Abstract
Big data analytics for traffic accidents is a hot topic and has significant values for a smart and safe traffic in the city. Based on the massive traffic accident data from October 2014 to March 2015 in Xiamen, China, we propose a novel accident occurrences analytics method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur consequentially by whom. Firstly, we analyze and visualize accident occurrences in both temporal and spatial view. Second, we illustrate spatio-temporal visualization results through two case studies in multiple road segments, and the impact of weather on crash types. These findings of accident occurrences analysis and visualization would not only help traffic police department implement instant personnel assignments among simultaneous accidents, but also inform individual drivers about accident-prone sections and the time span which requires their most attention.
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Notes
- 1.
Weather forecasting website, http://lishi.tianqi.com/xiamen/index.html.
- 2.
Tableau Desktop 8.3, www.tableau.com.
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Acknowledgments
This work is supported by the grants from Natural Science Foundation of China (No. 61300232, No. 61300042); Ministry of Education of China “Chunhui Plan” Cooperation and Research Project (No. Z2012114, Z2014141); Funds of State Key Laboratory for Novel Software Technology, Nanjing University (KFKT2014B09); Fundamental Research Funds for the Central Universities (lzujbky-2015-100); and China Telecom Corp. Gansu Branch Cuiying Funds (lzudxcy-2014-6). The authors acknowledge Xiamen Intelligent Transport Control Center (ITCC) for providing the data.
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Fan, X. et al. (2015). Big Data Analytics and Visualization with Spatio-Temporal Correlations for Traffic Accidents. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_18
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DOI: https://doi.org/10.1007/978-3-319-27122-4_18
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