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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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    Cited by:

    1. Zhang, Jinyu & Huang, Di & Liu, Zhiyuan & Zheng, Yifei & Han, Yu & Liu, Pan & Huang, Wei, 2024. "A data-driven optimization-based approach for freeway traffic state estimation based on heterogeneous sensor data fusion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).

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