Predicting the Severity of Vehicle Accidents Based on Traffic Accident Attributes Using Machine Learning

Simple item page
dc.contributor.authorSegun Abayomi Sofoluwe
dc.date.accessioned2024-10-09T10:43:47Z
dc.date.available2024-10-09T10:43:47Z
dc.date.issued2023-12
dc.description.abstractThe occurrence of accidents on global road networks results in a considerable loss of human life on a yearly basis, hence underscoring the urgent matter of ensuring road safety. This research aims to predict the severity of road traffic accidents and enhance prediction performance by employing two machine learning algorithms; the Random Forest model and the Decision Tree Classifier model. The study employs a dataset obtained from Kaggle.com, which is subjected to comprehensive data mining, pre-processing, and exploratory data analysis. The dataset was divided into training and testing subsets for model development and evaluation. The evaluation of model performance involved the computation of key performance metrics such as precision, recall, and F1-score. The findings of the study revealed that the Random Forest (RF) model continuously exhibited better performance compared to the Decision Tree (DT) model across all evaluation metrics, including precision, recall, F1-score, and overall accuracy. The evaluations consistently exhibited higher values for RF across all accident severity classes, indicating its greater predictive capability in accurately determining accident severity. The RF algorithm was found to have a higher weighted-average F1-score, taking into account the presence of class imbalances within the dataset. Therefore, based on the findings of this study, it can be concluded that the Random Forest (RF) model demonstrates superior performance in accurately predicting accident severity across all categories, with an overall accuracy rate of 0.84. In comparison, the Decision Tree (DT) model achieves an accuracy rate of 0.73. It is therefore recommended that additional analysis can be done in order to gain a deeper understanding of the underlying causes for misclassifications, with the aim of enhancing the performance of the models for these particular classes. Additionally, optimizing the hyperparameters of the models can result in enhanced performance and utilization of cross- validation methodologies, such as k-fold cross-validation, to more accurately evaluate the models' performance and mitigate the potential for overfitting. Keywords: Accuracy, Accident severity, Algorithms, Data analysis, Exploratory data analysis, F1-score, Fine-tuning, Machine learning, Precision, Random Forest model, Severity prediction Word Count: 311 Words
dc.identifier.citationKate Turabia
dc.identifier.issnM.Sc
dc.identifier.urihttps://repository.lcu.edu.ng/handle/123456789/779
dc.language.isoen
dc.publisherLead City University, Ibadan
dc.relation.ispartofseriesM.Sc
dc.subjectAccident severity
dc.subjectAlgorithms
dc.subjectExploratory data analysis
dc.subjectF1-score
dc.subjectFine-tuning
dc.subjectMachine learning
dc.subjectPrecision
dc.subjectRandom Forest model
dc.subjectSeverity prediction
dc.titlePredicting the Severity of Vehicle Accidents Based on Traffic Accident Attributes Using Machine Learning
dc.typeThesis

Files

Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
Segun, Sofoluwe Abayomi prelininary pages.pdf
Size:
1.39 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Segun Sofoluwe Full thesis.pdf
Size:
19.93 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Department of Computer Science;