000			02916nam a22003257a 4500
003			OSt
005			20250327165829.0
008			250327b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bC37 2024
100			_aCasupas, Alvin F. _eauthor
245			_aTraffic Management with Algorithm Analysis _cAlvin F. Casupas, Jonard Klent C. Estrella, Jeff Lendelle T. Jacob, Hosea A. Paulate and James Matthew G. Vargas
260			_aManila; _bTechnological University of the Philippines _c2024.
300			_axii 174pages _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's Thesis
502			_aCollege of Industrial Technology.-- _bBachelor of Engineering Technology major in Computer Engineering Technology (Non-Stem) _cTechnological University of the Philippines _d2024.
504			_aIncludes bibliographic references and index.
520			_aAs 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
650			_2Computer Engineering Technology
650			_2Traffic Management System
650			_2Raspberry Pi
700			_aEstrella, Jonard Klent C. _eauthor
700			_aJacob, Jeff Lendelle T. _eauthor
700			_aPaulate, Hosea A. _eauthor
700			_aVargas, James Matthew G. _eauthor
942			_2lcc _cBTH CIT _n0
999			_c29599 _d29599