Traffic Management with Algorithm Analysis
Alvin F. Casupas, Jonard Klent C. Estrella, Jeff Lendelle T. Jacob, Hosea A. Paulate and James Matthew G. Vargas
- Manila; Technological University of the Philippines 2024.
- xii 174pages 29cm.
Bachelor's Thesis
College of Industrial Technology.--
Includes bibliographic references and index.
As urban traffic management becomes more complex and requires such solutions of congestion decreasing, environmental impacts and increasing road safety, innovative solutions are needed. The research of this thesis is presented through the development and implementation of a Traffic Management System with Algorithm Analysis to improve the urban mobility by optimizing the traffic flow. The system is real time traffic monitoring and analytics using YOLOv11n, advanced machine learning algorithms, that utilize Raspberry Pi based hardware. The most important features involve vehicle detection from live video feeds and a data visualization web platform for the user. The system was designed to be both scalable and integratable, that is, it was thought should work even when running alongside other infrastructure. This work helps towards technological advancement of infrastructure, towards sustainable and efficient urban transportation systems which are aspired by United Nations Sustainable Development Goals (SDGs) 9 and 11. The results of evaluation showed great traffic flow efficiency improvement, congestion reduction, and better data driven traffic management disposition for traffic management authorities. This thesis research shows feasibility to build more intelligent and more sustainable urban transport system with the help of Internet of Things (IoT) devices and machine learning. Keywords: Traffic Management System, Raspberry Pi, YOLOv11n, Machine Learning, Internet of Things, Urban Mobility, Real-Time Monitoring, Sustainable Development Goals, Smart Cities, Intelligent Transportation