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

Seth Miller, Zachary Vander Laan and Nikola Marković

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 143, issue C

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.

Keywords: Trajectory scaling; Convex programming; Least absolute deviations; Data visualization; Origin-destination patterns; Traffic volumes (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.tre.2020.102105

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