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

CHEN Xin; LUO Xia; ZHU Ying; MAO Yuansi

doi:10.3969/j.issn.0258-2724.20200270

Volume 57 Issue 2

Jul. 2022

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Journal of Southwest Jiaotong University > 2022 > 57(2): 418-424.

NONG Xingzhong, SHI Haiou, YUAN Quan, ZENG Wenqu, ZHENG Qing, DING Guofu. Review on BIM Technology Used in Urban Rail Transit Projects[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 451-460. doi: 10.3969/j.issn.0258-2724.20200018

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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

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

doi: 10.3969/j.issn.0258-2724.20200270

CHEN Xin^{1, 2
,},
LUO Xia^{1, 2
,
,},
ZHU Ying³,
MAO Yuansi^{1, 2}

1.
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
2.
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China
3.
China Railway Group Limited, Beijing 100039, China

Received Date: 07 May 2020
Rev Recd Date: 14 Dec 2020
Publish Date: 25 Dec 2020

Abstract

Abstract

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.
- urban rail transit,
- delayed boarding,
- maximum likelihood estimate,
- automatic fare collection data

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References(17)

References

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