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Real time traffic states estimation on arterials based on trajectory data

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  • Hiribarren, Gabriel
  • Herrera, Juan Carlos

Abstract

New technologies able to register vehicle trajectories, such as GPS (Global Position Systems)-enabled cell phones, have opened a new way of collecting traffic data. However, good methods that convert these data into useful information are needed to leverage these data. In this study a new method to estimate traffic states on arterials based on trajectory data is presented and assessed. The method is based on the Lighthill–Whitham–Richards (LWR) theory. By using this theory, traffic dynamics on arterials can be better captured by extracting more information from the same piece of data. Trajectory data used consist of the trajectory of the latest equipped vehicle that crossed the segment under study. Preliminary analysis based on micro-simulation suggests that this method yields good traffic state estimates both at congested and uncongested situations, even for very low penetration rates (1%). The method is also able to forecast queue length at intersections and travel times along a road section.

Suggested Citation

  • Hiribarren, Gabriel & Herrera, Juan Carlos, 2014. "Real time traffic states estimation on arterials based on trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 69(C), pages 19-30.
    • Handle: RePEc:eee:transb:v:69:y:2014:i:c:p:19-30
    DOI: 10.1016/j.trb.2014.07.003
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    1. Ygnace, Jean-Luc & Drane, Chris & Yim, Y. B. & de Lacvivier, Renaud, 2000. "Travel Time Estimation on the San Francisco Bay Area Network Using Cellular Phones as Probes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8xn8m01v, Institute of Transportation Studies, UC Berkeley.
    2. Hofleitner, Aude & Herring, Ryan & Bayen, Alexandre, 2012. "Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning," Transportation Research Part B: Methodological, Elsevier, vol. 46(9), pages 1097-1122.
    3. Ramezani, Mohsen & Geroliminis, Nikolas, 2012. "On the estimation of arterial route travel time distribution with Markov chains," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1576-1590.
    4. Westerman, Marcel & Litjens, Remco & Linnartz, Jean-paul, 1996. "Integration Of Probe Vehicle And Induction Loop Data: Estimation Of Travel Times And Automatic Incident Detection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8mh629c3, Institute of Transportation Studies, UC Berkeley.
    5. Smulders, Stef, 1990. "Control of freeway traffic flow by variable speed signs," Transportation Research Part B: Methodological, Elsevier, vol. 24(2), pages 111-132, April.
    6. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    7. Sun, Zhanbo & Zan, Bin & Ban, Xuegang (Jeff) & Gruteser, Marco, 2013. "Privacy protection method for fine-grained urban traffic modeling using mobile sensors," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 50-69.
    8. Sanwal, Kumud K. & Walrand, Jean, 1995. "Vehicles As Probes," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3gh0890x, Institute of Transportation Studies, UC Berkeley.
    9. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
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    Cited by:

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    4. Pan, Yingjiu & Chen, Shuyan & Niu, Shifeng & Ma, Yongfeng & Tang, Kun, 2020. "Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity," Journal of Transport Geography, Elsevier, vol. 83(C).

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