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