Challenges and opportunities in traffic flow prediction:  review of machine learning and deep learning perspectives

Syed Aleem Uddin Gilani; Murad Al-Rajab; Mahmoud Bakka

doi:10.56294/dm2024378

Original

Published: 2024-06-29

DOI: https://doi.org/10.56294/dm2024378

Challenges and opportunities in traffic flow prediction: review of machine learning and deep learning perspectives

Abstract

In recent days, traffic prediction has been essential for modern transportation networks. Smart cities rely on traffic management and prediction systems. This study utilizes state-of-the-art deep learning and machine learning techniques to adjust to changing traffic conditions. Modern DL models, such as LSTM and GRU, are examined here to see whether they may enhance prediction accuracy and provide valuable insights. Repairing problems and errors connected to weather requires hybrid models that integrate deep learning with machine learning. These models need top-notch training data to be precise, flexible, and able to generalize. Researchers are continuously exploring new approaches, such as hybrid models, deep learning, and machine learning, to discover traffic flow data patterns that span several places and time periods. Our current traffic flow estimates need improvement. Some expected benefits are fewer pollutants, higher-quality air, and more straightforward urban transportation. With machine learning and deep learning, this study aims to improve traffic management in urban areas. Long Short-Term Memory (LSTM) models may reliably forecast traffic patterns.

Keywords:

Smart cities,

intelligent transportation systems,

Machine Learning (ML),

Deep Learning (DL),

Long Short-Term Memory (LSTM),

How to Cite

Uddin Gilani SA, Al-Rajab M, Bakka M. Challenges and opportunities in traffic flow prediction: review of machine learning and deep learning perspectives. Data and Metadata [Internet]. 2024 Jun. 29 [cited 2024 Jul. 3];3:378. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/378

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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