Status : Verified
| Personal Name | Marasigan, Mike Joseph L. |
|---|---|
| Resource Title | The future role of Artificial Intelligence (AI) and Machine Learning (ML) in the Philippine urban traffic management system: a technology foresight through scenario building with AI-assisted analysis |
| Date Issued | 26 May 2026 |
| Abstract | Urban traffic congestion in Metro Manila imposes severe economic costs—estimated at PHP 4.9 billion per day, and erodes the quality of urban life, yet its management remains reactive and fragmented. While Artificial Intelligence (AI) and Machine Learning (ML) offer transformative potential, their adoption in the Philippines is constrained not by technology availability but by institutional and readiness deficits. This study employs a Technology Foresight methodology, integrating a PESTLE‑informed environmental scan, a structured stakeholder questionnaire supplemented by key‑informant interviews, and the ten-stage Technology Foresight methodology (Imbang, 2019) to explore four plausible futures for AI/ML‑enabled traffic management over a 10‑to‑15‑year horizon. Drawing on absorptive capacity, socio‑technical systems, and the Collingridge dilemma, the analysis identifies two critical uncertainties: the level of inter‑agency governance coordination and the degree of technology and data readiness. The four resulting scenarios, ranging from integrated smart mobility to a stalled, reactive system, demonstrate that AI/ML becomes transformative only when institutional and technological capabilities co‑evolve. An AI‑assisted validation process, using Large Language Models to critique internal logic, generate commuter‑perspective vignettes, and propose early warning signals under human supervision, strengthens the scenario analysis. Cross‑scenario comparison yields five robust strategies: strengthening inter‑agency governance, improving data interoperability, building technical capability sequentially, using pilots as learning platforms, and aligning AI investments with long‑term mobility and sustainability goals. These strategies are sequenced in a three‑horizon roadmap. The study reframes Philippine traffic congestion not as an engineering problem awaiting a technical fix but as a technology‑management challenge requiring institution‑f |
| Degree Course | Master of Technology Management |
| Language | English |
| Keyword | AI in traffic management; Machine learning; Technology foresight; Scenario planning; Philippine urban mobility; Absorptive capacity; Governance |
| Material Type | Thesis/Dissertation |
Preliminary Pages
87.17 Kb
Category : P - Author wishes to publish the work personally.
Access Permission : Limited Access