Design Analysis of an Enterprise Architecture for a Big Data and IoT-Based Smart Traffic Management Information System to Improve Public Transportation Efficiency

Abstract

The rapid growth of urbanization triggers chronic congestion and deteriorating air quality in urban areas. Objective: This study aims to design an Enterprise Architecture (EA) for a Smart Traffic Management Information System based on Big Data IoT using the TOGAF ADM 9.2 framework. Methods: The research employed a qualitative-descriptive approach combined with system engineering methodology based on the TOGAF ADM 9.2 framework, limited to five main stages: Preliminary Phase, Architecture Vision, Business Architecture, Information Systems Architecture, and Technology Architecture. Findings: The design results in a blueprint for business architecture, data architecture, application architecture, and technology architecture that can reduce traffic data latency and sectoral emissions. The Dual-Stream Data Pipeline model enables simultaneous processing of traffic and emissions data to support efficient public transportation operational decisions. Conclusion and Implication: This EA provides strategic guidance for local governments in realizing efficient and sustainable public transportation. The novelty of this research lies in the integration of highway corridor architecture (Dual-Stream Data Pipeline) which brings together public transportation optimization and environmental emission monitoring simultaneously, addressing the gap between Smart Traffic and Smart Environment systems that typically operate in silos. Keywords: enterprise architecture, TOGAF ADM, big data IoT, smart traffic, smart environment.