Abstract
Accurate and reliable travel time prediction is important for promoting the development of urban public transportation, ensuring public travel safety, and establishing smart cities. In travel time prediction, studying spatiotemporal correlation features can help us better understand the dynamic changes and spatial dependencies in traffic data, and it can explain the patterns and trends in vehicle travel. Studying the external factors that influence vehicle travel can help us comprehensively consider the complexity of the transportation system, and incorporating these factors into prediction models can enhance the accuracy and robustness of the models. Therefore, this article proposes a novel deep spatiotemporal model for travel time prediction called DeepSTM-TTP. The architecture of this model consists of three parts: spatial-temporal convolution mechanism, external factor mechanism, and multitask learning mechanism. The spatiotemporal convolution mechanism is used to capture the spatiotemporal correlation of the trajectory external factor mechanism is used to handle the external information in the trajectory. A multitask learning mechanism achieves a balance between local path travel time prediction and whole path travel time prediction. The model fully considers the spatial-temporal correlation of the original GPS location sequence with external information. The experimental results on real datasets demonstrate that the model proposed in this article outperforms four well-known travel time prediction models, including a statistical model (HA), a machine learning model (GBDT), and two deep learning models (DeepTTE and DeepTTE-RNN).
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