Developing an iterative procedure to estimate origin-destination matrix based on two-point license plate tracking system

Document Type : Article

Authors

1 Department of Industrial Engineering, Branch of Tehran South, Islamic Azad University, Tehran, Iran

2 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Origin-Destination Matrix, one of the most important elements in transportation planning, is usually estimated by various techniques such as mathematical modeling, statistical methods, and heuristic approaches. Since using electronic devices is rapidly increased to help decision makers to improve models’ capabilities, an iterative procedure is proposed in this paper to estimate the O-D Matrix according to vehicles’ license plates detection. The main concept is to track vehicles on the first and the last links equipped by plate camera over the shortest path from origins to destinations. A two-step procedure and mathematical models are developed to adjust assigned the passing traffic to the network links by minimizing deviations between the observed and estimated truck traffic volumes. The proposed procedure is explained by an illustrative example followed by validation using experimental road network that covers seven eastern provinces of Iran including 310 nodes, 400 two-way edges, and around 3600 origin and destination pairs. Results revealed that the proposed procedure is capable to estimate O-D matrix when the network links are optimally located and equipped by road camera detection systems. In addition, such as the other heuristic approaches, the proposed procedure is sensitive to the number of iterations on the estimation accuracy.

Keywords


References:
[1].    Bera, S. and Rao, K.V. “Estimation of origin-destination matrix from traffic counts: the state of the art”, European Transport ,no49,pp 3-23, (2011). 
[2].    Castillo, E., Menéndez, J.M. and Sánchez-Cambronero, S. “Predicting traffic flow using Bayesian networks”. Transportation Research Part B: Methodological, 42(5), pp.482-509, (2008).
[3].    Yang, H., Yang, C. and Gan, L. “Models and algorithms for the screen line-based traffic-counting location problems”, Computers & Operations Research, 33(3), pp.836-858, (2006).
[4].    Wang, N., Gentili, M. and Mirchandani, P. “Model to locate sensors for estimation of static origin-destination volumes given prior flow information”. Transportation Research Record: Journal of the Transportation Research Board, (2283), pp.67-73, (2012).
[5].    Yang, H. and Zhou, J. “Optimal traffic counting locations for origin–destination matrix estimation”, Transportation Research Part B: Methodological, 32(2), pp.109-126, (1998).
[6].    Ehlert, A., Bell, M.G. and Grosso, S. “The optimisation of traffic count locations in road networks”, Transportation Research Part B: Methodological, 40(6), pp.460-479, (2006).
[7].    Yang, H., Iida, Y. and Sasaki, T. “An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts”, Transportation Research Part B: Methodological, 25(5), pp.351-363, (1991).
[8].    Larsson, T., Lundgren, J.T. and Peterson, A. “Allocation of Link Flow Detectors for Origin‐Destination Matrix Estimation—A Comparative Study”, Computer‐Aided Civil and Infrastructure Engineering, 25(2), pp.116-131, (2010).
[9].    Cipriani, E., Fusco, G., Gori, S. et al. “Heuristic methods for the optimal location of road traffic monitoring”, In Intelligent Transportation Systems Conference, ITSC'06. IEEE (pp. 1072-1077). IEEE , (2006).
[10].    Bianco, L., Confessore, G. and Reverberi, P.“A network based model for traffic sensor location with implications on O/D matrix estimates”, Transportation Science, 35(1), pp.50-60, (2001).
[11].    Mínguez, R., Sánchez-Cambronero, S., Castillo, E. et al. “Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks”, Transportation Research Part B: Methodological, 44(2), pp.282-298, (2010).
[12].    Sun, D., Chang, Y. and Zhang, L.” An ant colony optimisation model for traffic counting location problem”, In Proceedings of the Institution of Civil Engineers-Transport, Vol. 165, No. 3, pp. 175-185, (2012).
[13].    Kim, H.J., Chung, H.I. and Chung, S.Y., 2003. Selection of the optimal traffic counting locations for estimating origin-destination trip matrix. Journal of the Eastern Asia Society for Transportation Studies, 5, pp.1353-1365.
[14].    Fei, X., Mahmassani, H. and Eisenman, S. “Sensor coverage and location for real-time traffic prediction in large-scale networks”, Transportation Research Record: Journal of the Transportation Research Board, (2039), pp.1-15, (2007).
[15].    Khan, T. and Anderson, M. “Accurately Estimating Origin/Destination Matrices in Situations with Limited Traffic Counts: Case Study Huntsville, AL”, International Journal of Traffic and Transportation Engineering, 5(3), pp.64-72, (2016).
[16].    Asakura, Y., Hato, E. and Kashiwadani, M. “Origin-destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network”, Transportation, 27(4), pp.419-438, (2000).
[17].    Ioli, E.D., Edward D Ioli Trust. Vehicle identification, tracking and enforcement system. U.S. Patent 8,937,559, (2015).
[18].    Savrasovs, M. and Pticina, I.“Methodology of OD Matrix Estimation Based on Video Recordings and Traffic Counts”, Procedia Engineering, 178, pp.289-297,(2017).
[19].    Hu, S.R., Peeta, S. and Chu, C.H.“Identification of vehicle sensor locations for link-based network traffic applications”, Transportation Research Part B: Methodological, 43(8-9), pp.873-894, (2009).
[20].    Castillo, E., Gallego, I., Sanchez-Cambronero, S. et al., “Matrix tools for general observability analysis in traffic networks”, IEEE Transactions on Intelligent Transportation Systems, 11(4), pp.799-813, (2010).
[21].    Yim, P.K. and Lam, W.H. “Evaluation of count location selection methods for estimation of OD matrices”, Journal of transportation engineering, 124(4), pp.376-383, (1998).
[22].    Iqbal, M.S., Choudhury, C.F., Wang, P. et al., “Development of origin–destination matrices using mobile phone call data”, Transportation Research Part C: Emerging Technologies, 40, pp.63-74, (2014).
[23].    Wang, Y., Ma, X., Liu, Y., et al., “A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice”, PloS one, 11(1), p.e0146850, (2016).
[24].    Alexander, L., Jiang, S., Murga, M. et al., “Origin–destination trips by purpose and time of day inferred from mobile phone data”, Transportation research part c: emerging technologies, 58, pp.240-250 (2015).
[25].    Alsger, A.A., Mesbah, M., Ferreira, L. et al., “Use of smart card fare data to estimate public transport origin–destination matrix”, Transportation Research Record: Journal of the Transportation Research Board, (2535), pp.88-96, (2015).
[26].    Taha, H.A. Operations research: An introduction (for VTU). Pearson Education India, (2005).
[27].    Castillo, E., Grande, Z., Calviño, A., et al., “A state-of-the-art review of the sensor location, flow observability, estimation, and prediction problems in traffic networks”, Journal of Sensors, (2015).
[28].    Mahmoudabadi, A. and Seyedhosseini, S.M. “Solving Hazmat Routing Problem in chaotic damage severity network under emergency environment”, Transport policy, 36, pp.34-45, (2014).
Volume 28, Issue 4
Transactions on Industrial Engineering (E)
July and August 2021
Pages 2361-2373
  • Receive Date: 12 December 2018
  • Revise Date: 07 August 2019
  • Accept Date: 20 October 2019