IoT and ML-based AQI Estimation using Real-time Traffic Data

Ayu Parmar, Nitin Nilesh, Ritik Yelekar

Abstract:

This report proposes an IoT and machine learning (ML)-based novel method to estimate the air quality
index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes
deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service
providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different
ML models, namely random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP),
are trained on this dataset to predict the AQI category into five levels. The experimental results show an
accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models
were also trained on individual node datasets, and the behavior of AQI levels was observed.

Video URL: https://iiitaphyd-my.sharepoint.com/:v:/g/personal/ayu_parmar_research_iiit_ac_in/EcdevEBhARJPpELooy37BXQBVR_kSN5v3o87FhsTFQxcIA?e=B7Qo2z

Repository URL: https://github.com/ayuparmar/Here_Maps