAXBAND模型，结合速度引导方法，得到全局协调方案；然后，根据干线交叉口入口车道车辆饱和情况，以干线车辆绿波带宽最大、延误最小的全局协调为基础，建立车速引导下干线信号协调多目标优化模型，以主干道停靠次数最少、次向道路延误最少为优化目标。基于全局协调，采用周期与绿灯配时一体化控制机制，可根据实时交通状况进行动态调整。选择改进的多目标粒子群算法对模型进行求解，并基于VISSIM软件的COM接口和C#平台搭建仿真环境。选取南昌市赣江中路三个相邻交叉口作为案例研究，分别将本文方法与现行配时方案、MAXBAND方法以及仅速度引导下的优化方案进行比较。结果表明，本文提出的模型在干线延误、干线停车次数和支路延误等指标上取得了显着的优化效果。