Exploring the Role of Deep Learning in Developing Intelligent Urban Mobility Solutions for Sustainable Cities

Abstract

The rapid urbanization of cities worldwide has led to significant challenges in urban mobility, including traffic congestion, increased emissions, and inefficient public transportation systems. Addressing these challenges is critical for developing sustainable urban environments. Deep learning offers promising solutions for enhancing urban mobility by enabling intelligent transportation systems, optimizing traffic management, and improving public transit operations. This paper explores the application of deep learning in developing intelligent urban mobility solutions, focusing on its role in traffic prediction, congestion management, public transportation optimization, and shared mobility services. We analyze various deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), and their integration with IoT data for real-time decision-making. Additionally, we discuss the challenges of implementing these technologies, including data quality, model interpretability, and scalability. By leveraging deep learning, cities can develop more efficient, resilient, and sustainable urban mobility systems that enhance the quality of life for their residents.