Abstract
Session identification has attracted a lot of attention as it can play an important role in discovering useful patterns. A traffic session is a sequence of camera locations orderly passed by a vehicle to achieve a certain task. Based on the observations that both navigation regularity and temporal factor are crucial in determining the session boundaries, we propose an improved statistical language model which takes both factors into consideration in this paper. Extensive experiments are conducted on a real traffic dataset to testify the effectiveness of our proposal, and the result demonstrates its effectiveness compared to other alternative methods including the timeout method and the classic language model.
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References
Zheng, V., Zheng, Y., Xie, X., Yang, Q.: Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. Artificial Intelligence 184-185, 17–37 (2012)
Lee, W.H., Tseng, S.S., Shieh, W.Y.: Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system. Information Sciences 180(1), 62–70 (2010)
Torkkola, K., Zhang, K., Li, H., Zhang, H., Schreiner, C., Gardner, M.: Traffic Advisories Based on Route Prediction. In: Proceedings of Workshop on Mobile Interaction with the Real World, pp. 33–36 (2007)
Chen, L., Lv, M., Ye, Q., Chen, G., Woodward, J.: A personal route prediction system based on trajectory data mining. Information Sciences 181(7), 1264–1284 (2011)
Huang, X., Peng, F., An, A., Schuurmans, D.: Dynamic Web Log Session Identification With Statistical Language Models. Journal of the American Society for Information Science and Technology 55(14), 1290–1303 (2004)
Catledge, L., Pitkow, J.: Characterizing Browsing Strategies in the World-Wide Web. In: Proceedings of the 3rd International World Wide Web Conference, pp. 1065–1073 (1995)
He, D., Goker, A., Harper, D.J.: Combining evidence for automatic Web session identification. Information Processing and Management, 727–742 (2002)
Huang, X., Peng, F., An, A., Schuurmans, D., Cercone, N.J.: Session Boundary Detection for Association Rule Learning Using n-Gram Language Models. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 237–251. Springer, Heidelberg (2003)
Krumm, J.: A Markov Model for Driver Turn Prediction. In: Society of Automotive Engineers 2008 World Congress (2008) 2008-01-0195
Katz, S.: Estimation of Probability from Sparse Data for the Language Model Component of a Speech Recognizer. IEEE Transactions on Acoustics, Speech and Signal Processing 35(3), 400–401 (1987)
Wang, D., Cui, R.: Data Smoothing Technology Summary. Computer Knowledge and Technology 5(17), 4507–4509 (2009)
Bahl, L., Jelinek, F., Mercer, R.: A Maximum Likelihood Approach to Continuous Speech Recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 5(2), 179–190 (1983)
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Lou, X., Liu, Y., Yu, X. (2013). Traffic Session Identification Based on Statistical Language Model. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_24
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DOI: https://doi.org/10.1007/978-3-642-53917-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53916-9
Online ISBN: 978-3-642-53917-6
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