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Estimating probability distributions of travel demand on a congested network

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  • Yang, Yudi
  • Fan, Yueyue
  • Royset, Johannes O.

Abstract

We aim to infer the probability density function (pdf) of Origin–Destination (O–D) demand variables using multiple sets of traffic counts over a network. Through integrating statistics and optimization techniques with transportation domain knowledge, we propose an estimation framework based on Generalized Method of Moment (GMM) with both options of exact and approximated estimators. Compared with existing methods for day-to-day O–D matrix estimation, our approach has three unique advantages. First, it is a rigorous statistical method with a capability of incorporating complex traffic network models suitable for a congested network. Second, our estimation framework is flexible to handle a wide variety of probabilistic models. Finally, instead of just providing point estimates, it reveals large sample statistical properties of the proposed estimator, which serve as a theoretical foundation for assessing estimation quality, constructing confidence region and testing model adequacy. Three numerical examples of different scales are accompanied to demonstrate various aspects of the proposed estimation framework.

Suggested Citation

  • Yang, Yudi & Fan, Yueyue & Royset, Johannes O., 2019. "Estimating probability distributions of travel demand on a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 265-286.
    • Handle: RePEc:eee:transb:v:122:y:2019:i:c:p:265-286
    DOI: 10.1016/j.trb.2019.01.008
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    Cited by:

    1. Chao Sun & Yulin Chang & Xin Luan & Qiang Tu & Wenyun Tang, 2020. "Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network," Networks and Spatial Economics, Springer, vol. 20(3), pages 733-755, September.
    2. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.

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