• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 57 Issue 2
Jul.  2022
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Article Contents
CHEN Xin, LUO Xia, ZHU Ying, MAO Yuansi. Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270
Citation: CHEN Xin, LUO Xia, ZHU Ying, MAO Yuansi. Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 418-424. doi: 10.3969/j.issn.0258-2724.20200270

Delayed-Boarding Probability Distribution for Metro Stations Using Auto Fare Collection Data

doi: 10.3969/j.issn.0258-2724.20200270
  • Received Date: 07 May 2020
  • Rev Recd Date: 14 Dec 2020
  • Publish Date: 25 Dec 2020
  • To explore the characteristics of delayed boarding in metro stations, a probability distribution estimation method based on auto fare collection (AFC) data and operation timetable data is developed. Firstly, according to the relationship between passenger tap-in and tap-out time and train arrival and departure time, a probability distribution function of maximum access time and egress time is constructed, and an estimation method using the truncated sample is developed to estimate the access and egress time distribution. Secondly, an estimation method of the delayed-boarding probability distribution is constructed by analyzing the relationship among passenger tap-in and tap-out time, access and egress time and the number of delayed-boarding times. Finally, with some metro sections as a real case study, for different levels and types of stations, the access and egress time distribution and the delayed-boarding probability are estimated. The results of the case study show that the access and egress time distributions follow the estimated distributions at a 5% significant level, and the estimated delayed-boarding probability distribution is consistent with the practical results.

     

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