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A Methodological Framework of Travel Time Distribution Estimation for Urban Signalized Arterial Roads

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  • Fangfang Zheng

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031 Chengdu, China; National-Local Association Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong University, 610031 Chengdu, China)

  • Henk van Zuylen

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031 Chengdu, China; Department of Transport and Planning, Delft University of Technology, 2628 CN Delft, Netherlands)

  • Xiaobo Liu

    (School of Transportation and Logistics, Southwest Jiaotong University, 610031 Chengdu, China; National-Local Association Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong University, 610031 Chengdu, China)

Abstract

Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic flows, signals, and other conditions on the infrastructure. However, the most common way both in literature and practice is to estimate or predict only expected travel times, not travel time distributions. By doing so, it fails to provide full insight into the travel time dynamics and variability on urban roads. Another limitation of this common approach is that the effect of traffic measures on travel time reliability cannot be evaluated. In this paper, an analytical travel time distribution model is presented especially for urban roads with fixed-time controlled intersections by investigating the underlying mechanisms of urban travel times. Different from mean travel time models or deterministic travel time models, the proposed model takes stochastic properties of traffic flow, stochastic arrivals and departures at intersections, and traffic signal coordination between adjacent intersections into account, and therefore, is able to capture the delay dynamics and uncertainty at intersections. The queue spillback phenomenon is explicitly taken into account by applying shockwave theory in a probabilistic way. The proposed model was further validated with both VISSIM simulation data and field GPS data collected in a Chinese city. The results demonstrate that the travel time distributions derived from the analytical model can well represent those from VISSIM simulation. The comparison with field GPS data shows that the model estimated link and trip travel time distributions can also represent the field travel time distributions, though a small discrepancy can be observed in both middle range travel times and higher travel times.

Suggested Citation

  • Fangfang Zheng & Henk van Zuylen & Xiaobo Liu, 2017. "A Methodological Framework of Travel Time Distribution Estimation for Urban Signalized Arterial Roads," Transportation Science, INFORMS, vol. 51(3), pages 893-917, August.
    • Handle: RePEc:inm:ortrsc:v:51:y:2017:i:3:p:893-917
    DOI: 10.287/trsc.2016.0718
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    References listed on IDEAS

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    1. Satu Innamaa, 2005. "Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway," Transportation, Springer, vol. 32(6), pages 649-669, November.
    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. Wu, Xinkai & Liu, Henry X., 2011. "A shockwave profile model for traffic flow on congested urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1768-1786.
    4. 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.
    5. Viti, Francesco & van Zuylen, Henk J., 2010. "Probabilistic models for queues at fixed control signals," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 120-135, January.
    6. Hong K. Lo, 2001. "A Cell-Based Traffic Control Formulation: Strategies and Benefits of Dynamic Timing Plans," Transportation Science, INFORMS, vol. 35(2), pages 148-164, May.
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    5. 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.
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    7. Yao, Jia & Chen, Yanqin & Chen, Anthony & Liu, Zhiyuan, 2024. "Modeling link capacity constraints with physical queuing and toll in the bi-modal mixed road network including bus and car modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).

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