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Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors

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  • Chang, Gang-Len
  • Wu, Jifeng

Abstract

This research presents a dynamic system model and its on-line estimation algorithm for time-varying freeway origin-destination (O-D) matrices. The proposed model employs information formation form mainline traffic counts, ramp flow measurements, and macroscopic traffic characteristics to construct a set of dynamic equations, which realistically consider the interrelations between O-D distributions and observed flows under congested conditions. To improve the operational efficiency necessary for real-time applications, a revised model with some approximation have also been developed. Due to the difficulty in acquiring the real-world data, the proposed model was evaluated with simulation experiments. The results of laboratory evaluation indicate that the proposed methods offers a promising direction for tackling this complex issue.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:28:y:1994:i:2:p:141-160
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    Citations

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    Cited by:

    1. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    2. Lin, Pei-Wei & Chang, Gang-Len, 2007. "A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 554-572, June.
    3. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    4. 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.
    5. Sun, Carlos & Porwal, Himanshu, 2000. "Dynamic Origin/Destination Estimation Using True Section Densities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0f0711s6, Institute of Transportation Studies, UC Berkeley.
    6. Yasuo Asakura & Eiji Hato & Masuo Kashiwadani, 2000. "Origin-destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network," Transportation, Springer, vol. 27(4), pages 419-438, December.
    7. Guo, Jianhua & Liu, Yu & Li, Xiugang & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 27-40.
    8. 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.
    9. 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.
    10. Ritchie, Stephen & Sun, Carlos, 1998. "Section Related Measures of Traffic System Performance: Final Report," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4sc0t3bv, Institute of Transportation Studies, UC Berkeley.
    11. Hsun-Jung Cho & Yow-Jen Jou & Chien-Lun Lan, 2009. "Time Dependent Origin-destination Estimation from Traffic Count without Prior Information," Networks and Spatial Economics, Springer, vol. 9(2), pages 145-170, June.
    12. Zhang, Michael & Nie, Yu & Shen, Wei & Lee, Ming S. & Jansuwan, Sarawut & Chootinan, Piya & Pravinvongvuth, Surachet & Chen, Anthony & Recker, Will W., 2008. "Development of A Path Flow Estimator for Inferring Steady-State and Time-Dependent Origin-Destination Trip Matrices," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3nr033sc, Institute of Transportation Studies, UC Berkeley.
    13. Garcia, Reinaldo C., 2003. "Implementing a Kalman Filtering Dynamic O-D Algorithm within Paramics- Analysing Quadstone Won Efforts for the Dynamic O-D Estimation Problem," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6vf61301, Institute of Transportation Studies, UC Berkeley.
    14. Louis Grange & Felipe González & Shlomo Bekhor, 2017. "Path Flow and Trip Matrix Estimation Using Link Flow Density," Networks and Spatial Economics, Springer, vol. 17(1), pages 173-195, March.
    15. Garcia, Reinaldo C., 2002. "Implementing A Dynamic O-D Estimation Algorithm within the Microscopic Traffic Simulator Paramics," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt0n62j6nq, Institute of Transportation Studies, UC Berkeley.
    16. Wu, Jifeng, 1997. "A real-time origin-destination matrix updating algorithm for on-line applications," Transportation Research Part B: Methodological, Elsevier, vol. 31(5), pages 381-396, October.

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