Abstract
In the field of Intelligent Transport Systems, there have been many attempts to predict the speed of the vehicle by adopting the pattern of the speed using artificial intelligence and statistical methods. Traffic information has been collected mainly by fixed devices which are costly and hard to maintain. Recently, traffic data is progressively being collected by the probe cars equipped with GPS receivers. Most of probe cars are comprised of public and commercial transportation such as taxis and buses since the private drivers are reluctant to give their location information due to the privacy issues. This creates problem of insufficient number of cars available and the traditional analysis methods used for analyzing the data collected by the fixed devices are not applicable. The aim of this research is to propose and test a new method of calculating the optimal link speed for the collected information from probe cars. We propose the adoption of a fuzzy c-mean method for this purpose. In this paper the GPS speed data are automatically classified into three groups of speed patterns such as low, middle, and high speed and the link speed is predicted from the pattern clusters. In performance tests, the proposed method provides significantly better results than normal average speed data.
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References
Hoppner, F.: A Contribution to Convergence Theory of Fuzzy c-Mean and Derivatives. IEEE Transactions of Fuzzy System, 11(5) (2003)
You, J., Kim, T.J.: Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C: Emerging Technologies 8(1-6), 231–256 (2000)
Coifman, B.: Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transportation Research: Part A 36(4), 351–364 (2002)
Dharia, A., Adeli, H.: Neural network model for rapid forecasting of freeway link travel time. Engineering Applications of Artificial Intelligence 16(7-8), 607–613 (2003)
Chan, K.S., Lam, W.H.K.: Optimal speed detector density for the network with travel time information. Transportation Research Part A: Policy and Practice 36(3), 203–223 (2002)
Wang, Y., Papageorgiou, M.: Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transportation Research Part B: Methodological 39(2), 141–167 (2005)
Hellinga, B., Fu, L.: Reducing bias in probe-based arterial link travel time estimates. Transportation Research Part C: Emerging Technologies 10(4), 257–273 (2002)
Goktepe, A.B., Altun, S., Sezer, A.: Soil clustering by fuzzy c-means algorithm. Advances in Engineering Software 36(10), 691–698 (2005)
Mingoti, S.A., Lima, J.: Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research. In Press, Corrected Proof (2005)
Choi, K.J., Chung, Y.S.: A Data Fusion Algorithm for Estimating Link Travel Time. Intelligent Transportation Systems Journal 7(3-4), 235–260 (2002)
Boyce, D., Rouphail, N., Kirson, A.: Estimation and measurement of link travel times in ADVANCE project. In: Proceedings of Vehicle Navigation and Information Systems Conference, pp. 62–66. IEEE, Los Alamitos (1993)
Sisiopiku, V., Rouphail, N.: Exploratory Analysis of the Correlations Between Arterial link Travel Times and Detector Data from Simulation and Field Studies. In: Advance Working Paper No. 25 (October 1993)
Sisiopiku, V., Palacharla, P., Nelson, P.: Fuzzy Reasoning Model for Converting Loop Detector Data into Travel Times. In: Advance Working Paper Series No. 38 (June 1994)
Nelson, P., Palacharla, P.: A neural network model for data fusion in advance. In: Proceedings of Pacific Rim Conference, Seattle, Wash, pp. 237–243 (1993)
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© 2006 Springer-Verlag Berlin Heidelberg
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Lee, SH., Lee, BW., Yang, YK. (2006). Estimation of Link Speed Using Pattern Classification of GPS Probe Car Data. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751588_52
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DOI: https://doi.org/10.1007/11751588_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34072-0
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