IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v69y2014icp19-30.html
   My bibliography  Save this article

Real time traffic states estimation on arterials based on trajectory data

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S019126151400126X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2014.07.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaomeng Wang & Ling Peng & Tianhe Chi & Mengzhu Li & Xiaojing Yao & Jing Shao, 2015. "A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
    2. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    3. 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.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Herrera, Juan C. & Work, Daniel B. & Herring, Ryan & Ban, Xuegang Jeff & Bayen, Alexandre M, 2009. "Evaluation of Traffic Data Obtained via GPS-Enabled Mobile Phones: the Mobile Century Field Experiment," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0sd42014, Institute of Transportation Studies, UC Berkeley.
    3. Nantes, Alfredo & Ngoduy, Dong & Miska, Marc & Chung, Edward, 2015. "Probabilistic travel time progression and its application to automatic vehicle identification data," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 131-145.
    4. 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.
    5. Herrera, Juan C & Bayen, Alexandre M, 2007. "Traffic flow reconstruction using mobile sensors and loop detector data," University of California Transportation Center, Working Papers qt6v40f0bs, University of California Transportation Center.
    6. Comert, Gurcan, 2016. "Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters," European Journal of Operational Research, Elsevier, vol. 252(2), pages 502-521.
    7. 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.
    8. Bliemer, Michiel C.J. & Raadsen, Mark P.H., 2020. "Static traffic assignment with residual queues and spillback," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 303-319.
    9. Hao, Peng & Ban, Xuegang, 2015. "Long queue estimation for signalized intersections using mobile data," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 54-73.
    10. Yuan, Tianchen & Ioannou, Petros A., 2023. "Coordinated Traffic Flow Control in a Connected Environment," Institute of Transportation Studies, Working Paper Series qt6q67f9z4, Institute of Transportation Studies, UC Davis.
    11. Westgate, Bradford S. & Woodard, Dawn B. & Matteson, David S. & Henderson, Shane G., 2016. "Large-network travel time distribution estimation for ambulances," European Journal of Operational Research, Elsevier, vol. 252(1), pages 322-333.
    12. 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.
    13. Han, Youngjun & Chen, Danjue & Ahn, Soyoung, 2017. "Variable speed limit control at fixed freeway bottlenecks using connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 113-134.
    14. Bliemer, Michiel C.J. & Raadsen, Mark P.H., 2019. "Continuous-time general link transmission model with simplified fanning, Part I: Theory and link model formulation," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 442-470.
    15. Yin, Kai & Wang, Wen & Bruce Wang, Xiubin & Adams, Teresa M., 2015. "Link travel time inference using entry/exit information of trips on a network," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 303-321.
    16. Hans, Etienne & Chiabaut, Nicolas & Leclercq, Ludovic, 2015. "Applying variational theory to travel time estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 169-181.
    17. Martínez, Irene & Jin, Wen-Long, 2020. "Optimal location problem for variable speed limit application areas," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 221-246.
    18. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    19. Costeseque, Guillaume & Lebacque, Jean-Patrick, 2014. "A variational formulation for higher order macroscopic traffic flow models: Numerical investigation," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 112-133.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:69:y:2014:i:c:p:19-30. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.