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Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study

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  • Miller, Seth
  • Laan, Zachary Vander
  • Marković, Nikola

Abstract

This paper considers the problem of inferring statewide traffic patterns by scaling massive GPS trajectory data, which capture about 3% of the overall traffic in Utah. It proposes a least absolute deviations model with controlled overfitting to scale 2.3 million trajectories such that resulting data best fit vehicle counts measured by 296 traffic sensors across the state. The proposed model improves on an often-cited approach from the literature and achieves 45% lower error for locations not seen in model training, obtaining 18% median hourly error across all test locations.

Suggested Citation

  • Miller, Seth & Laan, Zachary Vander & Marković, Nikola, 2020. "Scaling GPS trajectories to match point traffic counts: A convex programming approach and Utah case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:transe:v:143:y:2020:i:c:s1366554520307535
    DOI: 10.1016/j.tre.2020.102105
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    References listed on IDEAS

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    1. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    2. Hazelton, Martin L., 2000. "Estimation of origin-destination matrices from link flows on uncongested networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 549-566, September.
    3. Hadavi, Majid & Shafahi, Yousef, 2016. "Vehicle identification sensor models for origin–destination estimation," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 82-106.
    4. Xie, Chi & Kockelman, Kara M. & Waller, S. Travis, 2011. "A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1465-1482.
    5. Fan Yang & Zhenxing Yao & Fan Ding & Huachun Tan & Bin Ran, 2019. "Understanding Urban Mobility Pattern with Cellular Phone Data: A Case Study of Residents and Travelers in Nanjing," Sustainability, MDPI, vol. 11(19), pages 1-17, October.
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