Prediction of Road Traffic Accidents based on Rolling-optimized Grey Markov Mode. Issue 1 (March 2020)

• Record Type: Journal Article
• Authors: Xu, Chengzhen; Zhang, Heng; Gong, Yanan; Sang, Huiyun; Sun, Guanglin; Chen, Jing
• Is Part Of: IOP conference series. Volume 792:Issue 1(2020)
• Publication Date: 2020-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/792/1/012012
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: In order to solve the problem that the accuracy of the accident prediction is affected by the time-effectiveness of the forecast data, the paper introduces the rolling optimization strategy based on the original data of the road traffic accident in the road section, and further establishes the rolling optimization-grey Markov dynamic prediction model. Using the Markov chain theory to explore the transition law between different states, the development trend of road traffic accidents volume is predicted, and the prediction accuracy of the random time series is further improved. Case analysis proves that the method has better prediction accuracy and practicability over a certain period of time, which can provide reference for road traffic accident prediction analysis and traffic safety early warning