交通运输系统工程与信息

交通运输系统工程与信息 ›› 2016, Vol. 16 ›› Issue (3): 60-66.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于粒子群小波神经网络的公交到站时间预测

季彦婕*，陆佳炜，陈晓实，胡波   

  1. 东南大学交通学院，南京210096
  • 收稿日期:2015-10-19 修回日期:2015-12-23 出版日期:2016-06-25 发布日期:2016-06-27
  • 作者简介:季彦婕（1980－），女，江苏靖江人，副教授，博士.
  • 基金资助:

    国家自然科学基金资助项目/ National Natural Science Foundation of China（51338003）；国家自然科学基金国际合 作与交流项目/ Projects of International Cooperation and Exchange of the National Natural Science Foundation of China (5151101143).

Prediction Model of Bus Arrival Time Based on Particle Swarm Optimization and Wavelet Neural Network

JI Yan-jie, LU Jia-wei, CHEN Xiao-shi, HU Bo   

  1. School of Transportation, Southeast University, Nanjing 210096, China
  • Received:2015-10-19 Revised:2015-12-23 Online:2016-06-25 Published:2016-06-27

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被引次数

2

摘要：

公交到站时间的实时预测是公交出行信息发布、公交出行诱导、公交动态调度 的关键技术.基于公交车辆运行特性分析，将公交到站时间分为路段运行时间和站点停靠 时间两部分，并考虑工作日与周末的运行特性差异，最后结合迭代思想提出利用粒子群 小波神经网络模型预测公交到站时间.实例分析表明：粒子群算法能有效降低小波神经网 络模型的训练误差；结合迭代法使用公交车上一站运行时间作为预测输入能够有效提高 预测精度；该预测模型对于公交车在工作日和周末到站时间的预测均能达到较高的精 度，平均绝对百分比误差分别为10.82%和9.85%.

关键词: 智能交通, 公交到站时间预测, 小波神经网络, 公交, 粒子群算法, 迭代法

Abstract:

Real-time bus arrival time prediction is the key technology of bus travel information release, bus trip guidance, and bus dynamic scheduling. Based on the characteristic analysis of bus operation, the bus arrival time is divided into section running time and platform docking time. With the consideration of differences between running characteristics of the weekday and weekend, a forecasting model is proposed based on iterative thinking, particle swarm optimization and wavelet neural network to forecast bus arrival time. Example analysis shows that the particle swarm optimization can effectively reduce the training error of wavelet neural network model. Combined with the iterative method, the use of bus running time as forecast input can effectively improve the prediction accuracy. The bus arrival time prediction model is built in this paper can reach high precision on weekday and weekend, and mean absolute error is 10.82% and 9.85%.

Key words: intelligent transportation, bus arrival time prediction, wavelet neural network, bus, particle swarm optimization, iteration

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