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Confidence interval estimation for path flow estimator

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  • Chootinan, Piya
  • Chen, Anthony

Abstract

The uncertainty of an origin–destination (O–D) trip table estimate is affected by two factors: (i) the multiplicity of solutions due to the underspecified nature of the problem, and (ii) the errors of traffic counts. In this paper, a confidence interval estimation procedure for path flow estimator (PFE) is developed for assessing the quality of O–D trip tables estimated from traffic counts. The confidence interval estimation consists of two parts: (i) a generalized demand scale (GDS) measure for quantifying the intrinsic underspecified nature of the O–D estimation problem at various spatial levels, and (ii) an error bound to quantify the contribution of input errors (traffic counts) to the estimation results. Numerical results using PFE as the O–D estimator show that the proposed confidence interval estimation procedure is able to separate the two sources of uncertainty in constructing the confidence intervals at various spatial levels. Simulation results also confirm that the proposed quality measure indeed contain the true estimates within the defined confidence intervals.

Suggested Citation

  • Chootinan, Piya & Chen, Anthony, 2011. "Confidence interval estimation for path flow estimator," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1680-1698.
  • Handle: RePEc:eee:transb:v:45:y:2011:i:10:p:1680-1698
    DOI: 10.1016/j.trb.2011.07.001
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    References listed on IDEAS

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    1. Fisk, Caroline, 1980. "Some developments in equilibrium traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 243-255, September.
    2. Yang, Hai & Zhou, Jing, 1998. "Optimal traffic counting locations for origin-destination matrix estimation," Transportation Research Part B: Methodological, Elsevier, vol. 32(2), pages 109-126, February.
    3. Bierlaire, Michel, 2002. "The total demand scale: a new measure of quality for static and dynamic origin-destination trip tables," Transportation Research Part B: Methodological, Elsevier, vol. 36(9), pages 837-850, November.
    4. Yang, Chao & Chen, Anthony, 2009. "Sensitivity analysis of the combined travel demand model with applications," European Journal of Operational Research, Elsevier, vol. 198(3), pages 909-921, November.
    5. Yang, Hai & Iida, Yasunori & Sasaki, Tsuna, 1991. "An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 25(5), pages 351-363, October.
    6. Sherali, Hanif D. & Sivanandan, R. & Hobeika, Antoine G., 1994. "A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes," Transportation Research Part B: Methodological, Elsevier, vol. 28(3), pages 213-233, June.
    7. 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.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Simonelli, Fulvio & Marzano, Vittorio & Papola, Andrea & Vitiello, Iolanda, 2012. "A network sensor location procedure accounting for o–d matrix estimate variability," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1624-1638.

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