Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM)...Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM) was proposed, which is named trajectory-CNN-LSTM(TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution(T-CONV), multi-layer perceptron(MLP), and recurrent neural network(RNN) model, respectively.展开更多
基金supported by the National Key Research and Development Program of China (2017YFB0503700)the Fundamental Research Funds for the Central Universities (2019PTB-010)。
文摘Vehicle trajectory modeling is an important foundation for urban intelligent services. Trajectory prediction of cars is a hot topic. A model including convolutional neural network(CNN) and long short-term memory(LSTM) was proposed, which is named trajectory-CNN-LSTM(TCL). CNN can extract the spatial features of the trajectory in the input image. Besides, LSTM can extract the time-series features of the input trajectory. After that, the model uses fully connected layers to merge the two features for the final predicting. The experiments on the Porto dataset of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD) show that the average prediction error of TCL is reduced by 0.15 km, 0.42 km, and 0.39 km compared to the trajectory-convolution(T-CONV), multi-layer perceptron(MLP), and recurrent neural network(RNN) model, respectively.
