Traffic Accident Analysis And Prediction Based On Traffic Data

With China becoming the world's second largest economy,road transport infrastructure is increasingly perfect.At the same time,car ownership and the number of drivers are continuing to grow.Although the incidence of road traffic accidents in the new century has stabilized,the severity of the accident was worse than ever.It is expected that road traffic accidents will become the world's third largest cause of death in the near future.The problem of road traffic safety has received the wide attention from all walks of life.How to effectively prevent road traffic accidents has become a key issue related to the study of experts and scholars.Based on the most direct characterization of road traffic accidents,the traffic flow is taken as the center of this paper.The research contents and methods are as follows:(1)Firstly,the temporal and spatial correlation of traffic flow is studied.The Pearson correlation coefficient is used to analyze quantitatively and traffic flow similarity is measured by the distance.Combined with the analysis of the measured traffic flow data,it is proposed that the most relevant traffic flow can not be separated from the current time traffic flow by more than 3 time intervals and the selection of the most relevant road segment must be within the interval of 5 sections.The temporal and spatial characteristics of traffic flow are used to preprocess the data,and the validity of the method is validated in the empirical study.(2)Short-term traffic flow forecasting is an important link in real-time prediction of accidents.Based on the analysis of the traffic flow forecasting problem and the typical forecasting model,the double mutation operator is introduced to improve the PSO and further optimize the neural network prediction model.The RMSE of this forecasting model is only 2.17 in the 10 min forecast,and the speed is fast,which satisfies the real-time,accuracy and reliability requirements of short-term traffic flow forecasting.(3)The principle and process of the real-time prediction on road traffic accident are sorted out.Traffic status is divided into normal and dangerous,and the accident prediction is transformed into a classification problem.The Parzen window nonparametric estimation method is used to estimate the probability density function of the candidate traffic flow at different time scales and the appropriate characteristic variables and time scale are selected based on the separability criterion of probability distribution.The Adaboost accident predictor is constructed and combined with the future short-term traffic flow to predict the accident in real time,and good results are obtained in the empirical study.