面向公交智能化调度的站点运行时间预测模型

A Prediction Model of Station Operation Time for Intelligent Bus Scheduling

摘要: 公交车准点到站预测技术是智能公交系统中的一项关键技术，结合大数据、人工智能等技术手段，能够实现对公交车的精准预测，为乘客提供诸如公交车到站时间等实时信息，对于提升公交车服务质量而言意义重大。对公交站点实施科学有效的调度优化策略，是降低乘客候车时长、减少排队规模，进而提高公交站点整体运营效能的核心举措。本研究运用系统工程的原理与方法，融合GPS技术、数据预处理技术、数据库管理技术以及GIS技术，构建了随机排队优化模型，实现对实时车辆到达动态预测。通过上海市奉浦快线智能公交示范线路的数据进行实证研究，实验结果显示准确率提高了12%。本研究成果为实际应用奠定了坚实基础。

Abstract: Accurate bus arrival time prediction is a key component of intelligent public transportation systems. By integrating technologies such as big data and artificial intelligence, it enables precise forecasting of bus arrivals and provides passengers with real-time information on expected arrival times. Optimizing bus stop scheduling is crucial for enhancing service quality; reasonable scheduling can effectively reduce passenger waiting times and queue lengths, thereby improving the operational efficiency of bus stops. This study adopts a systems engineering approach, integrating GPS, data preprocessing, database management, and GIS technologies to acquire relevant data. Based on this foundation, a stochastic queuing optimization model is constructed to enable real-time dynamic prediction of bus arrival times. An empirical study using data from the Fengpu Express Line, an intelligent bus demonstration route in Shanghai, shows a 12% improvement in forecast accuracy. The results of this study lay a solid foundation for practical applications.