As maintenance and operation costs increase with usage over time, equipment is replaced when the value of new equipment is more attractive. Some methods have been developed to solve this problem. In the public transport sector, such problems are frequently analyzed by fleet managers and determined by bus age restriction regulations. We propose an Integer Programming model that integrates both budgetary and environmental constraints (CO₂ emissions) which, as far as we know, have not previously been studied in conjunction. The study aims to determine the optimal replacement plan for a fleet of diesel buses of different size, age, maintenance costs and emissions rates, with new (less polluting) diesel buses over a time horizon of 50 years. The results indicate that it is possible to reduce emissions with a low annual budget using an optimal replacement policy.

This paper presents a multiobjective optimization model to find efficient bus fleet combinations taking into account greenhouse gas emissions, conventional air pollutant emissions and costs. The goal is to minimize, simultaneously, three objective functions, Z₁ (CO₂ emissions), Z₂ (Other Types of Emissions), and Z₃ (total costs), for a buses fleet of a transit agency. For this case, there were four types of buses (diesel, electric bus, electric bus of fast charging, and CNG (compressed natural gas bus), which were analyzed in three different lines (South-North, Itinga, and South-Central) in Joinville city, Brazil. The respective data were modeled and optimized using MS Excel. Two different scalarization methods (the Weighted Tchebycheff and the Augmented Weighted Tchebycheff) are used for solving this buses fleet management problem. Different tradeoffs, in terms of the objectives, were obtained. The choice of a bus type is directly related to the characteristics (number of stops, average speed among others) of each line. The electrical bus is the best choice for reducing emissions but has a high initial cost and low autonomy. The results indicate that in the South–North line due to the large number of stops and low average speed, the electric bus is the type of dominant. In the other lines, the dominant ones were the diesel bus in the Itinga line and the CNG bus in the South line.

This paper proposes the development of a smart traffic light prototype based on vehicular traffic flow measurement in the stretch between two avenues in the city of Limeira, SP, Brazil, focusing on the stretch towards UNICAMP’s School of Technology. To this end, we initially developed a Python code using the OpenCV library in order to detect and count vehicles. With the counting in operation, programming logic was inserted, aiming at preparing traffic light timers based on vehicular traffic. Finally, the traffic lights were added to display video via a code change to show the ongoing color changes, also obtaining a code for identifying vehicles and flow, in addition to the virtual traffic light system itself in the system. Vehicle counting accuracy was 75% for large vehicles, 90% for passenger cars, and 100% for motorcycles. The simulation of a smart traffic light implementation worked satisfactorily according to the post-processing of the video recorded for validation.

Efficient logistics and transport infrastructure are critical in contemporary urban and interurban scenarios due to their impact on economic development, environmental sustainability, and quality of life. This study explores the use of the Ant Colony Optimization (ACO) metaheuristic applied to the Vehicle Routing Problem (VRP) and the strategic positioning of service stations along major highways. Through a systematic mapping of the literature and practical application to a real-world scenario—specifically, a case study on the Bandeirantes Highway (SP348), connecting Limeira to São Paulo, Brazil—the effectiveness of ACO is demonstrated in addressing complex logistical challenges, including capacity constraints, route optimization, and resource allocation. The proposed method integrates graph theory principles, entropy concepts from information theory, and economic analyses into a unified computational model implemented using Python (version 3.12), showcasing its accessibility for educational and practical business contexts. The results highlight significant improvements in operational efficiency, cost reductions, and optimized service station placement, emphasizing the algorithm’s robustness and versatility. Ultimately, this research provides valuable insights for policymakers, engineers, and logistics managers seeking sustainable and cost-effective solutions in transport infrastructure planning and management.