Artificial Intelligence in Transport Governance: Advancing Public Finance, Road Safety, and Citizen-Centric Governance in India

Over the past two decades, India has undertaken one of the most extensive digitization exercises in public administration in the developing world. Processes that once depended on paper files, repeated physical visits, localized discretion, and fragmented record systems have progressively migrated to interoperable digital platforms across sectors such as taxation, welfare delivery, identity management, and transport regulation. In the transport sector, the transition has been especially consequential. Centralized databases, electronic tolling, automated enforcement, digital payments, and online service delivery have significantly improved transparency, operational efficiency, and traceability.

Yet digitization alone does not amount to intelligent governance. Digital systems can record, transmit, and reconcile transactions more efficiently, but by themselves they remain largely transactional. They do not automatically identify emerging patterns of non-compliance, anticipate expenditure stress, or direct limited state capacity to the highest-risk areas. The next phase of reform therefore lies in embedding analytical intelligence within existing digital systems.

This paper argues that the integration of artificial intelligence into transport public financial management represents a structural shift from digitized, rule-based administration to predictive, adaptive, and risk-informed governance. Drawing on fiscal state capacity theory, digital governance scholarship, public value thinking, and a human development perspective, the paper demonstrates how AI can strengthen revenue assurance without increasing statutory tax burdens, improve allocative efficiency through predictive budgeting, reduce corruption through systemic redesign, and enhance road safety through preventive analytics. Using India-relevant illustrations including FASTag, e-challan systems, and state-level transport revenue reforms, the paper shows that AI-enabled governance is already reshaping public finance and citizen outcomes. It also examines challenges associated with algorithmic bias, privacy, institutional capacity, federal coordination, and the digital divide. The paper concludes that AI-enabled public financial management is not merely a technological reform; it is a deeper institutional shift capable of strengthening fiscal resilience, governance integrity, road safety, and citizen-centric development.