Traffic accident prediction based on Markov chain cloud model
Xiaobin Ma, Zhang JinYang, Peijie Huang, Huiyun Sang, Guanglin Sun, Jing Chen · IOP Conference Series Earth and Environmental Science · 2020
Abstract A dynamic prediction model based on Markov chain and cloud model is established to predict the volume of road traffic accidents which is under the guidance of stochastic process and cloud theory referring to the characters of the road traffic accidents and time series data. First of all, we establish the evaluation model based on cloud model, and get the relative error range of the observed value and the predicted value. Then we can use Markov chain to correct the relative error. The result shows that this method balances both randomicity and fuzziness and has higher accuracy. Therefore, it can be used for analysing the trend of road traffic accidents in different traffic conditions and provide evidence for safety early warning and corresponding accident prevention countermeasures formulation in relative road segments.