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Statistical approach for activity-based model calibration based on plate scanning and traffic counts data

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  • Siripirote, Treerapot
  • Sumalee, Agachai
  • Ho, H.W.
  • Lam, William H.K.

Abstract

Traditionally, activity-based models (ABM) are estimated from travel diary survey data. The estimated results can be biased due to low-sampling size and inaccurate travel diary data. For an accurate calibration of ABM parameters, a maximum-likelihood method that uses multiple sources of roadside observations (link counts and/or plate scanning data) is proposed. Plate scanning information (sensor path information) consists of sequences of times and partial paths that the scanned vehicles are observed over the preinstalled plate scanning locations. Statistical performances of the proposed method are evaluated on a test network using Monte Carlo technique for simulating the link flows and sensor path information. Multiday observations are simulated and derived from the true ABM parameters adopted in the choice models of activity pattern, time of the day, destination and mode. By assuming different number of plate scanning locations and identification rates, impacts of data quantity and data quality on ABM calibration are studied. The results illustrate the efficiency of the proposed model in using plate scanning information for ABM calibration and its potential for large and complex network applications.

Suggested Citation

  • Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
  • Handle: RePEc:eee:transb:v:78:y:2015:i:c:p:280-300
    DOI: 10.1016/j.trb.2015.05.004
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    as
    1. Watling, David, 2006. "User equilibrium traffic network assignment with stochastic travel times and late arrival penalty," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1539-1556, December.
    2. 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.
    3. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    4. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
    5. He, Sheng-xue, 2013. "A graphical approach to identify sensor locations for link flow inference," Transportation Research Part B: Methodological, Elsevier, vol. 51(C), pages 65-76.
    6. Maruyama, Takuya & Sumalee, Agachai, 2007. "Efficiency and equity comparison of cordon- and area-based road pricing schemes using a trip-chain equilibrium model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(7), pages 655-671, August.
    7. Watling, David P., 1994. "Maximum likelihood estimation of an origin-destination matrix from a partial registration plate survey," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 289-314, August.
    8. Shoichiro Nakayama & Richard D., 2009. "Estimation of Parameters of Network Equilibrium Models: A Maximum Likelihood Method and Statistical Properties of Network Flow," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 39-56, Springer.
    9. Bell, Michael G. H., 1991. "The estimation of origin-destination matrices by constrained generalised least squares," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 13-22, February.
    10. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
    11. 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.
    12. 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.
    13. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    14. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    15. Shen, Wei & Wynter, Laura, 2012. "A new one-level convex optimization approach for estimating origin–destination demand," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1535-1555.
    16. Zhi-Chun Li & William Lam & S. Wong & A. Sumalee, 2010. "An activity-based approach for scheduling multimodal transit services," Transportation, Springer, vol. 37(5), pages 751-774, September.
    17. Mark Bradley & Peter Vovsha, 2005. "A model for joint choice of daily activity pattern types of household members," Transportation, Springer, vol. 32(5), pages 545-571, September.
    18. Clark, Stephen D. & Watling, David P., 2002. "Sensitivity analysis of the probit-based stochastic user equilibrium assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 617-635, August.
    19. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    20. Yang, Hai & Sasaki, Tsuna & Iida, Yasunori & Asakura, Yasuo, 1992. "Estimation of origin-destination matrices from link traffic counts on congested networks," Transportation Research Part B: Methodological, Elsevier, vol. 26(6), pages 417-434, December.
    21. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
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