Mobility Irregularity Detection with Smart Transit Card Data
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
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.
Details
Originalsprache | Englisch |
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Titel | Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings |
Redakteure/-innen | Hady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan |
Herausgeber (Verlag) | Springer, Berlin [u. a.] |
Seiten | 541-552 |
Seitenumfang | 12 |
ISBN (Print) | 9783030474256 |
Publikationsstatus | Veröffentlicht - 2020 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Lecture Notes in Computer Science, Volume 12084 |
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ISSN | 0302-9743 |
Konferenz
Titel | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 |
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Dauer | 11 - 14 Mai 2020 |
Stadt | Singapore |
Land | Singapur |
Externe IDs
ORCID | /0000-0002-2939-2090/work/141543751 |
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Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Irregular pattern detection, Similarity learning, Spatial-temporal profiling