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Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data

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  • Guo, Jianhua
  • Liu, Yu
  • Li, Xiugang
  • Huang, Wei
  • Cao, Jinde
  • Wei, Yun

Abstract

The origin–destination (OD) demand is a crucial input for traffic planning and management. The Radio Frequency Identification (RFID) is an emerging technology to collect traffic data. In this paper, the RFID data were used to derive the preliminary OD matrix and the dynamic assignment matrix by matching the RFID data collected in origins and destinations along the road links. However, the preliminary OD matrix was inaccurate due to reasons like malfunction or limited market penetration rate of equipped vehicles. To solve this problem, an optimization model was developed based on a least squares model. The optimization model estimated dynamic OD matrix by integrating the preliminary OD matrix, dynamic assignment matrix, and link flows detected by the inductive loop detectors. The genetic algorithm was adopted to solve the optimization model. Two criteria were proposed to evaluate the optimization results. The first was the correlation coefficient between the OD matrices before and after optimization, and the second was the ratio of the estimated OD flows starting from an entrance to the traffic volumes of the same entrance detected by the inductive loop detectors. The case study using real world data collected in Nanjing, China demonstrated that the proposed method is efficient to derive accurate dynamic OD matrix.

Suggested Citation

  • Guo, Jianhua & Liu, Yu & Li, Xiugang & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "Enhanced least square based dynamic OD matrix estimation using Radio Frequency Identification data," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 27-40.
    • Handle: RePEc:eee:matcom:v:155:y:2019:i:c:p:27-40
    DOI: 10.1016/j.matcom.2017.10.014
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    References listed on IDEAS

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    1. Yong Wang & Xiaolei Ma & Yong Liu & Ke Gong & Kristian C Henricakson & Maozeng Xu & Yinhai Wang, 2016. "A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-24, January.
    2. Chang, Gang-Len & Wu, Jifeng, 1994. "Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 28(2), pages 141-160, April.
    3. Watling, David P. & Maher, Michael J., 1992. "A statistical procedure for estimating a mean origin-destination matrix from a partial registration plate survey," Transportation Research Part B: Methodological, Elsevier, vol. 26(3), pages 171-193, June.
    4. Nancy L. Nihan & Gary A. Davis, 1989. "Application of Prediction-Error Minimization and Maximum Likelihood to Estimate Intersection O-D Matrices from Traffic Counts," Transportation Science, INFORMS, vol. 23(2), pages 77-90, May.
    5. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    6. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    7. Li, Baibing & De Moor, Bart, 1999. "Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 33(3), pages 203-214, April.
    8. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    9. Spiess, Heinz, 1987. "A maximum likelihood model for estimating origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 21(5), pages 395-412, October.
    10. Yang, Hai, 1995. "Heuristic algorithms for the bilevel origin-destination matrix estimation problem," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 231-242, August.
    11. Cremer, M. & Keller, H., 1987. "A new class of dynamic methods for the identification of origin-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 117-132, April.
    12. Lo, Hing-Po & Chan, Chi-Pak, 2003. "Simultaneous estimation of an origin-destination matrix and link choice proportions using traffic counts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(9), pages 771-788, November.
    13. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1996. "Estimation of an origin-destination matrix with random link choice proportions: A statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 309-324, August.
    14. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    15. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    16. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
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    2. Liu, Ping & Fu, Zao & Cao, Jinde & Wei, Yun & Guo, Jianhua & Huang, Wei, 2020. "A decentralized strategy for generalized Nash equilibrium with linear coupling constraints," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 221-232.

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