Urban Traffic Condition Identification And Short-term Prediction Based On GPS Data Of Floating Car

With the rapid development of the global industry,the number of motor vehicles increased year by year,making the traffic congestion problem more and more serious.In order to alleviate the problem of traffic congestion,the aim is to provide accurate real-time traffic information for travelers,and a large number of researches focus on traffic conditions and short-term forecast.With the rapid development of GPS technology,the acquisition cost of GPS is reduced and the precision is high.Therefore,based on the floating car GPS information traffic identification and short-term prediction is a good research and application direction.Based on the GPS positioning of the floating vehicle,this paper estimates the average speed of the road section and provides the real-time traffic identification and forecast information to the traveler according to the traffic classification criteria.The research of this paper includes the acquisition of GPS data and the methods of pretreatment,cycling estimation,estimation of average speed of road sections,ACD algorithm,and proving the validity of the algorithm and the necessity of road segmentation according to the example results,and according to the characteristics of particle swarm algorithm The wavelet neural network is improved and used for short-term traffic prediction.The experimental results show that the improved wavelet neural network is better than the ordinary wavelet neural network based on particle swarm optimization.In this paper,the ACD algorithm of urban road conditions and the improved wavelet neural network based on the particle swarm optimization algorithm are proposed in combination with the driving characteristics of the floating vehicle.The proposed method greatly increases the accuracy of the traffic speed estimation and traffic condition,and the actual traffic data of Huangpu Road in Dalian City is collected.Through the example analysis,the experimental results show the effectiveness of the algorithm.Providing accurate real-time traffic information for travelers is of great significance to alleviate the traffic congestion problem.