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Fine-grained OD estimation with automated zoning and sparsity regularisation

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  • Menon, Aditya Krishna
  • Cai, Chen
  • Wang, Weihong
  • Wen, Tao
  • Chen, Fang

Abstract

Given a road network, a fundamental object of interest is the matrix of origin destination (OD) flows. Estimation of this matrix involves at least three sub-problems: (i) determining a suitable set of traffic analysis zones, (ii) the formulation of an optimisation problem to determine the OD matrix, and (iii) a means of evaluating a candidate estimate of the OD matrix. This paper describes a means of addressing each of these concerns. We propose to automatically uncover a suitable set of traffic analysis zones based on observed link flows. We then employ regularisation to encourage the estimation of a sparse OD matrix. We finally propose to evaluate a candidate OD matrix based on its predictive power on held out link flows. Analysis of our approach on a real-world transport network reveals that it discovers automated zones that accurately capture regions of interest in the network, and a corresponding OD matrix that accurately predicts observed link flows.

Suggested Citation

  • Menon, Aditya Krishna & Cai, Chen & Wang, Weihong & Wen, Tao & Chen, Fang, 2015. "Fine-grained OD estimation with automated zoning and sparsity regularisation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 150-172.
    • Handle: RePEc:eee:transb:v:80:y:2015:i:c:p:150-172
    DOI: 10.1016/j.trb.2015.07.003
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    2. Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.

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