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The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments

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  • Hyun-ho Chang

    (Seoul National University)

  • Seung-hoon Cheon

    (Korea Transport Institute)

Abstract

A promising methodology is proposed to estimate reliable annual average daily traffic (AADT) volumes for no-surveyed road sections using probe volumes collected by a vehicle global positioning system (GPS). This research was inspired by the obvious concept that probe counts are a direct portion of AADT from the viewpoint of vehicle trip behavior. The method converts the probe volume of target road section to AADT using the nonlinear relationship between geographical neighborhoods composed of observed AADT volumes and annual average daily probe volumes. The relationship is determined with a locally weighted power-curve model. A feasibility of the proposed method was demonstrated through a case study using real-world data. Analysis results show that the proposed method is a practical and cost-effective way to estimate reliable AADT for unmeasured road segments. This indicates that there exists a strong relationship between AADT values and vehicle-GPS probe values from the trip characteristics of a road network.

Suggested Citation

  • Hyun-ho Chang & Seung-hoon Cheon, 2019. "The potential use of big vehicle GPS data for estimations of annual average daily traffic for unmeasured road segments," Transportation, Springer, vol. 46(3), pages 1011-1032, June.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:3:d:10.1007_s11116-018-9903-6
    DOI: 10.1007/s11116-018-9903-6
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    References listed on IDEAS

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    1. Selby, Brent & Kockelman, Kara M., 2013. "Spatial prediction of traffic levels in unmeasured locations: applications of universal kriging and geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 29(C), pages 24-32.
    2. Lowry, Michael, 2014. "Spatial interpolation of traffic counts based on origin–destination centrality," Journal of Transport Geography, Elsevier, vol. 36(C), pages 98-105.
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    Cited by:

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    2. Heber Hernández & Elisabete Alberdi & Heriberto Pérez-Acebo & Irantzu Álvarez & María José García & Isabel Eguia & Kevin Fernández, 2021. "Managing Traffic Data through Clustering and Radial Basis Functions," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
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    4. Hyunho Chang & Dongjoo Park, 2020. "Potentialities of Vehicle Trajectory Big Data for Monitoring Potentially Fatigued Drivers and Explaining Vehicle Crashes on Motorway Sections," Sustainability, MDPI, vol. 12(15), pages 1-16, July.

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