000			02819nam a2200301 i 4500
003			CSPC
005			20251202090521.0
008			251202s2024 ph a b 000 0 eng d
040			_aCSPC _beng _cCSPC _erda
090			_aUTH BSCS G249m 2024
100	1		_aGaspi, Jedele V. _eauthor.
245	1	0	_aMapping of Rinconada accident-prone zones using data mining / _cJedele V. Gaspi, Hazel A. Llorente, and Ian Dominic B. Sumpay
300			_a159 leaves.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
502			_aUndergraduate Thesis (BSCS) - Computer Science. _cCamarines Sur Polytechnic Colleges, 2024.
504			_aIncludes bibliographical references.
520	3		_aRoad accidents are a pressing global issue due to their devastating impact on lives, economies, and societies. It is the major public health concern due to their high fatality rate, economic losses, and lasting social impact. The goal of the study is to develop a pro-totype that can be utilized in identifying and visualizing accident-prone zones along the Rinconada highway. January 2019 to December 2023 historical accident data from Iriga City and Nabua was gathered and used in the study. The model employed the K-means clustering to group accident locations based on their geographical coordinates, identifying areas with a higher concentration of accidents. The analysis revealed that in Iriga City, San Roque (Pob.) was identified as the most accident-prone location. In Nabua, San An-tonio (Pob.) was identified as the most accident-prone location. Apriori algorithm was implemented to discover associations between different factors related to accidents, such as identifying if certain accident types are more common during specific times of day or locations. The result of the model reveals a strong association between young adults and accidents, particularly those involving motorcycles, and highlights the prevalence of acci-dents during afternoon and evening hours. The study identifies common accident types like sideswipes, rear-end collisions, and bad overtaking, analyzing their connection to time and location. A web-based mapping prototype is developed to visualize accident-prone areas and provide insights for targeted safety improvements. The findings offer valuable infor-mation for individuals to develop effective strategies for reducing accidents and enhancing road safety in Rinconada.
700	1		_aLlorente, Hazel A. _eauthor.
700	1		_aSumpay, Ian Dominic B. _eauthor.
720			_aFortuno, Kaela Marie N. _eadviser.
720			_aBenitez, Ian P. _echair, panelist.
720			_aOñate, Joseph Jessie S. _epanelist.
720			_aOnesa, Rosel O. _epanelist.
942			_2ddc _n0 _cUM _hG249m _iBSCS _kUTH _m2024
999			_c30979 _d30979