Title: Rapid analysis and detection algorithm and prevention countermeasures of urban traffic accidents under artificial intelligence

Authors: Zhao Yang; Yingjie Qi

Addresses: College of Traffic and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China; Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi, China ' College of Traffic and Transportation, Northeast Forestry University, Harbin 150040, Heilongjiang, China

Abstract: This article is a study on the rapid analysis and detection algorithm and preventive countermeasures of urban traffic accidents under the artificial intelligence threshold. First of all, it analyses the characteristics of artificial intelligence technology, and uses its flexibility, comprehensiveness and practicality to simulate a set of rapid analysis and detection models for accidents. Secondly, the closed line segment detection algorithm can identify multiple closed line segments in the image at the same time, and calculate the length and position of each closed line segment to accurately detect the vehicle path. Finally, the traffic accident black spot identification algorithm is used to calculate the initial fitness value of the accident spot, so as to reduce the frequency of accidents. The experimental conclusions show that the artificial intelligence-based urban traffic accident risk prediction model constructed in this paper can effectively predict the possible and potential accidents.

Keywords: artificial intelligence visual threshold; urban traffic accidents; particle filter lane detection; traffic accident black spots.

DOI: 10.1504/IJGUC.2021.119562

International Journal of Grid and Utility Computing, 2021 Vol.12 No.4, pp.431 - 439

Received: 07 Aug 2020
Accepted: 16 Sep 2020
Published online: 09 Dec 2021 *

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