Identification Of Truck Hotspots And Prediction Of Traffic State Based On GPS Data

With the increasing number of motor vehicles,traffic congestion on the road has become an urgent problem.Compared with other types of vehicles,freight cars are prone to form stop points in the driving process,and stay longer in some areas.These stop points will affect the surrounding traffic by the means of causing local regional congestion and leading to potential traffic accidents after diffusion.Therefore,based on the truck GPS trajectory data,this study extracts the truck stop point to identify the hot spot area of the truck.Mining the change laws of truck traffic in the hot area,judging the active level of truck in the area,and then realizing the prediction of truck traffic state in the hot area,providing the basis for road management departments to implement traffic control in and around the hot area,reducing the occurrence of regional traffic congestion and improving the efficiency of freight transportation.The main contents are as follows:(1)Aiming at the abnormal data in truck trajectory,the abnormal data cleansing rules are proposed.Firstly,the collection principle of truck GPS data is described.Secondly,the GPS data of freight cars in Beijing six ring area are screened.At last,according to five specific forms of truck GPS abnormal data,the corresponding data cleaning rules are proposed,and the flow chart of GPS abnormal data cleaning process is drawn.(2)The hot spot area recognition model of truck is constructed.Firstly,the freight car stop point extraction algorithm is proposed.Secondly,a hot spot area recognition model based on DBSCAN density clustering algorithm is built to identify the hot spot area of freight cars on working days and weekends.Then,the identification model of freight car hotspots based on kernel density estimation algorithm is built for identification and comparison.By using this identification model,the accuracy of the identified freight car hotspots is proved.(3)The concept of the degree of truck activity in hot spots is proposed,and the truck activity level is divided.Firstly,the concept of the degree of truck activity is proposed under the parameters of truck flow and average speed in hot spot area.Secondly,referring to the classification of road traffic congestion index in Beijing,the degree of truck activity is divided into five levels.Then,taking the area of Xinfadi and Airport Logistics Park as an example,the freight traffic flow rate and average speed in these two hot spots are statistically analyzed,and the degree of freight traffic activity and its activity level on a certain working day and weekend are visualized.(4)The prediction model of truck flow and speed in hot spot area based on GRU is constructed.Firstly,the weight parameters of GRU model are optimized by gradient descent algorithm Adam.Secondly,the improved PSO algorithm is used to iteratively optimize the hyper-parameters of the GRU model.With the improved PSO algorithm,a prediction model of freight traffic and speed in hot spots based on APSO-GRU is built.Then,by using the newly identified hot spots of freight cars,the prediction model is verified.The truck flow and average speed in the new area are predicted on working days and weekends,respectively,so as to judge the level of truck activity.Finally,the prediction results of APSO-GRU model are compared with those of GRU,SVR and ARIMA models,and its prediction accuracy is increased by 0.75%,2.44% and 4.05%,respectively in the prediction of freight traffic flow rate.In terms of truck speed prediction,the prediction accuracy is increased by 0.51%,2.31% and 3.83%,respectively.