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Moving Array Traffic Probes

Author

Listed:
  • Blake Davis
  • Ang Ji
  • Bichen Liu
  • David Levinson

    (TransportLab, School of Civil Engineering, University of Sydney)

Abstract

This paper explores the potential of moving array ‘probes’ to collect traffic data. This application simulates the prospect of mining environmental data on traffic conditions to present a cheap and potentially widespread source of traffic conditions. Based on three different simulations, we measure the magnitude and trends of probe error (comparing the probe’s ‘subjective’ or time-weighted perception with an ‘objective’ observer) in density, speed, and flow in order to validate the proposed model and compare the results with loop detectors. From these simulations, several conclusions were reached. A single probe’s error follows a double hump trend due to an interplay between the factors of traffic heterogeneity and shockwaves. Reduced visibility of the single probe does not proportionately increase the error. Multiple probes do not tend to increase accuracy significantly, which suggests that the data will be still useful even if probes are sparsely distributed. Finally, probes can measure the conditions of oncoming traffic more accurately than concurrent traffic. Further research is expected to consider more complex road networks and develop methods to improve the accuracy of moving array samples.

Suggested Citation

  • Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:movingarrays
    DOI: 10.3389/ffutr.2020.602356
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    File URL: https://hdl.handle.net/2123/21344
    File Function: First version, 2020
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    File URL: https://libkey.io/10.3389/ffutr.2020.602356?utm_source=ideas
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    References listed on IDEAS

    as
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    8. repec:ipt:iptwpa:jrc47967 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Autonomous vehicles; probes; traffic state estimation; floating car data;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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