Short-Term Traffic Speed Prediction of Urban Road With Multi-Source Data

Abstract

In recent years, the convolutional and recurrent neural networks are widely applied in traffic prediction tasks. Traffic speed prediction is an important and challenging topic in intelligent transportation systems. In this case, this paper proposes a hybrid deep learning structure for short-term traffic speed prediction, which combines convolutional neural networks and long short-term memory neural networks together. External factors such as weather condition and air quality can also affect the driving behavior of travelers and cause fluctuation of traffic speed. Based on theories in traffic engineering, we propose a data-fusion method to measure the impact of environmental factors. To enhance the performance of our model, we introduced attention mechanism to our model. With convolutional block attention module, our network could emphasize important channels and pixels of input features and suppress unnecessary ones. Comparing with several deep learning methods and hybrid deep learning structures, an experiment in one region of Suzhou which contains 909 links shows the outperformance of our model. Under different time steps, the prediction error of our model is lower than any other methods in urban expressway, primary-arterial, secondary-arterial, and branch-road. The results indicate that the spatial dependencies, the temporal correlations, and environmental impact should not be ignored in traffic speed prediction tasks.