ROAD ACCIDENT SEVERITY & HOSPITAL RECOMMENDATATION USING DEEP LEARNING TEHNIQUES

Abstract

Road accidents continue to be a significant cause of injuries and fatalities worldwide. 
Prompt medical care is crucial in reducing the severity of injuries and saving lives. 
This project aims to develop a system that predicts the severity of road accidents and 
recommends the nearest hospitals equipped to handle the resulting injuries. 
Leveraging deep learning techniques, specifically convolutional neural networks 
(CNNs) and recurrent neural networks (RNNs), the system utilizes various input 
features such as weather conditions, road type, time of day, and historical accident 
data to predict accident severity. Additionally, it incorporates geographical 
information to identify nearby hospitals capable of providing appropriate medical 
assistance based on the predicted severity levels. The proposed system not only aids 
in prioritizing emergency responses but also optimizes resource allocation in 
healthcare systems, ultimately contributing to improved road safety and better 
outcomes for accident victims. Its potential extends to informing urban planning 
decisions and policy-making to create safer road environments.