Study On Freeway Abnormal Driving Behavior Forewarning Based On Fractal Multi-Step Forecast

Freeway due to the large traffic flow and the vehicle running fast,resulting in serious consequences of traffic accidents.In the event of traffic accidents,not only road damage,but also easily lead to traffic congestion,the second traffic accident and other phenomena,and even cause a wide range of traffic paralysis.Therefore,the urgent need for traffic management departments to take high-tech means to prevent traffic risks.The abnormal driving behavior of freeway vehicles is an important cause of traffic accidents,real-time monitoring of the running status of vehicles and early warning of abnormal driving behavior is an effective way to realize traffic intelligent and traffic safety.Therefore,it is of great theoretical and practical value for the freeway traffic safety management to discover,identify and predict the abnormal driving behavior in time,and then to warn the influence scope of the abnormal driving behavior,which is based on the multi-step prediction of micro-parameter multi-step prediction.Firstly,based on the chaos theory,the chaotic characteristics of the micro-velocity and acceleration sequence data of the vehicle is analyzed.It is proved that the micro-parameters of the vehicle are predictable.Then,based on R / S analysis,it is proved that the two parameters have fractal characteristics,and the prediction model of microscopic parameters is constructed based on fractal theory.Through the quantification of the index of speed and acceleration time series data,neural network is used to estimate the predictable number of steps,and the prediction model of micro parameter predictable step is obtained.The fractal multi-step prediction model is obtained by combining the fractal prediction model and the predictable step number estimation model,and the vehicle micro parameters are predicted in real time.Based on the fractal multi-step prediction model,the data of the vehicle in the future is predicted in real time.According to the actual operation data of the vehicle and the predicted data sequence,the freeway vehicles abnormal driving behavior including speeding,low speed driving,emergency braking,emergency stop,temporary parking and reversing retrograde are carried out by threshold judgment method and mechanical learning method respectively.This paper also based on the above types of abnormal driving behavior,according to the severity of which,the hierarchical feedback design and hierarchical alarm design are carried out,and the safety warning range of various abnormal driving behaviors is analyzed from the security angle.Finally,the article makes an example verification of the proposed methods.The results of an example show that the vehicle instantaneous velocity and acceleration can be predicted by using the fractal theory.Based on the predictive data,the abnormal driving behavior recognition model has a high detection rate,which can effectively detect some abnormal driving behavior in advance,and can give its early warning vehicle quickly through security warning model.