Integrating Machine Learning and Deep Learning in Smart Cities for Enhanced Traffic Congestion Management: An Empirical Review

Abstract

Citation: A. B. Feroz Khan and P. Ivan, "Integrating machine learning and deep learning in smart cities for enhanced traffic congestion management: An empirical review," Abstract: In the rapid urbanization experienced globally, traffic congestion emerges as a critical challenge, detrimentally affecting economic performance and the quality of urban life. This study delves into the deployment of machine learning (ML) and deep learning (DL) methodologies for mitigating traffic congestion within smart city frameworks. An extensive literature review coupled with empirical analysis is conducted to scrutinize the application of these advanced technologies in various transportation domains, including but not limited to traffic flow prediction, optimization of routing, adaptive control of traffic signals, dynamic management of traffic systems, implementation of smart parking solutions, enhancement of public transportation systems, anomaly detection, and seamless integration with the Internet of Things (IoT) and sensor networks. The research methodology encompasses a detailed outline of data sources, the selection of ML and DL models, along with the processes of training and evaluation. Findings from the experiments underscore the efficacy of these technological interventions in real-world settings, highlighting notable advancements in the precision of traffic predictions, the efficiency of route optimization, and the responsiveness of adaptive traffic signal controls. Moreover, the study elucidates the pivotal role of ML and DL in facilitating dynamic traffic management, anomaly detection, smart parking, and the optimization of public transportation. Through illustrative case studies and examples from cities that have embraced these technologies, practical insights into their applicability and the consequential impact on urban mobility are provided. The research also addresses challenges encountered, offering a discourse on potential avenues for future research to further refine traffic congestion management strategies in smart cities. This contribution significantly enriches the existing corpus of knowledge, presenting pragmatic solutions for urban planners and policy makers to foster more efficient and sustainable transportation infrastructures.