The Research Of Destination Prediction Methods Based On Partial Initial Trajectory

With the popularity of mobile computing,there are a lot of trajectory data in different fields,and the research on trajectory is becoming more and more important,such as social network,intelligent city,position,movement trend,flow direction and so on.The existing prediction model is generally based on first-order,high-order Markov model,extended Markov model,dynamic Bayesian and other methods to predict.In the process of trajectory prediction,there is a problem that data collected by GPS devices cannot be used directly to match the map.In this paper,an improved weight-based road network matching algorithm is proposed to match the track points in the trajectory and compare with the existing methods.Experiments show the effectiveness of the proposed method.In addition,aiming at the problem of excessive use of road network matching,a popular driving route discovery algorithm is proposed,and the trajectory is further modeled.According to this method,the trajectory is transformed from the sequence of the road segment to the hot road sequence.The trajectory of the trajectory is improved compared with the original trajectory,which effectively improves the trajectory semantic information.Different from the traditional trajectory processing method,this paper predict the destination based on the convolution neural network(CNN)and the multi-layer perceptron(MLP),which is based on the deep learning technology.In the paper,the feature of trajectory will be extracted by using deep learning technology and general pattern of the trajectory also will be found.After a large-scale trajectory training,and the output of MLP is the prediction result.Compared with the traditional prediction model,the model in this paper get the better result,and also shows the reliability of the model.