基于多元数据的校园周边交叉口Synchro信号优化研究

Synchro Signal Optimization Study for Campus Surrounding Intersections Based on Multi-Source Data

摘要: 随着校园周边交通量的增加，交叉口的交通拥堵问题愈发严重，对行人和车辆的通行造成了较大困扰。为有效提高交叉口的通行效率，减少交通延误与排队长度，本文基于多元数据，利用Synchro交通仿真系统对校园周边的海棠路与丁香路交叉口进行了信号优化研究。通过对现状交通流量数据的收集与分析，建立交叉口的仿真模型，评估现有信号配时方案的不足之处。基于流量/容量比率(V/C ratio)、控制延误(Control Delay)和服务水平(LOS)等关键指标，对信号配时方案进行了调整与优化。优化后的信号配时方案显著降低了高峰期的排队长度，改善了各交通流向的通行效率，并提高了整体的服务水平。结果表明：燃料消耗由155升降低至105升，减少了约32%，优化效果明显；停车次数从1359次/小时减少至1121次/小时，降低了约17.5%；总延误时间从549.2秒缩短至520.1秒，减少了约5.3%；交叉口的总体服务水平由E提升为D。研究表明，基于多元数据的Synchro信号优化对缓解校园周边交叉口的交通拥堵、提高通行效率和道路安全具有重要作用。

Abstract: With the increasing traffic volume around campuses, congestion at intersections has become more severe, causing significant challenges for pedestrians and vehicles. To effectively improve the efficiency of intersection traffic flow and reduce delays and queue lengths, this study conducts a signal optimization analysis for the intersection of Haitang Road and Dingxiang Road near a campus, using the Synchro traffic simulation system and multi-source data. By collecting and analyzing the existing traffic flow data, a simulation model of the intersection was established to evaluate the shortcomings of the current signal timing plan. Based on key indicators such as volume-to-capacity ratio (V/C ratio), control delay, and level of service (LOS), adjustments and optimizations were made to the signal timing. The optimized signal timing plan significantly reduced queue lengths during peak hours, improved traffic flow efficiency across all directions, and enhanced the overall level of service. Results showed that fuel consumption was reduced from 155 liters to 105 liters, a decrease of approximately 32%; the number of stops decreased from 1359 stops/hour to 1121 stops/hour, reducing by about 17.5%; and total delay time was reduced from 549.2 seconds to 520.1 seconds, a decrease of approximately 5.3%. The overall level of service of the intersection was improved from E to D. The study indicates that Synchro signal optimization based on multi-source data plays a crucial role in alleviating congestion, enhancing traffic efficiency, and improving road safety at campus-area intersections.