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Travel time estimation on a freeway using Discrete Time Markov Chains

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  • Yeon, Jiyoun
  • Elefteriadou, Lily
  • Lawphongpanich, Siriphong

Abstract

Travel time is widely recognized as an important performance measure for assessing highway operating conditions. There are two methods for obtaining travel time: direct measurement, or estimation. For the latter, previously developed models tend to underestimate travel times under congested conditions because of the difficulties of calculations of vehicle queue formations and dissipations. The purpose of this study is to develop a model that can estimate travel time on a freeway using Discrete Time Markov Chains (DTMC) where the states correspond to whether or not the link is congested. The expected travel time for a given route can be obtained for time periods during which the demand is relatively constant. Estimates from the model are compared to field-measured travel time. Statistical analyses suggest that the estimated travel times do not differ from the measured travel time at the 99% confidence level.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:4:p:325-338
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    References listed on IDEAS

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    2. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    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. 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.
    5. 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.
    6. Tang, Jinjun & Hu, Jin & Hao, Wei & Chen, Xinqiang & Qi, Yong, 2020. "Markov Chains based route travel time estimation considering link spatio-temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    7. 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.
    8. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    9. Wörz, Sascha & Bernhardt, Heinz, 2017. "A novel method for optimal fuel consumption estimation and planning for transportation systems," Energy, Elsevier, vol. 120(C), pages 565-572.
    10. 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.
    11. Guo, Bao & Li, Minglun & Zhou, Mengnan & Zhang, Fan & Wang, Pu, 2023. "A new anomalous travel demand prediction method combining Markov model and complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    12. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
    13. Zhong, R.X. & Xie, X.X. & Luo, J.C. & Pan, T.L. & Lam, W.H.K. & Sumalee, A., 2020. "Modeling double time-scale travel time processes with application to assessing the resilience of transportation systems," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 228-248.
    14. Ruoqi Wang & Jiawei Li & Ruibin Bai, 2023. "Prediction and Analysis of Container Terminal Logistics Arrival Time Based on Simulation Interactive Modeling: A Case Study of Ningbo Port," Mathematics, MDPI, vol. 11(15), pages 1-23, July.

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