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Optimal sensor location and origin–destination matrix observation with and without sensors on uncongested networks

Abstract

The Origin–Destination (O–D) matrix, is an important information in transportation planning and traffic control. Rapid changes in land use, particularly in developing countries, have been and are on an increase, which makes the estimation and observation of this matrix more significant. The objective of this paper is to observe O–D matrix under two scenarios. In the first scenario, it is assumed that the traffic network is equipped with path-ID sensors. In this situation, the goal is to determine the optimal number and location of these sensors in the network, where by applying collected information through these sensors, the O–D matrix is observed. Because path-ID sensors are not available in many cities, in the second scenario the interview alternative is proposed in order to observe O–D matrix. The interview method has encountered some restrictions. Several mathematical programming models have been developed to overcome these restrictions. To illustrate these proposed methodologies, they are applied in the Nguyen–Dupuis transportation network and the results are analysed. By applying the model on the intercity road network in the Province of Isfahan (Iran), a large network, the efficiency of these proposed models is demonstrated. Finally, some conclusions and final recommendations are included.


First published online 10 October 2019

Keyword : origin–destination matrix, observability problem, network sensor location problem, uncongested networks, path-ID sensors, Province of Isfahan

How to Cite
Karimi, H., Shetab-Boushehri, S.-N., & Zeinal Hamadani, A. (2020). Optimal sensor location and origin–destination matrix observation with and without sensors on uncongested networks. Transport, 35(3), 315-326. https://doi.org/10.3846/transport.2019.11247
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Jul 9, 2020
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References

Bauer, D.; Richter, G.; Asamer, J.; Heilmann, B.; Lenz, G.; Kölbl, R. 2018. Quasi-dynamic estimation of OD flows from traffic counts without prior OD matrix, IEEE Transactions on Intelligent Transportation Systems 19(6): 2025–2034. https://doi.org/10.1109/TITS.2017.2741528

Caceres, N.; Wideberg, J. P.; Benitez, F. G. 2008. Review of traffic data estimations extracted from cellular networks, IET Intelligent Transport Systems 2(3): 179–192. https://doi.org/10.1049/iet-its:20080003

Castillo, E.; Calviño, A.; Lo, H. K.; Menéndez, J. M.; Grande, Z. 2014. Non-planar hole-generated networks and link flow observability based on link counters, Transportation Research Part B: Methodological 68: 239–261. https://doi.org/10.1016/j.trb.2014.06.015

Castillo, E.; Cobo, A.; Jubete, F.; Pruneda, R. E.; Castillo, C. 2001. An orthogonally based pivoting transformation of matrices and some applications, SIAM Journal on Matrix Analysis and Applications 22(3): 666–681. https://doi.org/10.1137/S0895479898349720

Castillo, E.; Nogal, M.; Rivas, A.; Sánchez-Cambronero, S. 2013. Observability of traffic networks. Optimal location of counting and scanning devices, Transportmetrica B: Transport Dynamics 1(1): 68–102. https://doi.org/10.1080/21680566.2013.780987

Castillo, E.; Rivas, A.; Jiménez, P.; Menéndez, J. M. 2012. Observability in traffic networks. Plate scanning added by counting information, Transportation 39(6): 1301–1333. https://doi.org/10.1007/s11116-012-9390-0

Chootinan, P.; Chen, A.; Yang, H. 2005. A bi-objective traffic counting location problem for origin-destination trip table estimation, Transportmetrica 1(1): 65–80. https://doi.org/10.1080/18128600508685639

De Grange, L.; González, F.; Bekhor, S. 2017. Path flow and trip matrix estimation using link flow density, Networks and Spatial Economics 17(1): 173–195. https://doi.org/10.1007/s11067-016-9322-1

Földes, D.; Csiszár, C. 2015. Route plan evaluation method for personalised passenger information service, Transport 30(3): 273–285. https://doi.org/10.3846/16484142.2015.1086889

Gentili, M.; Mirchandani, P. B. 2012. Locating sensors on traffic networks: Models, challenges and research opportunities, Transportation Research Part C: Emerging Technologies 24: 227–255. https://doi.org/10.1016/j.trc.2012.01.004

He, S.-X. 2013. A graphical approach to identify sensor locations for link flow inference, Transportation Research Part B: Methodological 51: 65–76. https://doi.org/10.1016/j.trb.2013.02.006

Hu, S.-R.; Peeta, S.; Chu, C.-H. 2009. Identification of vehicle sensor locations for link-based network traffic applications, Transportation Research Part B: Methodological 43(8–9): 873-894. https://doi.org/10.1016/j.trb.2009.02.008

Kim, H.; Nam, D.; Suh, W.; Cheon, S. H. 2018. Origin-destination trip table estimation based on subarea network OD flow and vehicle trajectory data, Transportation Planning and Technology 41(3): 265–285. https://doi.org/10.1080/03081060.2018.1435437

Lee, R. J.; Sener, I. N.; Mullins, J. A. 2016. An evaluation of emerging data collection technologies for travel demand modeling: from research to practice, Transportation Letters: the International Journal of Transportation Research 8(4): 181–193. https://doi.org/10.1080/19427867.2015.1106787

Li, X.; Kurths, J.; Gao, C.; Zhang, J.; Wang, Z.; Zhang, Z. 2018. A hybrid algorithm for estimating origin-destination flows, IEEE Access 6: 677–687. https://doi.org/10.1109/ACCESS.2017.2774449

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 44(2): 282–298. https://doi.org/10.1016/j.trb.2009.07.008

Mitsakis, E.; Chrysohoou, E.; Salanova Grau, J. M.; Iordanopoulos, P.; Aifadopoulou, G. 2017. The sensor location problem: methodological approach and application, Transport 32(2): 113–119. https://doi.org/10.3846/16484142.2016.1258674

Ng, M. 2012. Synergistic sensor location for link flow inference without path enumeration: A node-based approach, Transportation Research Part B: Methodological 46(6): 781–788. https://doi.org/10.1016/j.trb.2012.02.001

Olia, A.; Abdelgawad, H.; Abdulhai, B.; Razavi, S. N. 2017. Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment, Journal of Intelligent Transportation Systems 21(4): 296–309. https://doi.org/10.1080/15472450.2017.1332524

Ortúzar, J. de D.; Willumsen, L. G. 2011. Trip distribution modelling, in J. de D. Ortúzar, L. G. Willumsen (Eds.). Modelling Transport, 175–206. https://doi.org/10.1002/9781119993308.ch5

Pravinvongvuth, S. 2007. Two Location Problems in Transportation. PhD Thesis. Utah State University, US. 220 p.

Rinaldi, M.; Viti, F. 2017. Exact and approximate route set generation for resilient partial observability in sensor location problems, Transportation Research Part B: Methodological 105: 86–119. https://doi.org/10.1016/j.trb.2017.08.007

Rosenthal, R. E. 2008. GAMS – a User’s Guide. GAMS Development Corporation, Washington, DC, US. 293 p.

Schrijver, A. 1998. Theory of Linear and Integer Programming. Wiley. 484 p.

Viti, F.; Cantelmo, G.; Corman, F.; Rinaldi, M. 2015. Improving the reliability of demand estimation using traffic counts by including information on link flow observability, in 6th International Symposium on Transportation Network Reliability (INSTR 2015), 2–3 August 2015, Nara, Japan. https://doi.org/10.3929/ethz-b-000183287

Xu, X.; Lo, H. K.; Chen, A.; Castillo, E. 2016. Robust network sensor location for complete link flow observability under uncertainty, Transportation Research Part B: Methodological 88: 1–20. https://doi.org/10.1016/j.trb.2016.03.006