Evaluating the Impact of COVID-19 on Transportation Infrastructure Funding in the United States

2022-08-31

International Conference on Transportation and Development 2022

AI Summary

This study addresses the significant decline in motor fuel consumption during the COVID-19 pandemic and its adverse impact on U.S. state-level transportation infrastructure funding, which heavily relies on fuel tax revenues. For the first time, the research integrates pandemic dynamics, fuel consumption patterns, and demographic data into a machine learning framework to quantify the long-term fiscal effects on state transportation budgets and forecast future trends. The proposed model demonstrates exceptional predictive performance at the state level (R² > 95%), accurately capturing fluctuations in fuel usage. Projections indicate that fuel tax revenues in several states are expected to remain 10%–15% below pre-pandemic levels for one to two years, providing policymakers with a data-driven foundation for strategic planning and fiscal adaptation.

Technology Category

• Application Domains: Transportation
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has caused a reduction in business and routine activity and resulted in less motor fuel consumption. Thus, the gas tax revenue is reduced, which is the major funding resource supporting the rehabilitation and maintenance of transportation infrastructure systems. The focus of this study is to evaluate the impact of the COVID-19 pandemic on transportation infrastructure funds in the United States through analyzing the motor fuel consumption data. Machine learning models were developed by integrating COVID-19 scenarios, fuel consumptions, and demographic data. The best model achieves an R2-score of more than 95% and captures the fluctuations of fuel consumption during the pandemic. Using the developed model, we project future motor gas consumption for each state. For some states, the gas tax revenues are going to be 10%-15% lower than the pre-pandemic level for at least one or two years. © 2022 International Conference on Transportation and Development

Problem

• COVID-19
• transportation infrastructure funding
• gas tax revenue
• motor fuel consumption

Innovation

• machine learning
• fuel consumption prediction
• transportation funding
• COVID-19 impact analysis
• gas tax revenue