BSI: A System for Predicting and Analyzing Accident Risk
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.
KeywordsBlack spots Spatial-temporal sequence Clustering
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