Skip to main content
SpringerLink
Log in

Context-Aware Big Data Analytics and Visualization for City-Wide Traffic Accidents

  • Conference paper
  • First Online:
Modeling and Using Context (CONTEXT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

Various traffic big data has been emerging in cities, such as road networks, GPS trajectories of buses and taxicabs, traffic flows, accidents, etc. Based on the massive traffic accident data from January to December 2015 in Xiamen, China, we propose a novel accident 0analytics and visualization method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur consequentially by whom. First, we analyze and visualize accident occurrences and key features in both temporal and spatial view. Second, we propose our context-aware methodology. Finally, we illustrate spatio-temporal visualization results through two case studies. These findings would not only help traffic police department implement instant personnel assignments among simultaneous accidents, but also inform individual drivers about accident-prone sections and most dangerous time spans, which would require their most attention.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Weather forecasting website, http://lishi.tianqi.com/xiamen/index.html.

  2. 2.

    Tableau Desktop 8.3, www.tableau.com.

References

  1. Jagadish, H.V., Gehrke, J.: Labrinidis, Al., Papakonstantinou, Y., Patel, J., Ramakrishnan, R.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2004). doi:10.1145/2611567

    Article  Google Scholar 

  2. Ma, J., Kockelman, K.M., Damien, P.: A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accid. Anal. Prev. 40(3), 964–975 (2008)

    Article  Google Scholar 

  3. Lin, L., Wang, Q., Sadek, A.W.: A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction. Transp. Res. Part C 55, 444–459 (2015). doi:10.1016/j.trc.2015.03.015

    Article  Google Scholar 

  4. Hauer, E.: Speed and safety. Transp. Res. Rec. 2103, 10–17 (2009). doi:10.3141/2103-02

    Article  Google Scholar 

  5. Zhang, G., Yau, K.W., Zhang, X.: Analyzing fault and severity in pedestrian–motor vehicle accidents in China. Accid. Anal. Prev. 73, 141–150 (2014). doi:10.1016/j.aap.2014.08.018

    Article  Google Scholar 

  6. Lord, D., Geedipally, S.: Investigating the effect of modeling single-vehicle and multi-vehicle crashes separately on confidence intervals of Poisson–Gamma models. Accid. Anal. Prev. 42(4), 1273–1282 (2010)

    Article  Google Scholar 

  7. Yu, R., Abdel-Aty, M.: Utilizing support vector machine in real-time crash risk evaluation. Accid. Anal. Prev. 51, 252–259 (2013)

    Article  Google Scholar 

  8. Qin, X., Ivan, J., Ravishanker, N., Liu, J., Tepas, D.: Bayesian estimation of hourly exposure functions by crash type and time of day. Accid. Anal. Prev. 38(6), 1071–1080 (2006). doi:10.1016/j.aap.2006.04.012

    Article  Google Scholar 

  9. Yu, R., Abdel-Aty, M.A., Ahmed, M.M., Wang, X.: Utilizing microscopic traffic and weather data to analyze real-time crash patterns in the context of active traffic management. IEEE Trans. Intell. Transp. Syst. 15(1), 205–213 (2014). doi:10.1109/TITS.2013.2276089

    Article  Google Scholar 

  10. Zhang, G., Yau, K.W., Zhang, X.: Risk factors associated with traffic violations and accident severity in China. Accid. Anal. Prev. 59, 18–25 (2013). doi:10.1016/j.aap.2013.05.004

    Article  Google Scholar 

  11. Wang, Z., Lu, M., Yuan, X., Zhang, J., de Wetering, H.: Visual traffic jam analysis based on trajectory data. IEEE Trans. Vis. Comput. Graph. 19(12), 2159–2168 (2013). doi:10.1109/TVCG.2013.228

    Article  Google Scholar 

  12. Pack, M., Wongsuphasawat, K., VanDaniker, M., Filippova, D.: Ice–visual analytics for transportation incident datasets. In: Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 200–205 (2009)

    Google Scholar 

  13. Piringer, H., Buchetics, M., Benedik, R.: AlVis: situation awareness in the surveillance of road tunnels. In: Proceedings of IEEE VAST, pp. 153–162 (2012)

    Google Scholar 

  14. Park, H., Haghani, A.: Real-time prediction of secondary incident occurrences using vehicle probe data. Transportation Research Part C: Emerging Technologies (in press)

    Google Scholar 

Download references

Acknowledgments

The work was supported by grants from the National Natural Science Foundation of China (61300232); the Gansu Provincial Science and Technology Support Program (1504WKCA087); the China Postdoc Foundation (2015M580564); and Fundamental Research Funds for the Central Universities (lzujbky-2015-100, lzujbky-2016-br04).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Xiaoliang Fan

  2. Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen, China

    Xiaoliang Fan

  3. Information Department, Xiamen Airlines, Xiamen, China

    Baoqin He

  4. LIP6, University Pierre and Marie Curie, Paris, France

    Patrick Brézillon

Authors
  1. Xiaoliang Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baoqin He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Brézillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoliang Fan .

Editor information

Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fan, X., He, B., Brézillon, P. (2017). Context-Aware Big Data Analytics and Visualization for City-Wide Traffic Accidents. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57837-8_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation