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Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework

journal contribution
posted on 2022-02-09, 00:00 authored by X Kong, K Wang, M Hou, F Xia, G Karmakar, Jianxin LiJianxin Li
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.

History

Journal

IEEE Transactions on Intelligent Transportation Systems

Volume

23

Pagination

16148-16160

Location

Piscataway, NJ.

ISSN

1524-9050

eISSN

1558-0016

Language

eng

Notes

In press

Publication classification

C1 Refereed article in a scholarly journal

Issue

9

Publisher

Institute of Electrical and Electronics Engineers (IEEE)