Data-Driven Approaches to Mining Passenger Travel Patterns: 'Left-Behinds' in a Congested Urban Rail Transit Network

Autor: Xing Chen, Leishan Zhou, Zixi Bai, Yixiang Yue, Bin Guo, Hanxiao Zhou
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Journal of Advanced Transportation, Vol 2019 (2019)
Druh dokumentu: article
ISSN: 0197-6729
2042-3195
DOI: 10.1155/2019/6830450
Popis: 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.
Databáze: Directory of Open Access Journals
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