Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Tamás Tettamanti; Márton Tamás Horváth; István Varga

doi:10.2478/ttj-2014-0023

.blurhash-client-img { display: none !important; }

Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Transport and Telecommunication Journal

Volume 15 (2014): Issue 4 (December 2014)

By: Tamás Tettamanti, Márton Tamás Horváth and István Varga

Open Access

|Dec 2014

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.

References

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: 10.1080/15472450.2013.80684410.1080/15472450.2013.806844
Search in Google Scholar
2. Bachmann, C. (2011). Multi-Sensor Data Fusion for Traffic Speed and Travel Time Estimation, MSc Thesis, Toronto
Search in Google Scholar
3. Böker, G., Lunze, J. (2002). Stability and performance of switching Kalman filters, International Journal of Control, 75(16/17): 1269-128110.1080/0020717021000023708
Search in Google Scholar
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, IL10.1109/ALLERTON.2008.4797618
Search in Google Scholar
5. Chu, L., Oh, J., Recker, W. (2005). Adaptive Kalman filter based freeway travel time estimation, 84th TRB Annual Meeting, Washington
Search in Google Scholar
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.2010.06.00110.1016/j.inffus.2010.06.001
Search in Google Scholar
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:10.1080/03081060.2012.74572110.1080/03081060.2012.745721
Search in Google Scholar
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
Search in Google Scholar
9. Herrera, J. C., Bayen, A. M. (2007). Traffic flow reconstruction using mobile sensors and loop detector data, 87th TRB Annual Meeting
Search in Google Scholar
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
Search in Google Scholar
11. Küpper, A. (2005). Location-based Services. John Wiley & Sons, ISBN 978-0470-09231-6.10.1002/0470092335
Search in Google Scholar
12. Liberzon, D. (2003). Switching Systems and Control, Birkhäuser, Boston10.1007/978-1-4612-0017-8
Search in Google Scholar
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
Search in Google Scholar
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
Search in Google Scholar
15. Mitchell, H. B. (2007). Multi-sensor data fusion: An introduction, New York, Springer
Search in Google Scholar
16. Ng, G. W. (2003). Intelligent systems - fusion, tracking and control, Philadelphia: Research Studies Press Ltd.
Search in Google Scholar
17. Qing, O. (2011). Fusing Heterogeneous Traffic Data: Parsimonious Approaches using Data-Data Consistency, PhD Thesis, Delft University of Technology
Search in Google Scholar
18. Szeto, M. W. - Gazis D. C. (1972). Application of Kalman Filtering to the Surveillance and Control of Traffic Systems, Transportation Science, 6(4):419-439.10.1287/trsc.6.4.419
Search in Google Scholar
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):1-15.10.2478/ttj-2014-0023
Search in Google Scholar
20. Transport for London website: Developers’ Area http://www.tfl.gov.uk/businessandpartners/syndication/16492.aspx
Search in Google Scholar
21. Treiber, M., Helbing, D. (2002). Reconstructing the spatio-temporal traffic dynamics from stationary detector data, Cooperative Transportation Dynamics, 1(3):1-24.
Search in Google Scholar
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.2014.01.00510.1016/j.trc.2014.01.005
Search in Google Scholar
23. Wardrop, J. G. (1952). Some Theoretical Aspects of Road Traffic Research. Proceedings of the Institute of Civil Engineers, 1-2(9):325-378.
Search in Google Scholar
24. Welch, G., Bishop, G. (1995). An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill, TR95-041
Search in Google Scholar

Articles in this issue

DOI: https://doi.org/10.2478/ttj-2014-0023 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160

Journal RSS Feed

Language: English

Page range: 269 - 279

Published on: Dec 19, 2014

Published by: Transport and Telecommunication Institute

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Road Traffic Estimation,

Data fusion,

Switching Kalman Filter

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2014 Tamás Tettamanti, Márton Tamás Horváth, István Varga, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 15 (2014): Issue 4 (December 2014)