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Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning

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  • Hofleitner, Aude
  • Herring, Ryan
  • Bayen, Alexandre

Abstract

This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:46:y:2012:i:9:p:1097-1122
    DOI: 10.1016/j.trb.2012.03.006
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    Cited by:

    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.
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    3. 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.
    4. Laha, A. K. & Putatunda, Sayan, 2017. "Travel Time Prediction for Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Bayen, Alexandre & Gan, Qijian & Gomes, Gabriel, 2016. "From LOS to VMT, VHT and Beyond Through Data Fusion: Application to Integrate Corridor Management," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7fq6g5td, Institute of Transportation Studies, UC Berkeley.
    6. 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.
    7. 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.
    8. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
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    10. 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.
    11. Wong, Wai & Wong, S.C., 2015. "Systematic bias in transport model calibration arising from the variability of linear data projection," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 1-18.
    12. Hao, Peng & Ban, Xuegang (Jeff) & Guo, Dong & Ji, Qiang, 2014. "Cycle-by-cycle intersection queue length distribution estimation using sample travel times," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 185-204.
    13. 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.
    14. Wang, Zhengli & Jiang, Hai, 2019. "Simultaneous correction of the time and location bias associated with a reported crash by exploiting the spatiotemporal evolution of travel speed," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 199-223.
    15. 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.
    16. 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.
    17. Tang, Jinjun & Zhang, Xinshao & Yu, Tianjian & Liu, Fang, 2021. "Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    18. 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.
    19. Zhang, Canrong & Guan, Hao & Yuan, Yifei & Chen, Weiwei & Wu, Tao, 2020. "Machine learning-driven algorithms for the container relocation problem," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 102-131.

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