基于动态交通信息的公交到站预测服务

Abstract

公交到站时间(BAT)服务是提高公众交通吸引力的关键服务之一。服务给使用者提供实时车辆到站信息能够使用户更好的安排自己的公共交通行程。因此，实时公交到站时间预测技术在智能交通领域已经成为研究的热点。在本文中采用了新的公交到站时间预测模型。该模型提出了一套完整的算法来解决BAT预测中大规模的实时交通信息的计算，采用了高效的算法来实时纠正车辆的行驶方向。动态交通信息作为数据基础支撑BAT的计算。同时，采用虚拟预测来处理GPS数据丢失的情况保证了服务提供的稳定性。实验结果表明模型拥有较高的准确率(超过85.1%)和计算速度(最高处理5000条每秒)。 The bus arrival time (BAT) service is a key service to improve public transport attractiveness by providing users with real-time bus arrival information which can help them to arrange their bus travel schedule intelligently. Thus the technique of real-time bus arrival prediction has become a research hotspot in the community of Intelligent Transport Systems (ITS) nowadays. In this paper, a novel model on bus arrival time prediction is proposed. The model proposes a complete set of programs to solve BAT prediction for large-scale real-time traffic information calculating. It adopts an effective algorithm calculating vehicle’s driving direction real-timely. BAT is calculated based on dynamic traffic information and visual prediction is a way to complement when GPS information is not arrived as expected. Experimental results indicate that the model has considerable efficiency in accuracy (over 85.1%) and computational speed (max 5000 GPS records per second).