Abstract
Large number of taxicabs in major metropolitan cities are now equipped with a GPS device. Since taxis are on the road nearly twenty four hours a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this paper we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area. We adapt likelihood ratio tests (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in very accurate and rapid detection of anomalous behavior.
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References
Barlow, R.E., Scheuer, E.M.: Reliability Growth During A Development Testing Program. Technometrics (1966)
Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xie, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: 17th SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 1010–1018 (2011)
Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching trajectories by locations: an efficiency study. In: Proceedings of the 29th ACM SIGMOD International Conference on Management of Data (SIGMOD 2010), pp. 255–266 (2010)
Neill, D.B., Moore, A.W., Sabhnani, M., Daniel, K.: Detection of emerging space-time clusters. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD 2005), pp. 218–227 (2005)
Jung, I., Kulldorff, M., Klassen, A.C.: A spatial scan statistic for ordinal data. Stat Med, 1594–1607 (2007)
Jung, I., Kulldorff, M., Richard, O.J.: A spatial scan statistic for multinomial data. Stat Med, 1910–1918 (2010)
Yuan, J., Zheng, Y., Zhang, C.Y., Xie, W.L., Xie, X., Sun, G.Z., Huang, Y.: T-drive: Driving directions based on taxi trajectories. In: Proceedings of the 18th ACM SIGSPATIAL Conference on Advances in Geographical Information Systems, pp. 99–108
Kulldorff, M.: A spatial scan statistic. Comm. in stat. Theory and Methods, 1481–1496 (1997)
Kulldorff, M.: Spatial scan statistics: models, calculations, and applications. In: Glaz, J., Balakrishnan, N. (eds.) Scan Statistics and Applications. Birkhauser (1999)
Kulldorff, M., Nagarwalla, N.: Spatial disease clusters: detection and inference. Statistics in Medicine, 799–810 (1995)
Huang, L., Kulldorff, M., Gregorio, D.: A Spatial Scan Statistic for Survival Data. International Biometrics Society, 109–118 (2007)
Huang, L., Tiwari, R., Kulldorff, M., Zou, J., Feuer, E.: Weighted normal spatial scan statistic for heterogenous population data. American Statistical Association (2009)
Kulldorff, M., Athas, W., Feuer, E., Miller, B., Key, C.: Evaluating cluster alarms: a space-time scan statistic and cluster alarms in los alamos. American Journal of Public Health 88(9), 1377–1380 (1998)
Wu, M., Song, X., Jermaine, C., Ranka, S., Gums, J.: A LRT Framework for Fast Spatial Anomlay Detection. In: Proceedings of the 15th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 887–896 (2009)
Tango, T., Takahashi, K., Kohriyama, K.: A Space–Time Scan Statistic for Detecting Emerging Outbreaks. International Biometrics Society, 106–115 (2010)
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Pang, L.X., Chawla, S., Liu, W., Zheng, Y. (2011). On Mining Anomalous Patterns in Road Traffic Streams. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_18
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DOI: https://doi.org/10.1007/978-3-642-25856-5_18
Publisher Name: Springer, Berlin, Heidelberg
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