Data-driven model for passenger route choice in urban metro network

Jianjun Wu, Yunchao Qu, Huijun Sun, Haodong Yin, Xiaoyong Yan and Jiandong Zhao

Physica A: Statistical Mechanics and its Applications, 2019, vol. 524, issue C, 787-798

Abstract: Passenger flow distribution in the metro system is fundamental for many applications such as network planning and design, passenger flow forecasting, individual travel activity modeling and emergency response management. However, in most metro systems the smart card automated fare collection (AFC) equipment in Beijing only record when and where a passenger enters and leaves the metro network. Therefore, how to accurately determine passenger flow distribution in unknown travel routes remains a challenging task for the managers. This paper presents a methodology for reconstructing metro passenger flow distribution from large-scale smart card data. A clustering method was first applied to group the travel time of passengers between origin–destination (OD) station pairs into different clusters. Then an approach was proposed that considered both uncertain walking time and transfer time, to estimate the theoretical travel time of all possible routes between the OD pair. An approach to measure the similarity was further employed to match each travel time cluster to a most-likely travel route, and finally obtained the passengers’ flow of every route. Compared with two classical methods, the proposed approach was more accurate and efficient.

Keywords: Smart card; Travel behavior; Metro network; Passenger flow (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:524:y:2019:i:c:p:787-798

DOI: 10.1016/j.physa.2019.04.231

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