The simulation of routes and the optimization of urban traffic are crucial research areas for improving mobility in contemporary cities, particularly in Brazil. With population growth and a rapidly expanding vehicle fleet, urban areas face increasing challenges, such as traffic congestion, pollution, and extended travel times. This study centers on an initial traffic analysis using computational models to predict and optimize road and public transport flows. The methodology integrates real-time traffic data from public agencies and georeferenced information from bus fleets to analyze congestion patterns and vehicle behavior.
Various scenarios are examined, including modifications to road infrastructure, the use of intelligent traffic light systems, and the promotion of alternative transportation modes like bike lanes and enhanced public transport. These scenarios are tested to assess their potential impact on reducing congestion and improving public transit usage. Optimization tools like genetic algorithms and linear programming are employed to determine the most effective traffic management strategies.
Using simulation software as BEAM— Behavior, Energy, Autonomy, and Mobility, together with exact models such as the Traveling Salesman Problem (TSP) and the K-Rural Postman Problem (K-RPP) allow us to archive these goals. The preliminary results showed the critical parts of traffic and the pollution caused by it. With this data we can simulate changes in bus routes and schedules, and also make interventions. This way, we can increase traffic speed, reduce congestion, and improve the functioning of public transport. This study supports sustainable urban planning by providing a data-driven foundation for decision-making on urban mobility. By using real-world data from Brazil, we can test which models perform best in more complex environments like Pernambuco, where disorganized growth presents significant challenges for maintaining efficient transit flow.