Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.2

Purpose: This study examines how paid parking zones influence urban decision‐making and how ANPR and AI‐driven analytics enhance enforcement efficiency, predict demand, optimise pricing, and support adaptive parking policy‐making. Need for the Study: Urban congestion and parking inefficiencies remain critical issues in modern cities, affecting mobility, air quality, and economic activity. Amid rapid technological advance, municipalities increasingly rely on automated solutions such as ANPR to regulate parking. However, there is limited research on how these technologies influence decision-making in urban transport policies. Methodology: This study employs a literature review, municipal data collection, and comparative analysis across major Polish cities. It evaluates the effectiveness of paid parking zones, pricing policies, and technological tools, particularly ANPR-based enforcement systems, in managing congestion and ensuring compliance. Findings: The research identifies significant variations in parking policies among Polish cities, with higher fees and extended enforcement hours correlating with improved parking space turnover. The implementation of ANPR has resulted in a substantial increase in compliance rates, streamlining enforcement and reducing reliance on manual inspections. Warsaw, as an early adopter, demonstrated a tenfold increase in scanned vehicles between 2020 and 2024. Practical Implications: Findings highlight the importance of integrating technology-driven enforcement tools into urban transport policies. Policymakers should consider expanding ANPR systems to enhance compliance, reduce operational costs, and inform data-driven decisions on urban mobility strategies. The study underscores the necessity for a balanced approach that aligns parking policies with broader sustainability goals and public transport development.