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

This paper provides a comprehensive exploration of urban travel demand forecasting and its
implications for intelligent transportation systems, emphasizing the crucial role of intelligent …

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

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

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 …

Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …

Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel,
excessive air pollution, lost time, and reduced productivity. Understanding how humans …

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 …

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