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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)

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.

Keywords

Big data analytics Context awareness Crash-type analysis Visualization 

Notes

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).

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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

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