000			02444nam a22002417a 4500
003			ft8677
005			20250923104722.0
008			250923b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aTK7888 A24 2024
082			_a.
100	1		_aAbellar, Elijan Kathlene D.; Garcia, Kathrine Joyce C.; Jose, Aleeza M.; Vicera, Joshua V.
245			_aThe utilization of artificial neural networks (ANN) in developing an accident prediction model for Commonwealth Avenue
264		1	_aManila: _bPLM, _cJune 2024
300			_bUndergraduate Thesis: (Bachelor of Science in Civil Engineering) - Pamantasan ng Lungsod ng Maynila, 2024
336			_2text _atext _btext
337			_2 unmediated _a unmediated _b unmediated
338			_2 volume _a volume _b volume
505			_aABSTRACT: This study developed an accident prediction model for Commonwealth Avenue utilizing Artificial Neural Network (ANN). It aimed to determine the number of accidents in succeeding years for September 1, from Elliptical Road to Central Avenue; Segment 2, from Tandang Sora Avenue to Luzon Avenue; and Segment 3, from IBP Road (Sandigan) Meralco to IBP Road Litex Market. Various parameters were considered in the study, including the vehicle type, roadway characteristics, and environmental and time factors. The result showed that the most important road parameter was the vehicle type, with an importance value of 22.13%. It was followed by road width and weekdays with 18.26% and 15.61%, respectively. Meanwhile, the historical data was segmented into three portions 70% was allocated for training, while the remaining 30% was split equally between testing and validation. The correlation coefficients (R) were obtained for the three models, with values of 0.92001 for Segment 1, 0.92925 for Segment 2, and 0.94722 for Segment 3. The Neural Time Series App in MATLAB was employed to predict the number of accidents for 2024-2028. It indicated that the forecasted numbers of accidents in Segment 1 had a mix of fluctuations and relative stability. Segment 2, on the other hand, showed a tendency to slightly decrease in 2026 and slightly increase in subsequent years. While the third segment exhibited a more erratic pattern compared to the other segments, presenting mixed fluctuations and occasional spikes. It demonstrated the most variability in accident counts with less predictable trends.
526			_aF
655			_2.
942			_2lcc _cMS
999			_c36791 _d36791