Mobility Irregularity Detection with Smart Transit Card Data

Xuesong Wang, Lina Yao, Wei Liu, Can Li, Lei Bai, S. Travis Waller

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Identifying patterns and detecting irregularities regarding individual mobility in public transport system is crucial for transport planning and law enforcement applications (e.g., fraudulent behavior). In this context, most of recent approaches exploit similarity learning through comparing spatial-temporal patterns between normal and irregular records. However, they are limited in utilizing passenger-level information. First, all passenger transits are fused in a certain region at a timestamp whereas each passenger has own repetitive stops and time slots. Second, these differences in passenger profile result in high intra-class variance of normal records and blur the decision boundaries. To tackle these problems, we propose a modelling framework to extract passenger-level spatial-temporal profile and present a personalised similarity learning for irregular behavior detection. Specifically, a route-to-stop embedding is proposed to extract spatial correlations between transit stops and routes. Then attentive fusion is adopted to uncover spatial repetitive and time invariant patterns. Finally, a personalised similarity function is learned to evaluate the historical and recent mobility patterns. Experimental results on a large-scale dataset demonstrate that our model outperforms the state-of-the-art methods on recall, F1 score and accuracy. Raw features and the extracted patterns are visualized and illustrate the learned deviation between the normal and the irregular records.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020
EditorsHady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
PublisherSpringer
Pages541-552
Number of pages12
ISBN (Print)9783030474256
DOIs
Publication statusPublished - May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12084 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Irregular pattern detection
  • Similarity learning
  • Spatial-temporal profiling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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