IDEAS home Printed from https://ideas.repec.org/a/kap/netspa/v23y2023i1d10.1007_s11067-022-09582-9.html
   My bibliography  Save this article

Traffic Graph Convolutional Network for Dynamic Urban Travel Speed Estimation

Author

Listed:
  • Huan Ngo

    (University of Memphis)

  • Sabyasachee Mishra

    (University of Memphis)

Abstract

The dynamic urban link travel speed estimation (DU-LSE) problem has been studied extensively with approaches ranging from model to data driven since it benefits multiple applications in transport mobility, especially in dense cities. However, with drawbacks such as heavy assumption in model-driven and not being capable for big city network in data-driven, there has not been a consensus on the most effective method. This study aims to develop a Sequential Three Step framework to solve the DU-LSE problem using only the passively collected taxi trip data. The framework makes use of two deep learning models namely Traffic Graph Convolution (TGCN) and its recurrent variant TGCNlstm to capture both spatial and temporal correlation between road segments. The proposed framework has three advantages over similar approaches: (1) it uses only the affordable taxi data and overcomes the data’s incompleteness both in spatial (full GPS trajectory is not available) and temporal (incomplete historic time-series) domain, (2) it is specifically designed to preserve the directionality nature of traffic flow, and (3) it is capable for large networks. The model results and validations suggest the framework can achieve high enough accuracy and will provide valuable mobility data for cities especially those without traffic sensing infrastructure already in place.

Suggested Citation

  • Huan Ngo & Sabyasachee Mishra, 2023. "Traffic Graph Convolutional Network for Dynamic Urban Travel Speed Estimation," Networks and Spatial Economics, Springer, vol. 23(1), pages 179-222, March.
    • Handle: RePEc:kap:netspa:v:23:y:2023:i:1:d:10.1007_s11067-022-09582-9
    DOI: 10.1007/s11067-022-09582-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11067-022-09582-9
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11067-022-09582-9?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. Yeon, Jiyoun & Elefteriadou, Lily & Lawphongpanich, Siriphong, 2008. "Travel time estimation on a freeway using Discrete Time Markov Chains," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 325-338, May.
    2. Alireza Ermagun & David Levinson, 2018. "Spatiotemporal traffic forecasting: review and proposed directions," Transport Reviews, Taylor & Francis Journals, vol. 38(6), pages 786-814, November.
    3. 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.
    4. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    Full references (including those not matched with items on IDEAS)

    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. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    2. Luo, Xiaoqian & Wang, Dianhai & Ma, Dongfang & Jin, Sheng, 2019. "Grouped travel time estimation in signalized arterials using point-to-point detectors," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 130-151.
    3. Huili Zhang & Yinfeng Xu & Xingang Wen, 2015. "Optimal shortest path set problem in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 511-530, April.
    4. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Tseng, Yin-Yen & Verhoef, Erik T., 2013. "Door-to-door travel times in RP departure time choice models: An approximation method using GPS data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 134-150.
    5. 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.
    6. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
    7. Melchiori, Anna & Sgalambro, Antonino, 2020. "A branch and price algorithm to solve the Quickest Multicommodity k-splittable Flow Problem," European Journal of Operational Research, Elsevier, vol. 282(3), pages 846-857.
    8. Dimitris Bertsimas & Arthur Delarue & Patrick Jaillet & Sébastien Martin, 2019. "Travel Time Estimation in the Age of Big Data," Operations Research, INFORMS, vol. 67(2), pages 498-515, March.
    9. Luss, Hanan & Wong, Richard T., 2005. "Graceful reassignment of excessively long communications paths in networks," European Journal of Operational Research, Elsevier, vol. 160(2), pages 395-415, January.
    10. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    11. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    12. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    13. Chen, Bi Yu & Chen, Xiao-Wei & Chen, Hui-Ping & Lam, William H.K., 2020. "Efficient algorithm for finding k shortest paths based on re-optimization technique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    14. Doan, Xuan Vinh, 2022. "Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning," European Journal of Operational Research, Elsevier, vol. 300(1), pages 73-84.
    15. Hela Masri & Saoussen Krichen, 2018. "Exact and approximate approaches for the Pareto front generation of the single path multicommodity flow problem," Annals of Operations Research, Springer, vol. 267(1), pages 353-377, August.
    16. Fernández, Elena & Pozo, Miguel A. & Puerto, Justo & Scozzari, Andrea, 2017. "Ordered Weighted Average optimization in Multiobjective Spanning Tree Problem," European Journal of Operational Research, Elsevier, vol. 260(3), pages 886-903.
    17. 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.
    18. Alessandra Griffa & Mathieu Mach & Julien Dedelley & Daniel Gutierrez-Barragan & Alessandro Gozzi & Gilles Allali & Joanes Grandjean & Dimitri Ville & Enrico Amico, 2023. "Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    19. Gutierrez-Lythgoe, Antonio, 2023. "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper 117103, University Library of Munich, Germany.
    20. T. Gomes & J. Craveirinha & L. Jorge, 2010. "An effective algorithm for obtaining the whole set of minimal cost pairs of disjoint paths with dual arc costs," Journal of Combinatorial Optimization, Springer, vol. 19(3), pages 394-414, April.

    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:kap:netspa:v:23:y:2023:i:1:d:10.1007_s11067-022-09582-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.