BSI: A System for Predicting and Analyzing Accident Risk

  • Xinyu MaEmail author
  • Yuhao Yang
  • Meng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


In recent years, the rapid growth of motor vehicle ownership brings great pressure to the road traffic system and inevitably leads to a large number of traffic accidents. Therefore, it is a demanding task to build a well-developed system to identify the high-risk links, i.e., black spots, of a road network. However, most of the existing works focus on identifying black spots in a road network simply based on the statistic data of accidents, which leads to low accuracy. In this demonstration, we present a novel system called BSI, to predict and analyze the high-risk links in a road network by adequately utilizing the spatial-temporal features of accidents. First, BSI predicts the trend of accidents by a spatial-temporal sequence model. Then, based on predicted results, K-means method is utilized to discover the roads with the highest accident severity. Finally, BSI identifies the central location and coverage of a high-risk link by a modified DBSCAN clustering model. BSI can visualize the final identified black spots and provide the results to the user.


Black spots Spatial-temporal sequence Clustering 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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