Shawon, Ashadullah (Ontario Tech University), Azim, Akramul (Ontario Tech University)

Advancing Road Safety: Road Accident Severity Prediction Using Deep Learning Models

Scheduled for presentation during the Poster Session "Data Mining and Data Analysis" (FrAT14), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

The alarming rise in worldwide road traffic accidents is posing serious challenges to numerous aspects of human existence. However, there has been a lack of attention paid to the important aspects of traffic characteristics, causation analysis, accident severity analysis, and the relationships between diverse causal factors. The road accident severity prediction system can be a significant analytical resource for traffic analysts and researchers aimed at identifying the major elements of road accident severity to improve road safety for automobiles as well as individuals. In this paper, we proposed effective deep-learning models for the tabular data to predict the severity of road accidents and our experimental results outperformed previous researcher’s results.