A Study on the Transformation of Traffic Police Enforcement Model in the Era of Big Data

Abstract

This study explores the transformation of China’s traffic police enforcement model under the influence of big data. Traditional enforcement focuses on on-site enforcement and manual evidence collection, characterized by low transparency and standardization, simplified penalty procedures, and a strong emphasis on efficiency. It faces practical challenges such as inadequate procedural fairness, low transparency, and human resource shortages. With the deep integration of electronic monitoring, vehicle and driver databases, intelligent analysis, and other big data technologies, traffic enforcement is shifting towards proactive governance, data-driven decision-making, and comprehensive oversight. However, there are challenges related to legal compatibility, procedural fairness, algorithm transparency, and public trust when big data is involved in law enforcement. Further institutional and technological responses are urgently needed. To address these challenges, this paper proposes optimization paths from four dimensions: legislative alignment, reformation of enforcement procedures, technological governance, and social participation. Research has shown that big data not only enhances the efficiency and fairness of enforcement but also promotes sustainable traffic management and the building of public trust, laying the foundation for intelligent public governance.