Spatial-temporal attention-based seq2seq framework for short-term travel time prediction

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Short-term travel time prediction is an important research direction of the intelligent transportation system. However, due to the complex temporal and spatial correlation of the traffic data, the more accurate and efficient the prediction of the short-term travel time, the harder it is to achieve. In order to conquer the problems and accelerate the construction of smart cities, this study first proposes the seq2seq model based on LSTM network with strong time series processing capability and can avoid manual feature extraction. Then, a seq2seq model based on multi-level attention mechanism is proposed. Finally, the method proposed is evaluated and compared with the traditional prediction methods through experimental simulations based on the real traffic dataset. The results verify the accuracy and effectiveness of the proposed models, demonstrate that the utilisation of multi-level attention mechanism can effectively integrate temporal and spatial features of the dataset, and improve the efficiency of the original seq2seq model.