Road Accidents are a leading cause of injuries and fatalities worldwide, making them a critical area of research. By analyzing and predicting traffic accidents, we can identify key contributing factors.
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Updated
Aug 4, 2024
Road Accidents are a leading cause of injuries and fatalities worldwide, making them a critical area of research. By analyzing and predicting traffic accidents, we can identify key contributing factors.
[IEEE Research paper + Project] Real Time Road Accidents Detection System based on crash estimation; a computer vision techniques that detects road accidents and reports them in real-time as well as allowing the monitoring of accidents using a client server architecture and an interactive GUI.
Software for registering anomaly situations on roads
This is a traffic severity prediction model built using XGBoost, deployed on flask.
This contains the road accident data dashboard and the following documentation
The project aims at using quasi-experimental designs to estimate the impact of policy reducing speed limits in major cities, on the number of road accidents. It is based on Bayesian hierarchical modelling and Poisson regression
This is an Excel project on Road Accident Data Analysis in the form of an interactive Dashboard.
This is the Arduino code for a helmet that detects accidents as well as reports them to relevant authorities.
A graphical analysis report of traffic fatalities in Australia.
🌍 Brazil Road Accidents
Road collisions of Ireland
Road collision data for the United States
Road collision data for Canada
Road collision data for Australia and New Zealand
Data Analytics to find optimal locations for ambulance
Multi-dimensional Analytics Project on Road Accidents of India.
A brief analysis of the UK road accidents data to proof the relationship between accidents and internal and external factors.
Observable study of the road accidents in France in 2017
Data Analysis of Road Traffic Accidents
Visualizing road accidents in India using chart.js and data from data.gov.in
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