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Mapping of Rinconada accident-prone zones using data mining / Jedele V. Gaspi, Hazel A. Llorente, and Ian Dominic B. Sumpay

By:

Gaspi, Jedele V [author.]

Contributor(s):

Material type: Text

TextDescription: 159 leavesContent type:

text

Media type:

unmediated

Carrier type:

volume

Dissertation note: Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2024. Abstract: Road 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.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Unpublished Materials	Main Library	Undergraduate Thesis Section	UTH BSCS G249m 2024 (Browse shelf(Opens below))	1-1	Not for loan		007017UM

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Previous	UTH BSCS F769p 2021 Prediction of palay harvest time using convolutional neural network	UTH BSCS F959e 2023 Exploring facial recognition method in ccs employee attendance /	UTH BSCS G11d 2019 Development of interactive simulation based on the comparative study of A* and DIJKSTRA'S algorithm	UTH BSCS G249m 2024 Mapping of Rinconada accident-prone zones using data mining /	UTH BSCS G581i 2021 An implementation of greedy algorithm to coin change machine using arduino	UTH BSCS G589r 2021 Road accessibility using satellite image segmentation with deep learning	UTH BSCS G712a 2019 Android-based augmented reality science and earth book using opensurf algorithm	Next

Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2024.

Includes bibliographical references.

Road 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.

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