Urban traffic congestion remains a persistent and growing challenge in cities worldwide,
driven by rapid urbanization and increasing vehicle ownership. Traditional traffic …

Traffic management is improved in cutting-edge smart cities using technologies such as
machine learning and deep learning to streamline daily tasks and boost productivity …

Traffic congestion is a growing problem worldwide and is worsening from the continuous
increase in urban population and thus the number of vehicles. Designing new roads to …

This research explores the increasing applications of deep learning in traffic management
systems, with a focus on traffic prediction, object detection and recognition, autonomous …

Abstract rapid urbanization and the growing population in smart cities pose significant
challenges to the management of urban traffic. In recent years, there has been an increasing …

Rapid population growth with increasing urban-centric activities have imposed a massive
demand on urban transportation systems—leading to increased mobility, reduced safety …

The rapid urbanization of cities worldwide has led to significant challenges in urban mobility,
including traffic congestion, increased emissions, and inefficient public transportation …

The transition to smart cities entails the integration of advanced technologies, data-driven
solutions, and innovative urban planning strategies. By harnessing the potential of …

Smart city development necessitates the implementation of effective traffic management
strategies. In this vein, various deep learning architectures, including VGG16Net …

The rise of smart cities has necessitated the development of advanced traffic management
systems that can adapt to dynamic urban traffic conditions. Traditional traffic signal control …