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.

Over the past two decades, ecosystem services have gained recognition for their role in balancing ecosystems and human well-being. These encompass processes such as water purification, climate regulation, and pollination, which are often undervalued despite being critical for ecological balance and human health. Green infrastructure (GI) is especially recognized for its natural cooling effects, including shading and evapotranspiration, which help moderate local temperatures. This study investigates the role of GI in climate regulation in Campinas, Brazil, examining Land Use and Land Cover (LULC) changes and their impact on Land Surface Temperature (LST) using multi-temporal satellite data from 2018 to early 2024. Large-scale processing was conducted on a cloud-based geospatial platform (Google Earth Engine) to capture spatial–temporal patterns and improve analytical efficiency. Using remote sensing data, it evaluates vegetation indices and their correlation with LST across three zones: a densely urbanized area, a transitional agricultural zone, and a conservation forest. Findings indicate that areas near conservation forests exhibit significantly lower LST, whereas urban zones with reduced vegetation cover experience higher temperatures. The study demonstrates how GI effectively contributes to climate adaptation in tropical cities. This analysis identifies key spatial and temporal patterns that can inform sustainable urban planning. While noting limitations in spatial resolution, the study recommends integrating higher-resolution remote sensing and in situ data to enhance environmental analyses. These findings provide an evidence-based, replicable framework to guide urban planning and policy, promoting the protection and expansion of GI to mitigate urban heat and enhance climate resilience in tropical cities.

Neste artigo, tem-se como objetivo demonstrar a capacidade das ferramentas de geoprocessamento, incluindo imagens oriundas de diversos sistemas sensoriais, em contribuir para a caracterização, tanto sincrônica como diacrônica, de regiões que demandam regularização fundiária. Através da aplicação das ferramentas de geoprocessamento alinhado com produtos aerofotogramétricos na avaliação urbanística, busca-se demonstrar a presença e a progressão da ocupação informal periférica na cidade de Ribeirão Preto, no interior do estado de São Paulo. Este cenário é empregado como exemplo emblemático do fenômeno de favelização observado em cidades de porte médio no interior paulista.