- Title
- Exploring human mobility for multi-pattern passenger prediction : a graph learning framework
- Creator
- Kong, Xiangjiea; Wang, Kailai; Hou, Mingliang; Xia, Feng; Karmakar, Gour; Li, Jianxin
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190060
- Identifier
- vital:17530
- Identifier
-
https://doi.org/10.1109/TITS.2022.3148116
- Identifier
- ISSN:1524-9050 (ISSN)
- Abstract
- Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. © 2000-2011 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 9 (2022), p. 16148-16160
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright 2022 IEEE
- Rights
- Open Access
- Subject
- 3509 Transportation, logistics and supply chains; 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; Graph convolutional networks; Human mobility pattern; Passenger flow prediction; Smart city; Spatio-temporal data mining
- Full Text
- Reviewed
- Funder
- This work was supported in part by the National Natural Science Foundation of China under Grant 62072409, in part by the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003, and in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2020001.
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