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Estimation of an Urban OD Matrix Using Different Information Sources

  • Asma Sbaï
  • Henk J. van Zuylen
  • Jie LiEmail author
  • Fangfang Zheng
  • Fattehallah Ghadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)

Abstract

An Origin Destination matrix for urban trips is more difficult to develop than for interurban long and medium distance trips. The socio-economic characteristics are valuable parameters to estimate trip attractions and destinations, but often the distance does not have a significant effect on the distribution of urban trips. Since the 1980s methods are developed to estimate the trip matrix from traffic volumes. The problem is underdetermined: the information in the OD matrix is more than the information contained in the traffic volumes. Nowadays there are more information sources like probe vehicles, Automated Number Plate Recognition cameras, mobile phone data etc. This article discusses the possibilities and limitations of these additional information sources. Use is made of traffic data collected in Changsha, a town in middle-south China.

Keywords

Origin Destination matrix Probe vehicles Information minimization Urban traffic 

References

  1. 1.
    Hensher, D.A., Button, K.J.: Handbook of Transport Modelling. Pergamon, Oxford (2000)Google Scholar
  2. 2.
    Van Zuylen, H.J.: Some improvements in the estimation of an OD matrix from traffic counts. In: The 8th Internaional Symposium on Transportation and Traffic Theory, Toronto (1981)Google Scholar
  3. 3.
    Van Zuylen, H.J., Willumsen, L.G.: The most likely trip matrix estimated from traffic counts. Transp. Res. 14(3), 281–293 (1980)CrossRefGoogle Scholar
  4. 4.
    Li, J., van Zuylen, H.J., Wei, G.: Loop detector data error diagnosing and interpolating with probe vehicle data. Transp. Res. Rec. J. Transp. Res. Board (2014)Google Scholar
  5. 5.
    Li, J., van Zuylen, H.J., Liu, C., Lu, S.: Monitoring travel times in an urban network using video, GPS and Bluetooth. In: 14th Meeting of the Euro Working Group on Transportation, Poznan (2011). http://www.sciencedirect.com/science/article/pii/S1877042811014509
  6. 6.
    Jones, P. M., Koppelman, F.S., Orfeuil, J.P.: Activity analysis: state of the art and future directions. In: Developments in Dynamic and Activity-Based Approaches to Travel Analysis, pp. 34–55 (1990)Google Scholar
  7. 7.
    Ma, Y., Kuik, R., van Zuylen, H.J.: Day-to-day origin destination tuple estimation and prediction with hierarchical Bayesian networks using multiple data sources. Transp. Res. Rec. J. Transp. Res. Board 2342(1), 51–61 (2013)CrossRefGoogle Scholar
  8. 8.
    Maher, M.J.: Inferences on trip matrices from observations on link volumes: a Bayesian statistical approach. Transp. Res. Part B Methodol. 17(6), 435–447 (1983)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Tebaldi, C., West, M.: Bayesian inference on network traffic using link count data. J. Am. Stat. Assoc. 93(442), 557–573 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Li, B.: Bayesian inference for origin-destination matrices of transport networks 1210 using the EM algorithm. Technometrics 47(4), 399–408 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Cascetta, E.: Estimation of trip matrices from traffic counts and survey data: a generalized least squares estimator. Transp. Res. 18B, 289–299 (1984)CrossRefGoogle Scholar
  12. 12.
    Toledo, T., Kolechkina, T.: Estimation of dynamic origin–destination matrices using linear assignment matrix approximations. IEEE Trans. Intell. Transp. Syst. 14(2), 618–626 (2013)CrossRefGoogle Scholar
  13. 13.
    Spiess, H.A.: Maximum likelihood model for estimating origin-destination matrices. Transp. Res. 21B(5), 395–412 (1987)CrossRefGoogle Scholar
  14. 14.
    Constantinos, A., Balakrishna, R., Koutsopoulos, H.N.: A synthesis of emerging data collection technologies and their impact on traffic management applications. Eur. Transp. Res. Rev. 3(3), 139–148 (2011)CrossRefGoogle Scholar
  15. 15.
    Bachmann, C., Abdulhai, B., Roorda, M.J., Moshiri, B.: A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transp. Res. Part C Emerg. Technol. 26, 33–48 (2013)CrossRefGoogle Scholar
  16. 16.
    Byon, Y., Shalaby, A., Abdulhai, B., Elshafiey, S.: Traffic data fusion using SCAAT Kalman filters. In: Transportation Research Board 89th Annual Meeting Compendium of Papers DVD, Washington, DC (2010)Google Scholar
  17. 17.
    Ma, Y., van Zuylen, H., Kuik, R.: Freight origin-destination estimation based on multiple data source. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1239–1244. IEEE (2012)Google Scholar
  18. 18.
    Ma, Y.: The use of advanced transportation monitoring data for official statistics. Thesis ERIM Series Research in Management, Rotterdam (2016)Google Scholar
  19. 19.
    Morimura, T., Kato, S.: Statistical origin-destination generation with multiple sources. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3443–3446. IEEE, November 2012Google Scholar
  20. 20.
    Iqbal, M.S., Choudhury, C.F., Wang, P., González, M.C.: Development of origin–destination matrices using mobile phone call data. Transp. Res. Part C Emerg. Technol. 40, 63–74 (2014)CrossRefGoogle Scholar
  21. 21.
    Bugeda, B., Montero Mercadé, J., Marqués, L., Carmona, C.: A Kalman-filter approach for dynamic OD estimation in corridors based on Bluetooth and Wi-Fi data collection. In: 12th World Conference on Transportation Research, WCTR (2010)Google Scholar
  22. 22.
    Kostic, B., Gentile, G.: Using traffic data of various types in the estimation of dynamic OD matrices. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 66–73. IEEE, June 2015Google Scholar
  23. 23.
    Willumsen, L.G.: Estimating time-dependent trip matrices from traffic counts. In: Volmuller, J., Hamerslag, R. (eds.) Proceedings of the Ninth International Symposium on Transportation and Traffic Theory. VNU Science Press, Utrecht (1984)Google Scholar
  24. 24.
    Dunlap, M., et al.: Estimation of origin and destination information from Bluetooth and Wi-Fi sensing for transit. Transp. Res. Rec. J. Transp. Res. Board 2595, 11–17 (2016)CrossRefGoogle Scholar
  25. 25.
    Antoniou, C., Barceló, J., Breen, M., Bullejos, M., Casas, J., Cipriani, E., Montero, L.: Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: design, demonstration and validation. Transp. Res. Part C Emerg. Technol. 66, 79–98 (2016)CrossRefGoogle Scholar
  26. 26.
    Astaria, V.: A continuous time link model for dynamic network loading based on travel time function. In: Lesort, J.-B. (ed.) Transportation and Traffic Flow Theory, pp. 79–102. Pergamon, Oxford (1996)Google Scholar
  27. 27.
    Hadavi, M., Shafahi, Y.: Vehicle identification sensor models for origin–destination estimation. Transp. Res. Part B Methodol. 89, 82–106 (2016)CrossRefGoogle Scholar
  28. 28.
    Kikuchi, S., Miljkovic, D., van Zuylen, H.J.: Examination of methods that adjust observed traffic volumes on a network. Transportation Research Board Meeting, TRB Paper Number 00-1378 (2000)Google Scholar
  29. 29.
    Ye, P., Wen, D.: Optimal traffic sensor location for origin-destination estimation using a compressed sensing framework. IEEE Trans. Intell. Transp. Syst. (2016)Google Scholar
  30. 30.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Asma Sbaï
    • 1
  • Henk J. van Zuylen
    • 2
    • 3
    • 4
  • Jie Li
    • 4
    Email author
  • Fangfang Zheng
    • 3
  • Fattehallah Ghadi
    • 1
  1. 1.Faculty of ScienceIbn Zohr UniversityAgadirMorocco
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.South West Jiaotong UniversityChengduChina
  4. 4.Hunan UniversityChangshaChina

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