Citation

Fahad K, Joarder MF, Nahid M, Tasnim T, Arefin MS, Kaiser MS, Bhuiyan T, Dey N, Mahmud M. Lect. Notes Netw. Syst. 2024; 867: 793-808.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/978-981-99-8937-9_53

PMID

unavailable

Abstract

Nowadays road accidents are becoming a major global challenge, leading to a high rate of injuries, fatalities, and large economic losses annually. The number of collisions in our nation increasing daily. Developing an accurate model for the prediction of the severity of traffic accidents is a crucial task. Many researchers have already predicted different aspects of road accidents. They have applied different machine learning (ML) algorithms for their work. Whereas a minimal number of researchers have used ensemble-based ML algorithms. In this paper, we have used four ML algorithms such as Random Forest, XGBoost, KNN, and LGBM to form a voting-based ensemble ML model for road accident severity prediction. Multiple road accident datasets have been used to predict the best evaluation for this study. Bangladesh (BD) road accident data from 2017 to 2020 have been collected from Accident Research Institute (ARI) in Bangladesh University of Engineering and Technology (BUET). Another dataset was the USA road accident dataset from 2016 to 2020. Furthermore, SelectKBest and ExtraTreeClassifier algorithms have been used for selecting suitable features for this paper. According to the experimental results, the voting demonstrates the best contribution for both binary and multiclass classification in the BD dataset for achieving the highest accuracy of 96% and 71%, respectively. On the other hand, the voting indicates the highest performance for both binary and multiclass classification in the USA dataset, achieving the accuracy of 92.1% and 87%, respectively. Finally, the result of the comparative analysis shows that voting is providing the best performance than others.

Language: en

Keywords

ExtraTreeClassifier; Machine learning; Road accident; SelectKBest; Severity prediction; Voting-based ensemble model