%0 Journal Article
%A 陈新平
%A 管孟
%A 焦继超
%A 赵亚鑫
%T 	TCL: a taxi trajectory prediction model combining time and space features

%D 2021
%R 10.19682/j.cnki.1005-8885.2021.0010
%J 中国邮电高校学报（英文）
%P 63-75
%V 28
%N 3
%X <p style="text-align:justify;">
	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.
</p>
%U https://jcupt.bupt.edu.cn/CN/10.19682/j.cnki.1005-8885.2021.0010