Application of deep learning models for traffic flow prediction based on time-series data
Faculty of Information Technology, Sao Do University, HaiDuong, Vietnam.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 655-661.
Article DOI: 10.30574/wjaets.2024.13.1.0467
Publication history:
Received on 17 August 2024; revised on 26 September 2024; accepted on 29 September 2024
Abstract:
This paper investigates and applies the Long Short-Term Memory (LSTM) deep learning model for traffic flow prediction based on time-series data. The model is trained and tested with a large dataset comprising 26,497 records of vehicle counts at a specific observation point over four months. To evaluate the performance of the LSTM model, we conduct experiments and compare it with other popular machine learning methods. The results demonstrate that the LSTM deep learning model achieves an accuracy of 88.91%, outperforming traditional machine learning techniques. These results promise to support traffic flow prediction and provide reliable data for managers, helping them make accurate decisions in traffic coordination, thereby reducing congestion and enhancing the efficiency of urban traffic systems.
Keywords:
Traffic flow prediction; LSTM model; Machine Learning; Time-series Data
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0