A CNN-LSTM Model for Traffic Speed Prediction

Abstract

Increasingly serious traffic congestion requires an accurate and timely traffic speed prediction, which will significantly benefit both individual drivers and decision makers in travel planning and traffic management. However, traffic speed prediction is a long-standing and challenging topic. Due to the availability of traffic datasets and powerful computation resources, deep learning becomes a promising solution to this problem. In this paper, based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we propose a model named CLM, which is the first to make use of CNN to extract the features of daily and weekly periodicity of traffic speed at the target area and also extract the spatiotemporal features together with the output of CNN by LSTM layers. We conduct comprehensive simulations to assess the performance of our proposed method based on the real-world dataset of Hong Kong. The results indicate that our proposed CLM model can better predict traffic speed in different forecast time periods than the other five competing methods, including SVR, MLP, Lasso, Random forest, and LSTM.