000			02509nam a22002417a 4500
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005			20250923101022.0
008			250923b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aTK7886 B36 2024
082			_a.
100	1		_aBamo, Mark Christian U.; Ocampo, Anne Dominique P.; Dolores, Hannah Krisielle R.; Piangco, Marcjustin Red C.
245			_aDeveloping quantitative model for evaluating the impact of roadside friction factors on traffic capacity of roads in Divisoria, City of Manila
264		1	_aManila: _bPLM, _cMay 2024
300			_bUndergraduate Thesis: (Bachelor of Science in Civil Engineering) - Pamantasan ng Lungsod ng Maynila, 2024
336			_2text _atext _btext
337			_2unmediated _aunmediated _bunmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: In the Philippines, roadside friction are prevalent even on busy roads – pedestrians, parked vehicles, market stalls and carts, and unloading vehicles occupy portions of roads designed to be utilized by flowing vehicles; hence, it’s crucial to quantitatively evaluate how these roadside friction factors interact with traffic flow and contribute to heavy traffic. This study delves into analyzing roadside friction impact on traffic capacity. Roadside friction and traffic data were gathered from four roads – Claro M. Recto Avenue, Juan Luna, Reina Regente, and Ylaya Streets. The traffic capacity of roads and the impact of roadside friction factors on it were analyzed using multiple regression analysis. For all roads, the p-values obtained for dynamic friction are less than 0.05 – indicating that dynamic friction has significant impact on road traffic capacity, while static friction has significant impact only in one road. Subsequently, utilizing a four -phase modeling process involving validation and cross-validation through mean absolute percentage error (MAPE), a quantitative model was developed to determine the impact of roadside friction factors to traffic capacity of roads in Divisoria, City of Manila, J-Model was selected to represent the quantitative model as it has an adjusted r-squared of 0.87307, a MAPE of 9.6%, a highly accurate prediction on three roads, and a good prediction on one road. Utilizing the same model, a quantitative indicator through percent reduction in traffic capacity was established, which describes the quantity of impact of roadside friction factors in the locale.
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