Abstract
Fusing traffic data from a variety of traffic sensors into a coherent, consistent, and reliable picture of the prevailing traffic conditions (e.g. densities, speeds, flows) is a critical and challenging task in any off- or online traffic management or information system which use these data. Recursive Kalman filter-based approaches provide an intuitive and powerful solution for traffic state estimation and data fusion, however, in case the data cannot be straightforwardly aligned over space and time, the equations become unwieldy and computationally expensive. This chapter discusses three alternative data fusion approaches which solve this alignment problem and are tailored to fuse such semantically different traffic sensor data. The so-called PISCIT and FlowResTD methods both fuse spatial data (individual travel times and low-resolution floating car data, respectively) with a prior speed map obtained from either raw data or another estimation method. Both PISCIT and FlowResTD are robust to structural bias in those a priori speeds, which is critically important due to the fact that many real-world local sensors use (arithmetic) time averaging, which induces a significant bias. The extended and generalized Treiber–Helbing filter (EGTF) in turn is able to fuse multiple data sources, as long as for each of these it is possible to estimate under which traffic regime (congested, free flowing) the data were collected. The algorithms are designed such that they can be used in a cascaded setting, each fusing an increasingly accurate posterior speed map with new data, which in the end could be used as input for a model-based/Kalman filter approach for traffic state estimation and prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fosim (freeway operations simulation) (2008), http://www.fosim.nl/IndexUK.html
Papoulis, A.: Probability, Random Variables, and Stochastic Processes, 2nd edn., pp. 548–549. McGraw-Hill, New York (1984)
Dailey, D.J., Harn, P., Lin, P.J.: Its data fusion. Tech. Rep. Research Project T9903, Task 9, Washington State Transportation Commission, Department of Transportation and the U.S. Department of Transportation, Federal Highway Administration (1996)
Herrera, J.C., Bayen, A.M.: Traffic flow reconstruction using mobile sensors and loop detector data. In: Transportation Research Board 87th Annual Meeting, p. 18. Transportation Research Board (2008); 08-1868
Kerner, B.S.: The Physics of Traffic: Empirical Freeway Pattern Features, Engineering Applications, and Theory. Springer, Berlin (2004)
Kikuchi, S., Miljkovitc, D., van Zuylen, H.: Examination of methods that adjust observed traffic volumes on a network. Transport Research Record 1717, 109–119 (2000)
Knoop, V., Hoogendoorn, S., Zuylen, H.V.: Impact of data averaging methods on macroscopic traffic flow characteristics. In: Springer (ed.) Traffic and Granular Flow 2007. Springer, Heidelberg (2007) (to be published)
Leutzbach, W.: Introduction to the theory of traffic flow. Springer, Heidelberg (1987)
Lighthill, M., Whitham, G.: On kinematic waves ii: A theory of traffic flow on long crowded roads. Proc. R. Soc. A 229(1178), 317–345 (1955)
Linn, R.J., Hall, D.L.: A survey of multi-sensor data fusion systems. In: Proceedings of the SPIE - The International Society for Optical Engineering, Orlando, Florida, pp. 13–29 (1991)
Ni, D., Wang, H.: Trajectory reconstruction for travel time estimation. Journal of Intelligent Transportation Systems 12(3), 113–125 (2008)
Ou, Q., Van Lint, J., Hoogendoorn, S.: Piecewise inverse speed correction by using individual travel times. Transportation Research Record: Journal of the Transportation Research Board 2049(-1), 92–102 (2008); 10.3141/2049-11
Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Information Fusion 4(4), 259–280 (2003)
PTV: Vissim traffic microsimulation package (2005), http://www.ptv.de
Richards, P.: Shock waves on the highway. Operations Research 4, 42–51 (1956)
Simon, D.: Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. John Wiley & Sons, New York (2006)
Stipdonk, H., Toorenburg, J.v., Postema, M.: Phase diagram distortion from traffic parameter averaging. In: European Transport Conference (ETC), Leiden, The Netherlands (2008)
Treiber, M., Helbing, D.: Reconstructing the spatio-temporal traffic dynamics from stationary detector data. Cooper@tive Tr@nsport@tion Dyn@mics 1, 3.1–3.24 (2002)
Van Lint, J.W.C., Hoogendoorn, S.: A robust and efficient method for fusing heterogeneous data from traffic sensors on freeways. Computer-Aided Civil and Infrastructure Engineering 24, 1–17 (2009)
Van Lint, J., Hoogendoorn, S.P.: The technical and economic benefits of data fusion for real-time monitoring of freeway traffic. In: World Congress of Transportation Research. WCTRS, Berkely, CA, USA (2007)
Van Lint, J.W.C.: Reliable real-time framework for short-term freeway travel time prediction. Journal of transportation engineering-asce 132(12), 921–932 (2006)
Van Lint, J.W.C., Hoogendoorn, S.P., Hegyi, A.: Dual ekf state and parameter estimation in multi-class first-order traffic flow models. In: Proceedings of the 17th IFAC (International Federation of Automatic Control) World Congress, Seoul, Korea (2008)
Van Lint, J.W.C., Hoogendoorn, S.P., Van Zuylen, H.J.: Accurate travel time prediction with state-space neural networks under missing data. Transportation Research Part C: Emerging Technologies 13(5-6), 347–369 (2005)
Van Lint, J.W.C., Van der Zijpp, N.J.: Improving a travel time estimation algorithm by using dual loop detectors. Transportation Research Record 1855, 41–48 (2003)
Varshney, P.K.: Multisensor data fusion. Electronics and Communications Engineering Journal, 245–253 (December 1997)
Vermijs, R.G.M.M., Schuurman, H.: Evaluating capacity of freeway weaving sections and on-ramps using the microscopic simulation model fosim. In: Proceedings of the second international symposium on highway capacity, Sydney, Australia, vol. 2, pp. 651–670 (1994)
Wang, Y., Papageorgiou, M.: Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transportation Research Part B 39, 141–167 (2005)
Wang, Y., Papageorgiou, M., Messmer, A.: A real time freeway network traffic surveillance tool. IEEE Transactions on control systems technology 14(1) (2006)
Xiong, N., Svensson, P.: Multi-sensor management for information fusion: issues and approaches. Information Fusion 3(2), 163–186 (2002)
Yager, R.R.: A framework for multi-source data fusion. Information Sciences 163(1-3), 175–200 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ou, Q., van Lint, H., Hoogendoorn, S.P. (2010). Fusing Heterogeneous and Unreliable Data from Traffic Sensors. In: Babuška, R., Groen, F.C.A. (eds) Interactive Collaborative Information Systems. Studies in Computational Intelligence, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11688-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-11688-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11687-2
Online ISBN: 978-3-642-11688-9
eBook Packages: EngineeringEngineering (R0)