Based on deep learning methods, especially long short-term memory (LSTM) neural networks, short-term traffic forecasting has achieved explosive growth. This study proposes the Bi-LSTM model to effectively predict travel time. In order to validate the effectiveness of the proposed stacked LSTM, we used 9-day toll station entry and exit data from the expressways of Guangdong province with an updating frequency of 5 min. The experimental result indicates that excessive depths of the model will lead to the increase of loss values. Moreover, the stability of data will affect the prediction accuracy. In addition, compared with other machine learning methods, as well as different topologies of neural networks, the stacked Bi-LSTM neural network has advantages of reliability, accuracy, and stability, which could facilitate travel time prediction.

Long short-term memory neural network; Travel time prediction; Toll station data