🚨 AI-powered traffic accident severity predictor using Random Forest ML. Built with Python, Streamlit & Plotly. Features real-time prediction, interactive dashboards, risk radar charts & 22-feature weather/road analysis.

“Unsupervised ML project to cluster drivers and vehicles based on accident risk patterns using RTA dataset.”

React + Mapbox app to assess route accident risk. Pick start/end; backend derives curvature/lanes/road type, lighting (sunrise/sunset) and weather (NWS), runs an ML model, and returns risk. Terraform config included for AWS deployment.

An intelligent system that uses AI/ML 🤖🧠 to analyze traffic conditions 🚦🛣️, driver stress 😰📈, and behavioral patterns 👀📊 to predict accident risk ⚠️🚨 in real time ⏱️. Designed to enhance driver safety 🛡️🚗, reduce road accidents 🚑💥, and support smarter driving decisions 📊✅.

AI-Driven Road Accident Prediction System (Mar 2025): Developed a hybrid rule-based and ML (Random Forest) system with 85% accuracy, integrated 3 live APIs for real-time risk analysis, enabled hotspot visualization via geospatial heatmaps, and improved model accuracy by 15% through real-world feedback loops.

ML Project (Road Accident Risk Predicition)

Проект по прогнозированию дорожных рисков, ML-модель предсказывает вероятность ДТП с точностью 88.5%

Machine learning system for predicting high-risk road segments using geospatial data, feature engineering, and ensemble models (RF, XGBoost, Logistic Regression)

Using combination of models to predict road accident risk

Road Accident Risk Analysis using ML and Hotspot Clustering

ML-based accident risk prediction using weather, traffic, and road features

Road Accident Prediction & Risk Analysis using ML. Analyze historical data, predict accident severity, and gain insights to improve road safety and reduce risks.

ML pipeline to identify road accident risk factors in autonomous vehicle contexts

This project predicts road accident risk using ML and ANN models. It analyzes features like road type, weather, lighting, and speed limit to identify accident-prone conditions. Models such as Random Forest, CatBoost, and ANN were compared, achieving up to 88.5% accuracy and 0.0589 RMSE, ensuring reliable

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Predictive ML solution (XGBoost + feature engineering + hyperparameter tuning) for road-segment accident risk estimation

End-to-end ML pipeline for road accident risk prediction with synthetic data and Bayesian correction.

Interactive Streamlit app to compare two roads and predict accident risk based on road, traffic, and environmental features. Includes ML model predictions or a fallback formula for realistic risk scoring.

Analyze U.S. Accidents (2016-2023) using ML and parallel computing to uncover patterns, predict risks, and improve road safety.

🚧 Road Accident Severity Predictor (Jan 2025 – Present) Streamlit ML app predicting accident severity (Fatal/Serious/Slight) with 85%+ accuracy using Random Forest. Features include risk factor analysis, PDF reports, and a responsive UI for road safety planning.

An ML-based system to predict accidents on high-risk ghat roads using real-time and historical data such as weather, traffic, and road conditions. It aims to alert drivers and authorities to potential hazards, reducing accidents and improving road safety in mountainous regions.

A ML project for predicting road-accident risk based on road conditions, weather, lighting, and traffic-related factors. The system includes a FastAPI backend and a Streamlit web application that provides real-time risk predictions, visualizations and safety recommendations.

This project aims to analyze road and transportation data using Machine Learning (ML) models and Pandas DataFrames to predict the risk of accidents in real time. The goal is to identify accident-prone areas and generate early warnings to improve road safety.

RoadWatch: A full-stack accident analysis and prediction platform with interactive maps, ML-powered risk forecasting, and real-time SQLite-backed insights for proactive road safety management.

Analyzed 300K+ Chicago traffic accidents (2013–2020) using Python, GeoPandas, and ML models to predict risk and identify high-risk zones. Built interactive dashboards with Tableau and Plotly to provide data-driven insights for road safety improvements.

About A Streamlit ML app that predicts road accident severity using Random Forest/Decision Tree models. Features interactive UI, real-time visualizations, and analyzes 10 key traffic factors for risk assessment.

A machine learning project that predicts the likelihood of road accidents based on factors like weather, time, location, and vehicle type. It uses data preprocessing, feature engineering, and ML models to identify high-risk conditions and improve road safety insights.

An ML-based Android system for real-time road accident risk detection. It uses smartphone sensors to collect live driving data and predicts risky situations like harsh braking or sharp turns. The app sends instant voice or vibration alerts to the driver to prevent accidents.

IoT-based connected vehicle system for real-time environmental monitoring and road risk prediction using FastAPI and ML models trained on the US Accidents dataset (Kaggle). Features GPS tracking, Firebase integration, and live visualization dashboards.

This machine learning project is part of Kaggle Competition. The dataset is synthetically generated & acquired from Kaggle. The goal of this project is to build a ML model that predicts the risk of road accidents.