A Road Accident Prediction Model Using Data Mining Techniques Circuit Diagram The research addresses road accident prediction as a classification issue. Several studies have been conducted to predict road accidents and investigate the severity of road accidents, however, very few of them focus on analysing the relationships between road accidents and the factors contributing to those accidents.
This project combines predictive analytics and an interactive chatbot to enhance road safety. It uses historical traffic data to train accident prediction models and provides real-time feedback via a chatbot. The system aims to reduce accidents through data-driven insights and user engagement. Steps to Enhance Traffic Prediction with AI. Identify Your Unique Goals: Understand specifically what you want to achieve in predicting traffic accidents. Define success metrics. Collect Comprehensive Data: Gather data, including historical accident records, weather reports, and new traffic data. Use AI Tools: Sign up for AI development platforms like Appaca or TensorFlow to start building models.
A study on road accident prediction and contributing factors using ... Circuit Diagram
The project's scope revolves around developing a machine learning model for predicting road accident hotspots. The primary objective is to create a robust and efficient system capable of accurately identifying the road accident hotspots, thereby enhancing potential risks.
"Enhancing Road Safety Through AI-Driven Accident Prediction: A Case Study" This case study examines the practical application of AI-driven accident prediction in a specific city or region. It evaluates the effectiveness of the prediction model in identifying high-risk areas and providing actionable insights to transportation authorities.
AI and Traffic Accident Prediction: Enhancing Road Safety Circuit Diagram
A road traffic accident (RTA) is defined as a collision involving at least one vehicle with roadside objects or other vehicles and can result in property damage, injuries, or fatalities (Mamo et al., 2023).It is currently a global challenge that causes approximately 1.3 million fatalities annually (WHO, 2022).This is especially significant for children and young adults aged between 5 and 29
