Rapid analysis and detection algorithm and prevention countermeasures of urban traffic accidents under artificial intelligence. (2nd December 2021)

• Record Type: Journal Article
• Authors: Yang, Zhao; Qi, Yingjie
• Is Part Of: International journal of grid and utility computing. Volume 12:Number 4(2021)
• Page Start: 431
• Page End: 439
• Publication Date: 2021-12-02
• Subjects: artificial intelligence visual threshold -- urban traffic accidents -- particle filter lane detection -- traffic accident black spots; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.