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Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

Authors :
Chao Yu
Haiying Li
Xinyue Xu
Jun Liu
Jianrui Miao
Yitang Wang
Qi Sun
Source :
Journal of Advanced Transportation, Vol 2021 (2021)
Publication Year :
2021
Publisher :
Hindawi-Wiley, 2021.

Abstract

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.

Details

Language :
English
ISSN :
01976729 and 20423195
Volume :
2021
Database :
Directory of Open Access Journals
Journal :
Journal of Advanced Transportation
Publication Type :
Academic Journal
Accession number :
edsdoj.bf8eaad5913a46aea7bc7ba10bb4fbbc
Document Type :
article
Full Text :
https://doi.org/10.1155/2021/5574093