A real-time explainable traffic collision inference framework based on probabilistic graph theory

Millions of motor vehicle collisions occur each year and lots of them result in heavy fatalities. Although some promising works are proposed, they have the following problems: (1) most of existing methods depend on feature regression, but ignore the causal relationship among them; (2) the vision-based techniques cost enormous resources to process the large scale of video data; (3) the lack of considering real-time traffic environment leads to an unsatisfied performance. To tackle these problems, we propose a real-time explainable collision inference framework through social media analysis. First, we design and extract various kinds of real-time traffic features from the social media. Then, we propose an effective algorithm to discover the causal relationships among the adopted features, which are denoted by probabilistic graphs. Finally, we employ the probabilistic graphs with the top-k BDeu score to calculate the probability of one collision occurring with nearly linear time complexity. Extensive experiments show that our framework achieves 0.752, 0.747, and 0.750 in precision, recall, and F1-measure. Extensive results show that our proposal has good scalability and has a good chance to solve other emergency event inference. (C) 2020 Elsevier B.V. All rights reserved.