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Traffic Prediction Using Attentional Spatial-Temporal Deep Learning with Accident Embedding

Nowadays, an intelligent transportation system (ITS) is extremely important. High accuracy traffic prediction has been studied in large-scale networks. Deep learning methods are able to analyze the complex, multi-dimensional data to provide the least traffic prediction error. Both spatial and temporal dependencies provide significant implications for traffic prediction. Hence, we propose a combination of deep learning method architectures which consist of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to analyze spatial and temporal features and predict traffic speed in multiple steps. An accident has a major impact on local traffic congestion. We employ an autoencoder with a monotonic attention mechanism to learn accident embedding input to detect unexpected accidents and their effects. An experiment was conducted on two datasets of Highways England and Thai Expressways. Results showed that spatial and temporal features can solve problems at different locations in the traffic network using the deep learning method to improve traffic prediction performance. Furthermore, a monotonic attention mechanism using an autoencoder to learn how accidents impact on traffic congestion can minimize the prediction error.