Safe Route Prediction Based on AQI and Machine Learning

Pollution is the most indispensable and upsetting issues faced in today”s day to day life in the world. Analyzing air quality is crucial for understanding and addressing the potential impacts on human health., ecosystems., and the economy. In this paper., safe route prediction system is designed for Tumakauru city commuters based on Air Quality Index (AQI) integration and by leveraging machine learning models and map functionality. Multiple machine learning models, such as XGBoost, LR, MLP classifier, RFC, and Lasso, are utilized for accurate AQI predictions. The system integrates Google Maps to input source and destination locations, retrieve possible routes, and mark danger zones based on predefined AQI thresholds. Safer routes are suggested by eliminating paths passing through identified danger zones. The system enhances user decision-making and promotes health and environmental sustainability. The results demonstrate the models' accuracy in predicting AQI values and categorizing air quality levels. The system's practical implications include minimizing health risks and reducing air pollution exposure. Overall, the proposed solution introduces a comprehensive solution for safe commuting based on AQI predictions.