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Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation

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  • Jenelius, Erik
  • Koutsopoulos, Haris N.

Abstract

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple network components (e.g., links) between consecutive position samplings, creating challenges for (i) the allocation of the measured travel time to the traversed components, and (ii) the consistent estimation of component travel time distribution parameters. This paper shows that the solution to these problems depends on whether sampling is based on time (e.g., one report every minute) or space (e.g., one every 500m). For the special case of segments with uniform space-mean speeds, explicit formulae are derived under both sampling principles for the likelihood of the measurements and the allocation of travel time. It is shown that time-based sampling is biased towards measurements where a disproportionally long time is spent on the last segment. Numerical experiments show that an incorrect likelihood formulation can lead to significantly biased parameter estimates depending on the shapes of the travel time distributions. The analysis reveals that the sampling protocol needs to be considered in travel time estimation using probe vehicle data.

Suggested Citation

  • Jenelius, Erik & Koutsopoulos, Haris N., 2015. "Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 120-137.
    • Handle: RePEc:eee:transb:v:71:y:2015:i:c:p:120-137
    DOI: 10.1016/j.trb.2014.10.008
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    References listed on IDEAS

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    1. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    2. repec:ipt:iptwpa:jrc47967 is not listed on IDEAS
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    Cited by:

    1. Martínez-Díaz, Margarita & Pérez, Ignacio, 2015. "A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 19-35.
    2. Seo, Toru & Kawasaki, Yutaka & Kusakabe, Takahiko & Asakura, Yasuo, 2019. "Fundamental diagram estimation by using trajectories of probe vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 40-56.
    3. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    4. Liu, Siyuan & Qu, Qiang, 2016. "Dynamic collective routing using crowdsourcing data," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 450-469.
    5. Blake Davis & Ang Ji & Bichen Liu & David Levinson, 2020. "Moving Array Traffic Probes," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    6. Wong, Wai & Shen, Shengyin & Zhao, Yan & Liu, Henry X., 2019. "On the estimation of connected vehicle penetration rate based on single-source connected vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 169-191.
    7. JIA, Shaocheng & WONG, S.C. & WONG, Wai, 2025. "Adaptive signal control at partially connected intersections: A stochastic optimization model for uncertain vehicle arrival rates," Transportation Research Part B: Methodological, Elsevier, vol. 193(C).

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