Traffic Modeling and Urban Mobility Analysis in the Transition Toward Smart Cities

The rapid evolution of urban environments and the growing demand for efficient transportation systems have accelerated the transition toward smart cities. In this context, traffic modeling and urban mobility analysis play a critical role in understanding, predicting, and optimizing complex transportation dynamics. This study explores contemporary approaches to traffic modeling, integrating data-driven methodologies, simulation techniques, and intelligent transportation systems to enhance urban mobility in Petrosani city from Romania. Emphasis is placed on the use of big data, Internet of Things (IoT) technologies, and machine learning algorithms for real-time traffic monitoring, demand forecasting, and adaptive traffic management. The paper examines the interaction between traditional modeling frameworks and emerging smart city infrastructures, highlighting how advanced analytics can improve congestion mitigation, reduce environmental impact, and support sustainable mobility solutions. Furthermore, it discusses multimodal transportation integration, user behavior analysis, and policy implications for urban planners and decision-makers. A conceptual framework is proposed to bridge the gap between theoretical models and practical implementations within smart city ecosystems. The findings suggest that the convergence of digital technologies and traffic modeling significantly enhances the resilience, efficiency, and sustainability of urban mobility systems. The study contributes to the ongoing discourse by identifying key challenges, opportunities, and future research directions in the development of intelligent, data-driven transportation networks.