Road Condition Prediction Using Machine Learning
Our project leverages advanced machine learning models (CNN, RNN, LSTM, and Prophet) to predict road conditions using weather metrics like temperature, precipitation, humidity, and ice coverage. This initiative aims to enhance road safety and aid in better planning for drivers and maintenance teams.
Key Highlights
- Analyzed a diverse dataset containing weather and environmental data over a year.
- Built four machine learning models (CNN, RNN, LSTM, Prophet) to predict road conditions.
- Achieved over 80% accuracy with the RNN model and promising results with LSTM and Prophet for time-series forecasting.
- Provided valuable insights into road safety through data-driven modeling.
Conclusion/Abstract/Spoiler
Through our comprehensive analysis and experimentation with multiple machine learning models, we have successfully demonstrated how weather-related factors like temperature, humidity, precipitation, and ice coverage can be used to predict road conditions. The models we built—CNN, RNN, LSTM, and Prophet—each contributed valuable insights, with the RNN and LSTM models showing the highest accuracy and reliability for forecasting road conditions.
By integrating these models, we have laid the foundation for a more predictive and data-driven approach to road safety, offering a powerful tool for municipalities, maintenance teams, and drivers alike. The insights gained from this project can help mitigate risks, optimize maintenance schedules, and ultimately save lives by improving safety on the roads.
Future plans could include enhanced model training, wider data integration and collection, and possible collaboration with local authorities to implement real-time road condition monitoring systems.