Using Variational Encoders to Enhance Classification in Indian Roads Analysis

India possesses one of the world’s largest road networks, which is vital to the country’s economic development. Transportation of products and people is primarily centred on this network, which has about 5.8 million kilometres of road. Data analytics and machine learning approaches can be used to control and optimise this network. In this regard, we explored the application of classification algorithms to predict road surface quality using a dataset of synthetic road surface data. Several classification methods have been trained and evaluated, including Decision Trees, Random Forest, Logistic Regression, Support Vector Machine, XGBoost, CatBoost, and LGBM. The accuracy, precision, recall, and F1-score measures were used to compare the performance of various methods. Researchers have investigated the use of feature augmentation techniques such as Variational Autoencoder (VAE) to improve the performance of classification models. VAE is a deep generative model capable of learning a low-dimensional latent representation of high-dimensional input. Researchers can develop new features that represent the underlying structure of data by using VAE to road-related data such as traffic patterns and road conditions. Our results indicates the potential of applying machine learning algorithms to forecast road surface condition, which may be utilised to improve road network maintenance and safety.