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Smart city article details

Title	A Traffic Flow Prediction Framework Based On Deep Learning And Particle Swarm Optimization
ID_Doc	5609
Authors	Qin L.; Xueping Z.
Year	2023
Published	2023 IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2023
DOI	http://dx.doi.org/10.1109/ICSECE58870.2023.10263506
Abstract	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. © 2023 IEEE.
Author Keywords	Deep Learning; LSTM; Particle Swarm Optimization; Smart City; Traffic Flow Prediction

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