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Experienced travel time prediction for congested freeways

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  • Yildirimoglu, Mehmet
  • Geroliminis, Nikolas

Abstract

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.

Suggested Citation

  • Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
  • Handle: RePEc:eee:transb:v:53:y:2013:i:c:p:45-63
    DOI: 10.1016/j.trb.2013.03.006
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    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. Li, Xiaopeng & Peng, Fan & Ouyang, Yanfeng, 2010. "Measurement and estimation of traffic oscillation properties," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 1-14, January.
    3. Leclercq, Ludovic & Laval, Jorge A. & Chiabaut, Nicolas, 2011. "Capacity drops at merges: An endogenous model," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1302-1313.
    4. Dion, Francois & Rakha, Hesham, 2006. "Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 745-766, November.
    5. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    6. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    7. Ji, Yuxuan & Geroliminis, Nikolas, 2012. "On the spatial partitioning of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1639-1656.
    8. Coifman, Benjamin, 2002. "Estimating travel times and vehicle trajectories on freeways using dual loop detectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 351-364, May.
    9. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    10. Treiber, Martin & Kesting, Arne & Helbing, Dirk, 2010. "Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 983-1000, September.
    11. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    12. Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
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    2. Verbeeck, C. & Vansteenwegen, P. & Aghezzaf, E.-H., 2016. "Solving the stochastic time-dependent orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 255(3), pages 699-718.
    3. Hu, Zejing & Smirnova, M.N. & Zhang, Yongliang & Smirnov, N.N. & Zhu, Zuojin, 2021. "Estimation of travel time through a composite ring road by a viscoelastic traffic flow model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 501-521.
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    5. Guardiola, I.G. & Leon, T. & Mallor, F., 2014. "A functional approach to monitor and recognize patterns of daily traffic profiles," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 119-136.
    6. Bo Qiu & Wei (David) Fan, 2021. "Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    7. Liu, Wei & Szeto, Wai Yuen, 2020. "Learning and managing stochastic network traffic dynamics with an aggregate traffic representation," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 19-46.
    8. Kaniz Fatima & Sara Moridpour & Tayebeh Saghapour, 2021. "Spatial and Temporal Distribution of Elderly Public Transport Mode Preference," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    9. Liu, Wei & Geroliminis, Nikolas, 2016. "Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 470-494.
    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. Zhang, Kunpeng & Feng, Xiaoliang & Jia, Ning & Zhao, Liang & He, Zhengbing, 2022. "TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    12. 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.
    13. Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    14. Yildirimoglu, Mehmet & Ramezani, Mohsen, 2020. "Demand management with limited cooperation among travellers: A doubly dynamic approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 267-284.

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