Abstract
This paper presents a dynamic model that can provide prediction for the estimated arrival time of a bus at a given bus stop using Global Positioning System (GPS) data. The proposed model is a hybrid intelligent system combining Fuzzy Logic and Neural Networks. While Neural Networks are good at recognizing patterns and predicting, they are not good at explaining how they decide their input parameters. Fuzzy Logic systems, on the other hand, can reason with imprecise information, but require linguistic rules to explain their fuzzy outputs. Thus combining both helps in countering each other’s limitations and a reliable and effective prediction system can be developed. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions. The accuracy of result is 86.293% obtained
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References
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME Journal of Basic Engineering 82(D), 35–45 (1960)
Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)
Beale, R., Jackson, T.: Neural Computing: An Introduction. Hilger, Philadelphia (1991)
Krishnapuram, R., Keller, J.M.: The possibilistic C-means algorithm: insights and recommendations. IEEE Transactions on Fuzzy Systems 4(3), 385–393 (1996)
Al-Deek, H., D’Angelo, M., Wang, M.: Travel Time Prediction with Non-Linear Time Series. In: Proceedings of the ASCE 1998 5th International Conference on Applications of Advanced Technologies in Transportation, pp. 317–324 (1998)
Chien, S.I.J., Ding, Y., Wei, C.: Dynamic Bus Arrival Time Prediction with Artificial Neural Networks. Journal of Transportation Engineering 128(5) (2002)
Williams, B., Hoel, L.: Modeling and Forescating Vehicle Traffic Flow as a Seasonal Arima Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering 129(6), 664–672 (2003)
Jeong, R.H.: The Prediction of Bus Arrival time Using Automatic Vehicle Location Systems Data. A Ph.D. Dissertation at Texas A and M University (2004)
Chen, M., Liu, X.B., Xia, J.X.: A Dynamic Bus Arrival Time Prediction model based on APC data. Computer Aided Civil and Infrastructure, Engineering, 364–376 (2004)
Shalaby, A., Farhan, A.: Bus travel time prediction for dynamic operations control and passenger information systems. In: 82nd Annual Meeting of the Transportation Research Board. National Research Council, Washington D.C (2004)
Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A Possibilistic Fuzzy c-Means Clustering Algorithm. IEEE Transactions of Fuzzy Systems 13(4), August 2005
Bin, Y., Zhong-Zhen, Y., Baozhen, Y.: Bus Arrival Time Prediction using Support Vector Machines. Journal of Intelligent Transportation Systems 10(4), 151–158 (2006)
Ramakrishna, Y., Ramakrishna, P., Sivanandan, R.: Bus Travel Time Prediction Using GPS Data. In: proceedings of Map India, New Delhi (2006)
Saad, M.F., Alimi, A.M.: Modified Fuzzy Possibilistic C-means. In: IMECS 2009, Hong Kong, March 18–20, 2009
Thomas, T., Weijermars, W.A.M., Van Berkum, E.C.: Predictions of Urban Volumes in Single Time Series. IEEE Transactiuons on Intelligent Transportation Systems 11(1), 71–80 (2010)
Exponential Smoothing, October 17, 2014. http://en.wikipedia.org/wiki/Exponential_smoothing
Dublin Dataset, October 17, 2014. http://dublinked.com/datastore/datasets/dataset-304.php
BPNN, October 17, 2014. “http://wwwold.ece.utep.edu/research/webfuzzy/docs/kk-thesis/kk-thesis-html/node22.html
Levenberg-Marquardt algorithm, October 17, 2014. http://en.wikipedia.org/wiki/Levenberg-Marquardt_algorithm
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Khetarpaul, S., Gupta, S.K., Malhotra, S., Subramaniam, L.V. (2015). Bus Arrival Time Prediction Using a Modified Amalgamation of Fuzzy Clustering and Neural Network on Spatio-Temporal Data. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_12
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DOI: https://doi.org/10.1007/978-3-319-19548-3_12
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