考虑速度感知的最大压力交叉口信号动态控制方法

Speed-Aware Max-Pressure Dynamic Signal Control Method for Urban Intersections

摘要: 随着城市交通需求持续增长，信号交叉口成为制约通行效率的重要因素。传统最大压力控制主要依赖流量或排队信息，对车辆速度等动态状态信息利用不足，在动态识别拥堵和信号切换方面存在一定局限，难以满足现代交通系统日益复杂和多变的需求。引入速度信息刻画拥堵强度，旨在增强动态信号控制对排队和速度变化的敏感性，及时优化信号配时以适应快速变化的交通需求和提升交叉口通行效率。本文提出考虑速度感知的最大压力交叉口信号动态控制算法，基于SUMO (Simulation of Urban Mobility)软件进行仿真，并与固定配时控制和传统最大压力控制算法进行对比。以上海市吴中路与虹许路交叉口为研究对象进行仿真分析，结果表明，所提出的速度感知最大压力算法的平均旅行时间比固定配时缩短52.0%、比传统最大压力缩短9.4%，平均延误对比固定配时与传统最大压力分别减少83.5%与32.5%。本研究可为城市交通信号优化提供参考，进一步扩展至多路口联动及智能网联环境下的自适应信号调度。

Abstract: With the continuous growth of urban traffic demand, signalized intersections have become a critical bottleneck constraining traffic efficiency. Traditional maximum pressure control strategies primarily rely on traffic flow or queue length information, while insufficiently exploiting dynamic vehicle state information such as speed. As a result, they exhibit limitations in dynamically identifying congestion and adjusting signal phases, making it difficult to meet the increasingly complex and variable demands of modern traffic systems. By incorporating speed information to characterize congestion intensity, this study aims to enhance the sensitivity of dynamic signal control to both queue formation and speed variations, thereby enabling timely optimization of signal timing to adapt to rapidly changing traffic demand and improve intersection operational efficiency. A speed-aware maximum pressure-based dynamic signal control algorithm for signalized intersections is proposed. Simulation experiments are conducted using the Simulation of Urban Mobility (SUMO) platform, and the performance of the proposed method is compared with that of conventional fixed-time control and traditional maximum pressure control algorithms. Based on simulation experiments conducted at the Wuzhong Road and Hongxu Road intersection in Shanghai, the results demonstrate that the speed-aware max-pressure algorithm reduces the average travel time by 52.0% and 9.4% compared with fixed-time control and the conventional max-pressure approach, respectively. Furthermore, the average delay is decreased by 83.5% and 32.5% under the proposed method. This work can serve as a reference for urban traffic signal optimization and can be further extended to multi-intersection coordinated control and adaptive signal scheduling in intelligent traffic environments.