IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v17y2025i1d10.1007_s12469-023-00346-3.html
   My bibliography  Save this article

Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery

Author

Listed:
  • Awad Abdelhalim

    (Massachusetts Institute of Technology)

  • Jinhua Zhao

    (Massachusetts Institute of Technology)

Abstract

Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operations and passenger real-time information.

Suggested Citation

  • Awad Abdelhalim & Jinhua Zhao, 2025. "Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery," Public Transport, Springer, vol. 17(1), pages 221-246, March.
    • Handle: RePEc:spr:pubtra:v:17:y:2025:i:1:d:10.1007_s12469-023-00346-3
    DOI: 10.1007/s12469-023-00346-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-023-00346-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-023-00346-3?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. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    2. 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.
    3. Zack Aemmer & Andisheh Ranjbari & Don MacKenzie, 2022. "Measurement and classification of transit delays using GTFS-RT data," Public Transport, Springer, vol. 14(2), pages 263-285, June.
    4. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    5. Xin Zhou & Peixin Dong & Jianping Xing & Peijia Sun, 2019. "Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network," Future Internet, MDPI, vol. 11(12), pages 1-11, November.
    6. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    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. Nicholas Molyneaux & Riccardo Scarinci & Michel Bierlaire, 0. "Design and analysis of control strategies for pedestrian flows," Transportation, Springer, vol. 0, pages 1-41.
    2. Nicholas Molyneaux & Riccardo Scarinci & Michel Bierlaire, 2021. "Design and analysis of control strategies for pedestrian flows," Transportation, Springer, vol. 48(4), pages 1767-1807, August.
    3. Sirapop Para & Thanachok Wirotsasithon & Thanisorn Jundee & Merkebe Getachew Demissie & Yoshihide Sekimoto & Filip Biljecki & Santi Phithakkitnukoon, 2024. "G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS data," Public Transport, Springer, vol. 16(3), pages 893-928, October.
    4. 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.
    5. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    6. 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.
    7. MD Sultan Ali & Henrick Haule & John Kodi & Priyanka Alluri & Thobias Sando, 2023. "Transferability of a calibrated microscopic simulation model parameters for operational assessment of transit signal priority," Public Transport, Springer, vol. 15(3), pages 791-812, October.
    8. Yuan, Yun & Zhang, Zhao & Yang, Xianfeng Terry & Zhe, Shandian, 2021. "Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 88-110.
    9. Florin, Ryan & Olariu, Stephan, 2020. "Towards real-time density estimation using vehicle-to-vehicle communications," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 435-456.
    10. Tianxing Dai & Brian D. Taylor, 2023. "Three’s a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic," Public Transport, Springer, vol. 15(2), pages 321-341, June.
    11. 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.
    12. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    13. 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.
    14. Gao, Yang & Levinson, David, 2024. "A multi-stage spatial queueing model with logistic arrivals and departures consistent with the microscopic fundamental diagram and hysteresis," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    15. Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    16. Čičić, Mladen & Johansson, Karl Henrik, 2022. "Front-tracking transition system model for traffic state reconstruction, model learning, and control with application to stop-and-go wave dissipation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 212-236.
    17. 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.
    18. Cheng, Qixiu & Liu, Zhiyuan & Lu, Jiawei & List, George & Liu, Pan & Zhou, Xuesong Simon, 2024. "Using frequency domain analysis to elucidate travel time reliability along congested freeway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    19. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    20. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.

    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:spr:pubtra:v:17:y:2025:i:1:d:10.1007_s12469-023-00346-3. 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.