<![CDATA[The rapid growth of urban populations has intensified the pressure on transportation infrastructure, leading to challenges such as traffic congestion, increased travel time, pollution, and reduced overall mobility. To address these issues, the use of simulation and modelling has emerged as a powerful approach in understanding and optimizing urban transportation systems. This study investigates how various simulation techniques—such as discrete-event simulation, agent-based modelling, and system dynamics—can be applied to analyze traffic patterns, test policy interventions, and predict system behavior under different scenarios. By integrating real-time data and historical trends, simulation models provide a virtual environment for assessing the impact of traffic management strategies, including signal optimization, public transit prioritization, road pricing, and multi-modal integration. The research presents case studies and comparative analyses that highlight the effectiveness of simulation tools in enhancing decision-making processes for urban planners and policymakers. The findings suggest that strategic use of modelling can reduce congestion, improve efficiency, and support sustainable urban mobility. Furthermore, the study emphasizes the importance of interdisciplinary collaboration and the integration of smart technologies to build more resilient and adaptive transport systems. In conclusion, simulation and modelling play a pivotal role in shaping the future of urban transportation in an increasingly complex and data-driven world.]]>
