Speed Prediction And Satety Evaluation Method Of Expressway Traffic Flow Under Foggy Conditions

With the rapid development of China's social economy,the construction of expressway network has also been gradually improved.However,with expressway promoting the social economy development and bringing much convenience for people's travel,it can also lead to huge economic losses to the society due to the frequent occurrence of traffic accidents.Otherwise,the number of traffic accidents will increase significantly and the corresponding consequences are worse under foggy conditions.In order to strengthen the management of expressway and reduce the occurrence of traffic accidents,it is necessary to make safety evaluation for expressway.It is found that there is a close correlation between expressway traffic accidents and vehicle speed,and most traffic accidents are caused by unreasonable operation speed of vehicles.Therefore,it is of great significance to strengthen expressway traffic management under foggy conditions by studying safety evaluation of expressway based on speed indexes.First of all,based on the two aspects of speed prediction and expressway safety evaluation,this paper summarizes the both domestic and foreign research status at home and abroad and then proposes its research ideas.After pre-processing the original traffic data,this paper analyses the speed characteristics of expressway under foggy conditions and reveals regulation of vehicle speed under different visibility levels is also revealed.Secondly,a speed prediction model based on improved artificial bee colony algorithm to optimize the neural network is established.The model introduces the mutation operator in the differential evolution algorithm and the selection operator,crossover operator and mutation operator in the genetic algorithm to optimize the artificial bee colony algorithm,and adaptively changesmakes adaptive changes for each operator in order that the operator can get different values according to the variation of the population fitness value in the evolutionary process.Taking Beijing-Hong Kong-Macao expressway as an example,and with the speed,flow rate and weather factor being as input parameters of the prediction model.The improved prediction model is used to predict the speed of vehicles and its prediction result is compared with the corresponding one that of the wavelet neural network model and the wavelet neural network model.The results indicate that the prediction effect of the improved model is better than that of the traditional model's.Finally,the expressway safety evaluation system under foggy conditions isestablished based on four indicators,namely speed coordination,speed continuity,speed discreteness and weather conditions.The fuzzy comprehensive evaluation model is used to evaluate the safety of expressways.The entropy weight method is applied to calculate weight and the deficiency of entropy weight method is improved.The safety evaluation based on prediction speed is carried out on the Beijing-Hong Kong-Macao expressway section under foggy conditions,and the results of prediction evaluation and empirical evaluation results are compared.The results show that the accuracy of the expressway section safety prediction is relatively high.This method provides a reference for expressway management departments to predict and manage the expressway safety situation under foggy conditions in the short term.