Forecasting of passenger flow's distribution among urban rail transit stations based on AFC data

被引：9
作者：
Cai, Changjun [1 ,2 ,3 ]
Yao, Enjian [1 ,2 ]
Zhang, Yongsheng [1 ]
Liu, Shasha [1 ]
机构：
[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing
[2] MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, Beijing
[3] Guangzhou Metro Corporation, Guangzhou, 510310, Guangdong
来源：
Zhongguo Tiedao Kexue/China Railway Science | 2015年 / 36卷 / 01期
关键词：
Aggregate data; Automatic fare collection; Disaggregate choice model; Passenger flow forecasting; Topology of railway network; Urban rail transit;
D O I：
10.3969/j.issn.1001-4632.2015.01.18
中图分类号：
学科分类号：
摘要：
Based on the aggregate data from AFC (Automatic Fare Collection) and behavior analysis theory, a model for forecasting passenger flow's distribution among stations is proposed which is appropriate for a changed network structure in urban rail transit system. Firstly, a destination choice model is established based on random utility maximization theory, and some properties such as the passenger flow volume attracted by destination, in-vehicle travel time, transfer time, number of transfers, whether origin collinear with destination and land use scale around station, are comprehensively considered in utility function to catch the impact of attractiveness of destination, level of service of urban rail transit system, and line position matching relationship between origin and destination on passenger's destination choice behavior. Thereafter, a forecasting model of passenger flow's distribution among urban rail transit stations is proposed. Then, based on the disaggregate data transformed from the AFC data by the representative indi-vidual method, the proposed model are estimated by WESML (Weighted Exogenous Sampling Maximum Likelihood) method. On the condition of Line 6 before and after being put into operation in Guangzhou Metro, based on AFC data, the proposed model is tested, and the results show that the prediction accuracy of proposed method is satisfactory with the mean absolute error of only 36. ©, 2015, Chinese Academy of Railway Sciences. All right reserved.
引用
收藏
页码：126 / 132
页数：6
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