Estimation of origin–destination matrices using link counts and partial path data
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After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a multiobjective model which utilizes combinations of vehicle counts and partial path data. The methods were tested to estimate the origin–destination matrix of a large-scale network from Mashhad City with 163 traffic zones and 2093 links, and the results were compared with the conventional gradient-based algorithm. The results show that the innovative methods performed better as compared to the gradient-based algorithm.
KeywordsOrigin–destination matrix estimation Automated vehicle identification data Vehicle count data Innovative method Gradient-based algorithm
The authors would like to thank PTV group for providing PTV VISUM software and anonymous reviewers who helped to improve the paper with their comments and suggestions.
- Asakura, Y., Hato, E., Kashiwadani, M.: Origin–destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network. Kluwer Acad. Publ. 27, 419–438 (2000)Google Scholar
- Ashok, K.: Estimation and prediction of time-dependent origin–destination flows. Ph.D. Thesis, Massachusetts Inst. Technol. USA. (1996). https://doi.org/10.1287/trsc.126.96.36.1993
- Bera, S., Rao, K.V.K.: Estimation of origin-destination matrix from traffic counts: the state of the art. Eur. Transp. Trasp. Eur. 49, 3–23 (2011)Google Scholar
- Dixon, M.P., Rilett, L.R.: Population origin–destination estimation using automatic vehicle identification and volume data. J. Transp. Eng. 131, 75–82 (2005). https://doi.org/10.1061/(ASCE)0733-947X(2005)131:2(75) CrossRefGoogle Scholar
- Gentile, G., Noekel, K.: Linear user cost equilibrium: a new algorithm for traffic assignment. In: European Transport Conference, At Leeuwenhorst Conference Centre, The Netherlands, pp. 1–52 (2009)Google Scholar
- ITSR: Mashhad Comprehensive Transportation Studies. Sharif University of Technology, Tehran (1995)Google Scholar
- Jamali, A.: O–D demand estimation base on automatic vehicle identification data. Master Thesis, Sharif University Technology, Iran (2014)Google Scholar
- Nguyen, S.: Estimating an OD matrix from network data: a network equilibrium approach. Universite de Montreal, Centre de Recherche sur les Transports, Montreal (1977)Google Scholar
- Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall, Inc., Englewood Cliffs (1985)Google Scholar
- Spiess, H.: A gradient approach for the O–D matrix adjustment problem. Centre for Research on Transportation, University of Montreal, Montreal (1990)Google Scholar
- Talebian Yazdi, P.: Solving location problem for vehicle identification sensors to observe or estimate path flows in large-scale networks. Master Thesis, Sharif University Technology, Iran (2018)Google Scholar