AI Model and Data Analytics for Estimating Environmental Footprint Parameters for Automobiles in US

Abstract: This pioneering research analyzes carbon emissions across a comprehensive dataset of over 27,000+ transportation modes, including cars, eCars, hybrid vehicles, buses, trams, metro, trains as well as walking, biking, and eBikes. Spanning from 1985 to 2024, the dataset captures critical emissions metrics—CO2, NOx, and VOC—alongside detailed vehicle specifications such as engine displacement, weight, and year model. To address incomplete data or predict emissions for an automobile not present in our dataset, advanced machine learning (ML) models are employed. These models analyze existing patterns from the comprehensive dataset, incorporating factors such as engine displacement, weight, fuel type, and model year and more to predict emissions metrics like CO2, NOx, and VOC. By training on historical data spanning multiple vehicle types and modes of transportation, the ML models can accurately predict emissions for new or unseen automobiles. This predictive capability ensures comprehensive coverage and allows the model to deliver insights even for vehicles not explicitly included in our original dataset.
Further application of this research involves the development of an interactive web or smartphone platform that enables users to track and reduce their travel footprints. With Gmail login, users can save vehicle information and input trip details using an intuitive autocomplete feature, generating real-time route-based trip footprint estimates. The platform would use gamification—rewards, recognition, and social connections—to motivate eco-friendly transportation choices, while an AI-powered assistant offers personalized feedback.
This research has far-reaching environmental implications, offering a practical tool to help individuals and policymakers better understand and reduce transportation-related emissions. By providing reliable environmental data and predictive insights, this work contributes to broader environmental analysis studies, supporting evidence-based policy decisions and helping to drive global efforts toward reducing environmental emissions. Its potential to promote behavior change at the individual level also makes it a powerful tool for contributing to long-term environmental sustainability.

Keywords: Carbon Emissions, Vehicle Emissions, Predictive Modeling, Machine Learning, Transportation Sustainability, CO2, NOx, VOC, Environmental Impact, eCars, Hybrid Vehicles, Public Transit, Real-Time Carbon Footprint, AI, Data Analytics.