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An integrated model for estimating time-varying network origin-destination distributions

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  • Chang, Gang-Len
  • Tao, Xianding

Abstract

This paper presents an integrated method for estimating time-varying O-Ds in urban networks. The proposed method starts with our previously developed two-stage, non-assignment-based model that can yield a time-varying O-Ds without a reliable prior O-D set and a dynamic traffic assignment model (DTA). With the initial estimated O-D set, one can compute the link-incident matrix with any acceptable DTA model and generate a revised distribution of network O-Ds. To further improve the estimation accuracy and also account for the impact of urban signals, we have developed an intersection O-D estimation model that can produce an additional set of system observation constraints based on either existing or estimated intersection turning fractions. Although the statistical properties and variances of such a system under a large-scale network remain to be investigated, the results of simulation experiments have clearly indicated that our proposed method for integrated estimation of time-varying network O-D distributions is quite promising.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:33:y:1999:i:5:p:381-399
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    References listed on IDEAS

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    1. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
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    Cited by:

    1. 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.
    2. 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.
    3. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
    4. 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.

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