Improved Long Short-Term Memory-Based Periodic Traffic Volume Prediction Method

Abstract

In response to the problem of fixed time intervals for short-term traffic flow prediction, which fails to meet the requirements of traffic signal control based on traffic cycle signals, this paper proposes an improved long short-term memory-based method for periodic traffic volume prediction. The method presented in this study involves improvements to the Long Short-Term Memory (iLSTM) and Bidirectional Long Short-Term Memory (iBiLSTM) models, leading to the construction of the iBiLSTM-iLSTM-NN model. This model incorporates spatial data from surrounding intersections and employs data fitting techniques to establish the correlation between periodic queue length and traffic volume. Subsequently, a predictive model for periodic traffic volume is developed based on this correlation, enabling reliable forecasting of future traffic volumes within a given cycle. Additionally, actual intersection data is collected for simulation analysis. The results indicate that the prediction error of periodic traffic volume is influenced by different traffic flow characteristics such as peak, off-peak, and normal periods, as well as different inbound lanes. Different model parameters have a noticeable impact on the model’s performance, with smaller batch sizes leading to more stable models. By comparing the performance of different prediction models using various error evaluation metrics, this study finds that the proposed model exhibits the most stable performance. The research findings can be applied to rapidly predict future traffic volumes for several periods based on the instantaneous queue length at the end of the red signal phase, providing reliable, accurate, and timely data for urban traffic signal control.

Keywords

• Traffic flow prediction
• cycle queue length
• cycle traffic volume
• improved long short-term memory (iLSTM)
• improved bidirectional long short-term memory (iBiLSTM)
• deep learning