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Statistical inference for time varying origin-destination matrices

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  • Hazelton, Martin L.

Abstract

We consider the problem of estimating a sequence of origin-destination matrices from link count data collected on a daily basis. We recommend a parsimonious parameterization for the time varying matrices so as to permit application of standard statistical estimation theory. A number of examples of suitably parameterized matrices are provided. We propose a multivariate normal model for the link counts, based on an underlying overdispersed Poisson process. While likelihood based inference is feasible given information from sufficiently many network links, we focus on Bayesian methods of estimation because of their ability to incorporate prior information in a natural manner. We derive the Bayesian posterior distribution, but note that its normalizing constant is not available in closed form. A Markov chain Monte Carlo algorithm for generating posterior samples is therefore developed. From this we can obtain point estimates, and corresponding measures of precision, for parameters of the origin-destination matrix. The methodology is illustrated by an example involving OD matrix estimation for a section of the road network in the English city of Leicester.

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  • Hazelton, Martin L., 2008. "Statistical inference for time varying origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 542-552, July.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:6:p:542-552
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    References listed on IDEAS

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    2. S. Travis Waller & Sai Chand & Aleksa Zlojutro & Divya Nair & Chence Niu & Jason Wang & Xiang Zhang & Vinayak V. Dixit, 2021. "Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    3. Yang, Yudi & Fan, Yueyue, 2015. "Data dependent input control for origin–destination demand estimation using observability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 385-403.
    4. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.
    5. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    6. Yang, Yudi & Fan, Yueyue & Royset, Johannes O., 2019. "Estimating probability distributions of travel demand on a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 265-286.
    7. Wu, Laiyun & Kang, Jee Eun & Chung, Younshik & Nikolaev, Alexander, 2021. "Inferring origin-Destination demand and user preferences in a multi-modal travel environment using automated fare collection data," Omega, Elsevier, vol. 101(C).
    8. Laha, A. K. & Putatunda, Sayan, 2017. "Real Time Location Prediction with Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.
    10. Tangjian Wei & Feng Shi & Guangming Xu, 2019. "Estimation of Time-Varying Passenger Demand for High Speed Rail System," Complexity, Hindawi, vol. 2019, pages 1-24, March.
    11. Yang, Yudi & Fan, Yueyue & Wets, Roger J.B., 2018. "Stochastic travel demand estimation: Improving network identifiability using multi-day observation sets," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 192-211.
    12. Hazelton, Martin L., 2010. "Bayesian inference for network-based models with a linear inverse structure," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 674-685, June.
    13. 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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