Moving Array Traffic Probes

Blake Davis, Ang Ji, Bichen Liu and David Levinson

No 2022-01, Working Papers from University of Minnesota: Nexus Research Group

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.

Keywords: Autonomous vehicles; probes; traffic state estimation; floating car data (search for similar items in EconPapers)
JEL-codes: R41 (search for similar items in EconPapers)
Date: 2020
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Published in Frontiers in Future Transportation. doi: 10.3389/ffutr.2020.602356.

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https://hdl.handle.net/2123/21344 First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:movingarrays

DOI: 10.3389/ffutr.2020.602356

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