A Short-Term Public Transit Volume Forecasting Model Based
on IC Card and RBF Neural Network

doi:10.3969/j.issn.1674-0696.2015.06.20

Journal of Chongqing Jiaotong University(Natural Science) ›› 2015, Vol. 34 ›› Issue (6): 106-110.DOI: 10.3969/j.issn.1674-0696.2015.06.20

• Transportation Engineering • Previous Articles Next Articles

A Short-Term Public Transit Volume Forecasting Model Based on IC Card and RBF Neural Network

Lu Baichuan^1,2, Deng Jie^1,3, Ma Qinglu¹, Liu Quanfu¹, Zhang Kai¹

1. School of Traffic & Transportation， Chongqing Jiaotong University， Chongqing 400074， China; 2. Key Lab of Traffic System & Safety in Mountain Cities， Chongqing Jiaotong University， Chongqing 400074， China; 3. Department of Information Engineering， Guizhou Polytechnic College of Communications， Guiyang 550008， Guizhou， China

Received:2014-04-15 Revised:2014-10-15 Online:2015-12-30 Published:2015-12-29

基于IC卡和RBF神经网络的短时公交客流量预测

陆百川^1,2，邓捷^1,3，马庆禄¹，刘权富¹，张凯¹

1.重庆交通大学交通运输学院，重庆 400074；2. 重庆交通大学重庆山地城市交通系统与安全实验室，重庆 400074； 3.贵州交通职业技术学院信息工程系，贵州贵阳 550008

作者简介:陆百川（1961—），男，江苏南通人，教授，博士，博士生导师，主要从事交通信息工程及控制方面的研究。E-mail: dengjie2079@126.com。
基金资助:
国家外国专家局2011教科文卫引智项目计划(What011201)

Abstract

Abstract: On the base of the analysis on characteristics of bus passenger volume, the real-time data of public transit volume was obtained by IC card. Combining with GPS data, the real-time distribution of passenger volume was analyzed by OD back-stepping method. And then the forecasting model of short-term public transit volume based on IC card and RBF neural network was established, meanwhile, the specific forecasting process was also introduced. No. 841 bus route in Chongqing was taken as an example to verify the proposed forecasting model. It is found that the average absolute relative error of the real value and the predicted value of the passenger flow is less than 1.5%. The results of case study show that the proposed model can obtain real-time traffic data and achieve high prediction accuracy, which has certain practical application value.

Key words: traffic and transportation engineering, IC card information, GPS data, RBF neural network, short-term public transit volume, passenger volume forecasting

摘要： 在公交客流量特性分析基础上，通过IC卡获取了实时公交客流量数据；结合GPS数据，利用OD反推法分析了实时客流分布；进而建立了基于IC卡和RBF神经网络的短时公交客流量预测模型并介绍了具体预测过程。对重庆市841公交线路进行了实例分析，得到上下车客流真实值与预测值的平均绝对相对误差均小于1.5%，实例计算结果表明该模型能获取实时客流数据，预测精度高，具有一定的实际应用价值。

关键词: 交通运输工程, IC卡信息, GPS数据, RBF神经网络, 短时公交客流, 客流预测

CLC Number:

U491.1

Lu Baichuan, Deng Jie, Ma Qinglu, Liu Quanfu, Zhang Kai. A Short-Term Public Transit Volume Forecasting Model Based on IC Card and RBF Neural Network[J]. Journal of Chongqing Jiaotong University(Natural Science), 2015, 34(6): 106-110.

陆百川，邓捷，马庆禄，刘权富，张凯. 基于IC卡和RBF神经网络的短时公交客流量预测[J]. 重庆交通大学学报（自然科学版）, 2015, 34(6): 106-110.

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A Short-Term Public Transit Volume Forecasting Model Based on IC Card and RBF Neural Network

基于IC卡和RBF神经网络的短时公交客流量预测

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