Vehicular Traffic Flow Detection and Monitoring for Implementation of Smart Traffic Light: A Case Study for Road Intersection in Limeira, Brazil

• Published in: Future transportation Vol. 4; no. 4; pp. 1388 - 1401
• Main Authors: dos Santos Ximenes, Talía Simões, de Oliveira Silva, Antonio Carlos, de Martino, Guilherme Pieretti, Emiliano, William Machado, Menzori, Mauro, Meyer, Yuri Alexandre, Molina Júnior, Vitor Eduardo
• Format: Journal Article
• Language: English
• Published: Chicago MDPI AG 01.12.2024
• Subjects: Accuracy; Camcorders; Cameras; Computer vision; Control algorithms; Cost control; Energy consumption; Libraries; Light; Machine learning; monitoring; OpenCV library; OpenCV vehicle detection; Python; Roads & highways; Sensors; Smart cities; smart traffic light; smart traffic systems; Surveillance; Traffic congestion; Traffic control; Traffic flow; traffic flow management; Vehicles; Brazil; United States > US
• ISSN: 2673-7590; 2673-7590
• DOI: 10.3390/futuretransp4040067

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.