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Transit Pattern Detection Using Tensor Factorization

Author

Listed:
  • Bowen Du

    (State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China)

  • Wenjun Zhou

    (Department of Business Analytics and Statistics, University of Tennessee, Knoxville, Tennessee 37996)

  • Chuanren Liu

    (Decision Sciences and MIS Department, Drexel University, Philadelphia, Pennsylvania 19104)

  • Yifeng Cui

    (State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China)

  • Hui Xiong

    (Management Science and Information Systems Department, Rutgers University, Edison)

Abstract

Understanding citywide transit patterns is important for transportation management, including city planning and route optimization. The wide deployment of automated fare collection (AFC) systems in public transit vehicles has enabled us to collect massive amounts of transit records, which capture passengers’ traveling activities. Based on such transit records, origin–destination associations have been studied extensively in the literature. However, the identification of transit patterns that establish the origin–transfer–destination (OTD) associations, in spite of its importance, is underdeveloped. In this paper, we propose a framework based on transit tensor factorization (TTF) to identify citywide travel patterns. In particular, we create a transit tensor, which summarizes the citywide OTD information of all passenger trips captured in the AFC records. The TTF framework imposes spatial regularization in the formulation to group nearby stations into meaningful regions and uses multitask learning to identify traffic flows among these regions at different times of the day and days of the week. Evaluated with large-scale, real-world data, our results show that the proposed TTF framework can effectively identify meaningful citywide transit patterns.

Suggested Citation

  • Bowen Du & Wenjun Zhou & Chuanren Liu & Yifeng Cui & Hui Xiong, 2019. "Transit Pattern Detection Using Tensor Factorization," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 193-206, April.
  • Handle: RePEc:inm:orijoc:v:31:y:2019:i:2:p:193-206
    DOI: 10.1287/ijoc.2018.0824
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    References listed on IDEAS

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    1. Marcelo Prais & Celso C. Ribeiro, 2000. "Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 164-176, August.
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    Cited by:

    1. Shi, Shuyang & Wang, Lin & Wang, Xiaofan, 2022. "Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    3. Junming Liu & Mingfei Teng & Weiwei Chen & Hui Xiong, 2023. "A Cost-Effective Sequential Route Recommender System for Taxi Drivers," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1098-1119, September.
    4. Yicheng Song & Nachiketa Sahoo & Shuba Srinivasan & Chrysanthos Dellarocas, 2022. "Uncovering Characteristic Response Paths of a Population," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1661-1680, May.
    5. Siyuan Liu & Shaojie Tang & Jiangchuan Zheng & Lionel M. Ni, 2022. "Unsupervised Learning for Human Mobility Behaviors," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1565-1586, May.

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