Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network
Joint Authors
Guo, Bin
Yue, Yixiang
Chen, Xing
Bai, Zixi
Zhou, Hanxiao
Zhou, Leishan
Source
Journal of Advanced Transportation
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-04-01
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
The “left-behind” phenomenon occurs frequently in Urban Rail Transit (URT) networks with booming travel demand, especially during peak hours in a complex URT network, which makes passenger travel patterns more complicated.
This paper proposes a methodology to mine passenger travel patterns based on fare transaction records from automatic fare collection (AFC) systems and Automatic Vehicle Location (AVL) data from Communication Based Train Control (CBTC) Systems or tracking systems.
By introducing the concept of a sequence, a space-time-sequence trajectory model is proposed to simulate a passenger’s travel activities, including when they are left-behind.
The paper analyzes passenger travel trajectory links and estimates the weight of each feasible trajectory under tap-in/tap-out constraints.
The station time parameters, including access/egress and transfer walking-time parameters, are important inputs for the model.
The paper also presents a maximum-likelihood approach to estimate these parameters from AFC transaction data and AVL data.
The methodology is applied to a case study using AFC and AVL data from the Beijing URT network during peak hours to test the proposed model and algorithm.
The estimation results are consistent with the results obtained from the authorities, and this finding verifies the feasibility of our approach.
American Psychological Association (APA)
Chen, Xing& Zhou, Leishan& Bai, Zixi& Yue, Yixiang& Guo, Bin& Zhou, Hanxiao. 2019. Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1170048
Modern Language Association (MLA)
Chen, Xing…[et al.]. Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network. Journal of Advanced Transportation No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1170048
American Medical Association (AMA)
Chen, Xing& Zhou, Leishan& Bai, Zixi& Yue, Yixiang& Guo, Bin& Zhou, Hanxiao. Data-Driven Approaches to Mining Passenger Travel Patterns: “Left-Behinds” in a Congested Urban Rail Transit Network. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1170048
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170048