Enhancing Urban Traffic Efficiency Through Traffic Flow Prediction using Long Short-Term Memory Neural Networks

Authors

  • Majd Ali Tishreen University

Abstract

Traffic congestion is a widespread issue affecting urban areas worldwide, leading to significant economic and environmental costs. Predicting traffic flow accurately is crucial for effective traffic management and planning. This study aims to develop a robust traffic flow prediction model that leverages the capabilities of Long Short-Term Memory (LSTM) neural networks in handling time series data. Suggested models were trained and tested on a comprehensive dataset, which included various traffic parameters provided by The Luxembourg administration of Ponts et Chaussées. The models achieved high accuracy in forecasting the average speed and flow rate in a studied location. So, the outputs can be used in an assistance system to help humane operators adjust traffic signal timings based on the predicted traffic conditions, reducing congestion and improving flow.

Downloads

Published

2024-08-04

How to Cite

1.
Ali M. Enhancing Urban Traffic Efficiency Through Traffic Flow Prediction using Long Short-Term Memory Neural Networks. Tuj-eng [Internet]. 2024Aug.4 [cited 2024Aug.12];46(3):41-50. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/17365

Issue

Vol. 46 No. 3 (2024): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

License

Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.