Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Tamás Tettamanti 1 , Márton Tamás Horváth 1  und István Varga 1
  • 1 Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek J. u. 2., H-1111 Budapest, Hungary

Abstract

The knowledge of road traffic parameters is of crucial importance to ensure state-of-the-art traffic services either in public or private transport. In our days, a plethora of road traffic data are continuously collected producing historical and real-time traffic information as well. The available information, however, arrive from inhomogeneous sensor systems. Therefore, a data fusion methodology is proposed based on Switching Kalman Filter. The concept enables efficient travel time estimation for urban road traffic network. On the other hand, the method may contribute to a better macroscopic traffic modelling.

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  • 1. Anand, A., Ramadurai, G. and Vanajakshi, L. (2013). Data Fusion Based Traffic Density Estimation and Prediction, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, accepted author version, DOI: 10.1080/15472450.2013.806844

  • 2. Bachmann, C. (2011). Multi-Sensor Data Fusion for Traffic Speed and Travel Time Estimation, MSc Thesis, Toronto

  • 3. Böker, G., Lunze, J. (2002). Stability and performance of switching Kalman filters, International Journal of Control, 75(16/17): 1269-1281

  • 4. Claudel, C. G., Bayen, A. M. (2008). Guaranteed bounds for traffic flow parameters estimation using mixed lagrangian-eulerian sensing. Proceedings of the 46th Annual Allerton Conference on Communication, Control, and Computing, Allerton, IL

  • 5. Chu, L., Oh, J., Recker, W. (2005). Adaptive Kalman filter based freeway travel time estimation, 84th TRB Annual Meeting, Washington

  • 6. El Faouzi, N-E., Leung, H., Kurian, A. (2011). Data fusion in intelligent transportation systems: Progress and challenges - A survey, Information Fusion, 12(1): 4-10 DOI: 10.1016/j.inffus.2010.06.001

  • 7. Guo, F., Krishnan R. and Polak, J. (2013). A computationally efficient two-stage method for short-term traffic prediction on urban roads, Transportation Planning and Technology, 36(1): 62-75, DOI:10.1080/03081060.2012.745721

  • 8. Gühnemann, A., Schäfer, R. P. Thiessenhusen, K. U., Wagner, P. (2004). Monitoring Traffic and Emissions by Floating Car Data, ITS Working Paper ITS-WP-04-07, Institute of Transport Studies, University of Sydney

  • 9. Herrera, J. C., Bayen, A. M. (2007). Traffic flow reconstruction using mobile sensors and loop detector data, 87th TRB Annual Meeting

  • 10. Klein, L. A., Mills, M. K. (2006). Gibson, D. R. P.: Traffic Detector Handbook:Third Edition, Federal Highway Administration Turner-Fairbank Highway Research Center

  • 11. Küpper, A. (2005). Location-based Services. John Wiley & Sons, ISBN 978-0470-09231-6.

  • 12. Liberzon, D. (2003). Switching Systems and Control, Birkhäuser, Boston

  • 13. Ludvig, Á., Tettamanti, T., Varga, I. (2012). Travel time estimation in urban road traffic networks based on radio signaling data. In: 14th International Conference on Modern Information Technology in the Innovation Processes of Industrial Enterprises, MITIP, Budapest,2012.10.24-2012.10.26. pp. 514-527. ISBN: 978-963-311-373-8

  • 14. Nokia Solutions and Networks OY, Vékony, N., Tettamanti, T, Varga, I., Ludvig, Á. (2014) Determining travel information (patent), Invention Publication No. WO/2014/023339012E00798 HU, http://patentscope.wipo.int/search/en/WO2014023339

  • 15. Mitchell, H. B. (2007). Multi-sensor data fusion: An introduction, New York, Springer

  • 16. Ng, G. W. (2003). Intelligent systems - fusion, tracking and control, Philadelphia: Research Studies Press Ltd.

  • 17. Qing, O. (2011). Fusing Heterogeneous Traffic Data: Parsimonious Approaches using Data-Data Consistency, PhD Thesis, Delft University of Technology

  • 18. Szeto, M. W. - Gazis D. C. (1972). Application of Kalman Filtering to the Surveillance and Control of Traffic Systems, Transportation Science, 6(4):419-439.

  • 19. Tettamanti T., Varga I. (2014). Mobile phone location area based traffic flow estimation in urban road traffic, Columbia International Publishing, Advances in Civil and Environmental Engineering, 1(1):1-15.

  • 20. Transport for London website: Developers’ Area http://www.tfl.gov.uk/businessandpartners/syndication/16492.aspx

  • 21. Treiber, M., Helbing, D. (2002). Reconstructing the spatio-temporal traffic dynamics from stationary detector data, Cooperative Transportation Dynamics, 1(3):1-24.

  • 22. Vlahogianni, E., Karlaftis, M. G., Golias, J. C. (2014). Short-term traffic forecasting: Where we are and where we're going, Transportation Research Part C (2014), in press, DOI: 10.1016/j.trc.2014.01.005

  • 23. Wardrop, J. G. (1952). Some Theoretical Aspects of Road Traffic Research. Proceedings of the Institute of Civil Engineers, 1-2(9):325-378.

  • 24. Welch, G., Bishop, G. (1995). An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, TR95-041

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