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A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework

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  • Zhou, Xuesong
  • Mahmassani, Hani S.

Abstract

Dynamic origin-destination (OD) estimation and prediction is an essential support function for real-time dynamic traffic assignment model systems for ITS applications. This paper presents a structural state space model to systematically incorporate regular demand pattern information, structural deviations and random fluctuations. By considering demand deviations from the a priori estimate of the regular pattern as a time-varying process with smooth trend, a polynomial trend filter is developed to capture possible structural deviations in real-time demand. Based on a Kalman filtering framework, an optimal adaptive procedure is further proposed to capture day-to-day demand evolution, and update the a priori regular demand pattern estimate using new real-time estimates and observations obtained every day. These models can be naturally integrated into a real-time dynamic traffic assignment system and provide an effective and efficient approach to utilize the real-time traffic data continuously in operational settings. A case study based on the Irvine test bed network is conducted to illustrate the proposed methodology.

Suggested Citation

  • Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    • Handle: RePEc:eee:transb:v:41:y:2007:i:8:p:823-840
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    1. Chang, Gang-Len & Wu, Jifeng, 1994. "Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 28(2), pages 141-160, April.
    2. 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.
    3. Chang, Gang-Len & Tao, Xianding, 1999. "An integrated model for estimating time-varying network origin-destination distributions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 381-399, June.
    4. Cremer, M. & Keller, H., 1987. "A new class of dynamic methods for the identification of origin-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 117-132, April.
    5. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    6. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
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    Cited by:

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    5. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.
    6. Nie, Yu (Marco) & Zhang, H.M., 2008. "A variational inequality formulation for inferring dynamic origin-destination travel demands," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 635-662, August.
    7. Fang, Zhixiang & Shaw, Shih-Lung & Tu, Wei & Li, Qingquan & Li, Yuguang, 2012. "Spatiotemporal analysis of critical transportation links based on time geographic concepts: a case study of critical bridges in Wuhan, China," Journal of Transport Geography, Elsevier, vol. 23(C), pages 44-59.
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    9. 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.
    10. Lu, Chung-Cheng & Ying, Kuo-Ching & Chen, Hui-Ju, 2016. "Real-time relief distribution in the aftermath of disasters – A rolling horizon approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 1-20.
    11. Cantelmo, Guido & Qurashi, Moeid & Prakash, A. Arun & Antoniou, Constantinos & Viti, Francesco, 2020. "Incorporating trip chaining within online demand estimation," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 171-187.
    12. Lederman, Roger & Wynter, Laura, 2011. "Real-time traffic estimation using data expansion," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1062-1079, August.
    13. Liu, Jiangtao & Zhou, Xuesong, 2019. "Observability quantification of public transportation systems with heterogeneous data sources: An information-space projection approach based on discretized space-time network flow models," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 302-323.
    14. Han Zheng & Junhua Chen & Zhaocha Huang & Kuan Yang & Jianhao Zhu, 2022. "Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method," Mathematics, MDPI, vol. 10(19), pages 1-30, October.
    15. Guo, Qiangqiang & Ban, Xuegang (Jeff), 2023. "A multi-scale control framework for urban traffic control with connected and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    16. Xing, Tao & Zhou, Xuesong, 2011. "Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1660-1679.
    17. Alex A. Kurzhanskiy, 2022. "A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network," Mathematics, MDPI, vol. 10(3), pages 1-11, February.
    18. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
    19. Tangjian Wei & Feng Shi & Guangming Xu, 2019. "Estimation of Time-Varying Passenger Demand for High Speed Rail System," Complexity, Hindawi, vol. 2019, pages 1-24, March.
    20. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    21. Chen, Roger B., 2018. "Models of count with endogenous choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 862-875.
    22. Yong Lin, 2023. "Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    23. Felipe Zúñiga & Juan Carlos Muñoz & Ricardo Giesen, 2021. "Estimation and prediction of dynamic matrix travel on a public transport corridor using historical data and real-time information," Public Transport, Springer, vol. 13(1), pages 59-80, March.
    24. Dimitris Bertsimas & Julia Yan, 2018. "From Physical Properties of Transportation Flows to Demand Estimation: An Optimization Approach," Transportation Science, INFORMS, vol. 52(4), pages 1002-1011, August.
    25. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.

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