Advances in intelligent transportation systems: ML and DL techniques for traffic congestion forecasting
TL;DR
A key contribution of this study lies in its comparative synthesis of ML and DL models, revealing that hybrid and graph-based DL architectures consistently outperform traditional ML methods when handling large-scale, heterogeneous traffic datasets.
Abstract
This study presents a comprehensive review of machine learning (ML) and deep learning (DL) techniques for traffic flow prediction, identifying key methodological trends, performance strengths, and existing research gaps. The review systematically examines ML approaches such as K-means LSTM, KNN, and SVM, highlighting their effectiveness in short-term forecasting and feature-driven prediction scenarios. In addition, advanced DL architectures including CNN–LSTM, GRGCAN, and TS-RNN are analysed for their ability to capture complex spatial–temporal dependencies in traffic data, demonstrating superior adaptability and predictive accuracy in dynamic traffic environments. A key contribution of this study lies in its comparative synthesis of ML and DL models, revealing that hybrid and graph-based DL architectures consistently outperform traditional ML methods when handling large-scale, heterogeneous traffic datasets. The review further identifies critical limitations in existing studies, particularly challenges related to real-time deployment, limited integration of external factors such as weather and traffic incidents, and data quality constraints. Based on these findings, the study highlights the importance of incorporating multimodal transport data and external contextual information to improve prediction robustness. Overall, this review provides actionable insights for researchers and practitioners, supporting the development of more reliable and adaptive intelligent transportation systems to reduce urban congestion and improve traffic management.