Artificial Intelligence, Traffic Management, Olongapo City, Law Enforcement, digital infrastructure, policy implementation 

Traffic congestion remains a persistent urban challenge in Olongapo City, Philippines. This study examines the feasibility and strategic framework for integrating Artificial Intelligence (AI) into the city’s traffic management system to mitigate this issue. Utilizing a qualitative research design, data were collected through key informant interviews with city planners, law enforcement personnel, local government officials, and technology experts. The study also included a comprehensive review of literature and case studies from cities that have successfully implemented AI-based traffic solutions. Findings reveal that AI is perceived as a transformative tool capable of reducing congestion, enhancing real-time traffic monitoring, and improving enforcement efficiency through technologies such as adaptive traffic signals and automated violation detection.

The effective deployment of AI depends on critical enabling factors, including robust digital infrastructure, sound data governance, skilled human resources, and supportive policy frameworks. Identified challenges include high implementation costs, limited technical capacity, cybersecurity vulnerabilities, and public trust issues. The study proposes a phased implementation plan emphasizing infrastructure development, capacity building, legal and regulatory frameworks, multisectoral collaboration, and community engagement. The findings underscore that with strategic investment and inclusive governance, Olongapo City can lead the way in AI-driven traffic management in the Philippines, fostering sustainable urban mobility and enhanced quality of life.