A Traffic Flow Prediction Framework Based on Deep Learning and Particle Swarm Optimization

In this study, a deep learning and particle swarm optimization-based technique for predicting traffic flow is proposed. First, the time-series features of traffic flow are captured using the Long Short-Term Memory (LSTM) model. The LSTM model’s parameters are then optimised using the Particle Swarm Optimisation (PSO) technique in an effort to improve forecast accuracy. We validate our approach using actual traffic flow data from the public dataset - "Caltrans". Experimental findings show that our suggested strategy outperforms other widely used techniques in terms of prediction accuracy, including ARIMA, SVM, and Decision Tree, especially performing excellently on two critical indicators: average speed and traffic volume. Furthermore, through meticulous experiment design and result analysis, we reveal the superiority of deep learning and particle swarm optimization in traffic flow prediction. These findings indicate that our method is practical and effective for actual traffic flow prediction tasks.