Predicting Traffic Accidents Using CCTV Footage Weather Data

This project uses CCTV videos and weather data to study why road accidents happen. By observing rain, fog, and traffic flow, we learn patterns that help predict risky spots and keep people safe daily.

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Inspiration

Road accidents are increasing every year, especially during rain, fog, and low visibility. I noticed that most accidents happen because drivers don’t get timely warnings about risky road conditions. This inspired me to build a system that uses CCTV and weather data to predict accident-prone situations before they occur and help save lives.

What it does

Our project analyzes CCTV footage and weather data together to identify when and where accidents are most likely to happen. It detects patterns like:

sudden traffic density

low visibility

rainfall or fog

unusual vehicle movement

Using these patterns, it predicts high-risk zones and provides early warnings.

How we built it

Collected CCTV video frames and weather datasets.

Cleaned and combined both datasets using Python and Pandas.

Extracted traffic density and weather indicators.

Used correlation analysis and simple ML logic to identify accident-risk patterns.

Visualized results through line graphs, heatmaps, and trend charts.

Built a prediction module that estimates risk levels based on input conditions.

Challenges we ran into

Lack of real-time traffic datasets

Handling noisy CCTV frames

Matching timestamps between CCTV and weather data

Deciding which features were most useful

Making predictions with limited historical accident labels

Accomplishments that we're proud of

Successfully combined two very different data sources: CCTV and weather

Identified clear patterns between rainfall, fog, and accident spikes

Built clean visualizations that show risk rising with weather changes

Created a lightweight predictive model

Turned a raw idea into a working, understandable project

What we learned

How to perform data fusion between video frames and weather datasets

How traffic conditions drastically change with weather

Practical experience in feature engineering and EDA

Real-world data is messy and requires careful cleaning

Importance of timestamp synchronization in multi-source data projects

What's next for Predicting Traffic Accidents Using CCTV Footage Weather Data

Adding real-time live CCTV processing

Using deep learning (YOLO) for vehicle detection

Adding real-time risk alerts to traffic police dashboards

Integrating GPS and map-based accident heatmaps

Expanding the system to multi-city datasets

What's next for Predicting Traffic Accidents Using CCTV Footage Weather Data

Real-time CCTV Integration: Live video processing that can detect traffic flow and unusual movement instantly.

Deep Learning Vehicle Detection: Implementing YOLO or similar models to accurately detect vehicles, speed, and congestion levels.

Real-time Risk Alerts: Sending automatic alerts to traffic police and control rooms when accident risk becomes high.

Interactive Accident Heatmap: Building a live map that highlights high-risk zones based on weather and traffic conditions.

Mobile App for Drivers: A simple app that warns drivers about accident-prone areas during bad weather.

Multi-City Deployment: Expanding the model to more CCTV networks across different cities to improve accuracy.

Historical Accident Labels: Adding real accident data from government sources to train a more accurate prediction model.

Integration with Weather APIs: Connecting to real-time weather forecasting APIs for dynamic predictions.

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