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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2018: Advances in Knowledge Discovery and Data Mining pp 471–482Cite as

Traffic Accident Detection with Spatiotemporal Impact Measurement

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10938)

Abstract

Traffic incidents continue to cause a significant loss in deaths, injuries, and property damages. Reported traffic accident data contains a considerable amount of human errors, hindering the studies on traffic accidents. Several approaches have been developed to detect accidents using traffic data in real time. However, those approaches do not consider the spatiotemporal patterns inherent in traffic data, resulting in high false alarm rates. In this paper, we study the problem of traffic accident detection by considering multiple traffic speed time series collected from road network sensors. To capture the spatiotemporal impact of traffic accidents to upstream locations, we adopt Impact Interval Grouping (IIG), which compares real-time traffic speed with historical data, and generates impact intervals to determine the presence of accidents. Furthermore, we take a multivariate time series classification approach and extract three novel features to measure the severity of traffic accidents. We use real-world traffic speed and accident datasets in our empirical evaluation, and our solutions outperform state-of-the-art approaches in multivariate time series classification.

Keywords

  • Traffic accident
  • Multivariate time series classification

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References

  1. National Safety Council: NSC motor vehicle fatality estimates (2016). http://www.nsc.org/NewsDocuments/2016/mv-fatality-report-1215.pdf. Accessed 13 Feb 2017

  2. Yue, M., Fan, L., Shahabi, C.: Inferring traffic incident start time with loop sensor data. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 2481–2484. ACM (2016)

    Google Scholar 

  3. Payne, H., Helfenbein, E., Knobel, H.: Development and testing of incident detection algorithms, volume 2: research methodology and detailed results. Technical report, FHWA (1976)

    Google Scholar 

  4. Payne, H., Tignor, S.: Freeway incident-detection algorithms based on decision trees with states. Technical report, National Research Council (1978)

    Google Scholar 

  5. Stephanedes, Y.J., Chassiakos, A.P.: Freeway incident detection through filtering. Transp. Res. C: Emerg. Technol. 1(3), 219–233 (1993)

    CrossRef  Google Scholar 

  6. Zhu, T., Wang, J., Lv, W.: Outlier mining based automatic incident detection on urban arterial road. In: Proceedings of the 6th International Conference on Mobile Technology, Application & Systems (29), September 2009

    Google Scholar 

  7. Yuan, F., Cheu, R.: Incident detection using support vector machines. Transp. Res. Part C: Emerg. Technol. 11(3–4), 309–328 (2003)

    CrossRef  Google Scholar 

  8. Sadek, S., Al-Hamadi, A., Michaelis, B., Sayed, U.: A statistical framework for real-time traffic accident recognition. J. Sig. Inf. Process. 1(1), 77–81 (2010)

    Google Scholar 

  9. Parkany, E., Xie, C.: A complete review of incident detection algorithm & their deployment: what works and what doesn’t. Technical report, New England Transportation Consortium (2005)

    Google Scholar 

  10. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)

    CrossRef  Google Scholar 

  11. Bashir, M., Kempf, J.: Reduced dynamic time warping for handwriting recognition based on multidimensional time series of a novel pen device. Int. J. Intell. Syst. Technol. WASET 3(4), 194 (2008)

    Google Scholar 

  12. Ten Holt, G.A., Reinders, M.J., Hendriks, E.: Multi-dimensional dynamic time warping for gesture recognition. In: Thirteenth Annual Conference of the Advanced School for Computing and Imaging, vol. 300 (2007)

    Google Scholar 

  13. Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Trans. Knowl. Data Eng. 17(9), 1186–1198 (2005)

    CrossRef  Google Scholar 

  14. Wang, X., Wirth, A., Wang, L.: Structure-based statistical features and multivariate time series clustering. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 351–360. IEEE (2007)

    Google Scholar 

  15. Fulcher, B.D., Jones, N.S.: Highly comparative feature-based time-series classification. IEEE Trans. Knowl. Data Eng. 26(12), 3026–3037 (2014)

    CrossRef  Google Scholar 

  16. Pan, B., Demiryurek, U., Shahabi, C., Gupta, C.: Forecasting spatiotemporal impact of traffic incidents on road networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 587–596. IEEE (2013)

    Google Scholar 

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Acknowledgement

This research has been funded in part by NSF grants CNS-1461963, Caltrans-65A0533, the USC Integrated Media Systems Center (IMSC), the USC METRANS Transportation Center, and a UAlbany FRAP-A award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any of the sponsors such as NSF.

Author information

Authors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Mingxuan Yue & Cyrus Shahabi

  2. University at Albany SUNY, Albany, NY, USA

    Liyue Fan

Authors
  1. Mingxuan Yue
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  2. Liyue Fan
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  3. Cyrus Shahabi
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Corresponding author

Correspondence to Mingxuan Yue .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Prof. Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Yue, M., Fan, L., Shahabi, C. (2018). Traffic Accident Detection with Spatiotemporal Impact Measurement. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_37

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  • DOI: https://doi.org/10.1007/978-3-319-93037-4_37

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  • Publisher Name: Springer, Cham

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