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Norm approximation method for handling traffic count inconsistencies in path flow estimator

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  • Chen, Anthony
  • Chootinan, Piya
  • Recker, Will

Abstract

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin-destination (O-D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three Lp-PFE models for estimating consistent path flows and O-D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three Lp-PFE models. In addition, the proposed Lp-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.

Suggested Citation

  • Chen, Anthony & Chootinan, Piya & Recker, Will, 2009. "Norm approximation method for handling traffic count inconsistencies in path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 852-872, September.
    • Handle: RePEc:eee:transb:v:43:y:2009:i:8-9:p:852-872
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    Cited by:

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    10. Chootinan, Piya & Chen, Anthony, 2011. "Confidence interval estimation for path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1680-1698.
    11. Lo, Hong K. & Chen, Anthony & Castillo, Enrique, 2016. "Robust network sensor location for complete link flow observability under uncertaintyAuthor-Name: Xu, Xiangdong," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 1-20.
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    13. Ryu, Seungkyu & Chen, Anthony & Michael Zhang, H. & Recker, Will, 2014. "Path flow estimator for planning applications in small communities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 212-242.
    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. Wang, Dong & Liao, Feixiong & Gao, Ziyou & Rasouli, Soora & Huang, Hai-Jun, 2020. "Tolerance-based column generation for boundedly rational dynamic activity-travel assignment in large-scale networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    16. Li, Guoyuan & Chen, Anthony, 2023. "Strategy-based transit stochastic user equilibrium model with capacity and number-of-transfers constraints," European Journal of Operational Research, Elsevier, vol. 305(1), pages 164-183.
    17. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
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