A Multidisciplinary Study on Smart Mobility Solutions

Abstract

Smart mobility represents a transformative approach to designing and managing transportation systems by integrating information and communication technologies, artificial intelligence, sustainable engineering, urban planning, and human-centered design. Traditional transportation models, characterized by static infrastructure and reactive management, struggle to address rapid urbanization, environmental pressures, and evolving user expectations. Smart mobility enables adaptive, data-driven, and interconnected transport ecosystems through real-time system awareness and predictive control. This multidisciplinary study examines smart mobility solutions at the intersection of technological, social, environmental, and economic dimensions. It integrates insights from intelligent transportation systems (ITS), IoT-based sensing, machine learning-driven decision systems, and sustainable mobility engineering. A conceptual framework is proposed for evaluating smart mobility architectures using distributed computing environments such as edge intelligence and cloud coordination. Key performance indicators (KPIs) are developed to measure congestion reduction, emission control, safety improvement, and user satisfaction. Simulation results indicate that learning-enabled mobility systems outperform traditional models in responsiveness, scalability, and sustainability. However, challenges related to interoperability, data governance, cybersecurity, and equitable access remain. Future research directions include federated learning, cross-layer optimization, and socio-technical policy integration.