Satellite-Driven Traffic Volume Estimation: Harnessing Hybrid Machine Learning for Sustainable Urban Planning and Pollution Control

As cities grow and more cars occupying the roads, greenhouse gas emissions and air pollution in urban areas are going up. To better understand the emissions and pollutions, and help effective urban environmental mitigation, an accurate estimation of traffic volume is crucial. This study delves into the application of Hybrid Machine Learning models to estimate and predict traffic volume by utilizing satellite data and other datasets in both the USA and Europe. The research investigates the predictive capabilities of machine learning models employing freely accessible global datasets, including Sentinel 2, Night-time light data, population, and road density. Neural Network, nearest neighbours, random forest and XGBoost regression models were employed for traffic volume prediction, and their accuracy was enhanced using a hyperparameter-tuned K-Fold Cross-validation technique. Model accuracy, evaluated through Mean Percentage Error (MPE%) and R-square, revealed that XGBoost Regression model yielding an R2 accuracy of 0.81 and MPE of 13%. The low error (and therefore high accuracy) as well as the model's versatility allows its application worldwide for traffic volume computation utilizing readily available datasets. Machine learning models, particularly the XGBoost Regression model, prove valuable for on-road traffic volume prediction, offering a dataset applicable to town planning, urban transportation, and combating urban air pollution.