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

  • Xiaoliang FanEmail author
  • Baoqin He
  • Patrick Brézillon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10257)


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.


Big data analytics Context awareness Crash-type analysis Visualization 



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).


  1. 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 CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 4.
    Hauer, E.: Speed and safety. Transp. Res. Rec. 2103, 10–17 (2009). doi: 10.3141/2103-02 CrossRefGoogle Scholar
  5. 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 CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Yu, R., Abdel-Aty, M.: Utilizing support vector machine in real-time crash risk evaluation. Accid. Anal. Prev. 51, 252–259 (2013)CrossRefGoogle Scholar
  8. 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 CrossRefGoogle Scholar
  9. 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 CrossRefGoogle Scholar
  10. 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 CrossRefGoogle Scholar
  11. 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 CrossRefGoogle Scholar
  12. 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. 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. 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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.Fujian Key Laboratory of Sensing and Computing for Smart CityXiamen UniversityXiamenChina
  3. 3.Information DepartmentXiamen AirlinesXiamenChina
  4. 4.LIP6, University Pierre and Marie CurieParisFrance

Personalised recommendations