Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior-Reference-Cited by-同舟云学术

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Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior

Published:2021-06-12 Issue: Volume: Page:
ISSN:1566-113X
Container-title:Networks and Spatial Economics
language:en
Short-container-title:Netw Spat Econ

Author:

Sun Lijun^ORCID,Chen Xinyu,He Zhaocheng,Miranda-Moreno Luis F.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s11067-021-09542-9.pdf

Reference52 articles.

1. Ahas R, Aasa A, Silm S, Tiru M (2010) Daily rhythms of suburban commuters’ movements in the tallinn metropolitan area: Case study with mobile positioning data. Transp Res Part C Emerg Technol 18(1):45–54

2. Axhausen KW, Zimmermann A, Schönfelder S, Rindsfüser G, Haupt T (2002) Observing the rhythms of daily life: a six-week travel diary. Transportation 29(2):95–124

3. Baratchi M, Meratnia N, Havinga PJ, Skidmore AK, Toxopeus BA (2014) A hierarchical hidden semi-Markov model for modeling mobility data. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, pp 401–412

4. Barbosa H, Barthelemy M, Ghoshal G, James CR, Lenormand M, Louail T, Menezes R, Ramasco JJ, Simini F, Tomasini M (2018) Human mobility: Models and applications. Phys Rep 734:1–74

5. Bertini RL, Lasky M, Monsere CM (2005) Validating predicted rural corridor travel times from an automated license plate recognition system: Oregon’s frontier project. In: Proceedings of the 2005 IEEE intelligent transportation systems conference, pp 296–301

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