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Title	Challenges And Opportunities In Traffic Flow Prediction: Review Of Machine Learning And Deep Learning Perspectives; [Desafíos Y Oportunidades En La Predicción Del Flujo De Tráfico: Revisión De Las Perspectivas De Aprendizaje Automático Y Aprendizaje Profundo]
ID_Doc	13624
Authors	Uddin Gilani S.A.; Al-Rajab M.; Bakka M.
Year	2024
Published	Data and Metadata, 3
DOI	http://dx.doi.org/10.56294/dm2024378
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. © 2024; Los autores.
Author Keywords	Deep Learning (DL); Intelligent Transportation Systems; Long Short-Term Memory (LSTM); Machine Learning (ML); Smart Cities

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