An attention-based recurrent learning model for short-term travel time prediction

• Published in: PloS one Vol. 17; no. 12; p. e0278064
• Main Authors: Chughtai, Jawad-Ur-Rehman, Haq, Irfan Ul, Muneeb, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2022; Public Library of Science (PLoS)
• Subjects: Adaptation, Psychological; Biology and Life Sciences; Computer and Information Sciences; Engineering and Technology; Forecasts and trends; Intelligent vehicle-highway systems; Neural networks; Physical Sciences; Research and Analysis Methods; Technology application; Time Factors; Transportation; Travel; Travel time (Traffic engineering); Pakistan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0278064

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).