A Conceptual Framework for Data-Driven Optimization in Transportation Logistics and Infrastructure Asset Management

• Author(s): Francess Chinyere Okolo ; Emmanuel Augustine Etukudoh ; Olufunmilayo Ogunwole ; Grace Omotunde Osho ; Joseph Ozigi Basiru
• Paper ID: 1702840
• Page: 454-466
• Published Date: 31-07-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 1 July-2021

Abstract

The evolution of transportation systems and infrastructure asset management has increasingly relied on the integration of advanced data-driven technologies to enhance decision-making, operational efficiency, and sustainability. This paper proposes a comprehensive conceptual framework for data-driven optimization in transportation logistics and infrastructure asset management. The framework emphasizes the critical role of big data analytics, machine learning, and Internet of Things (IoT) technologies in transforming raw data into actionable insights. By synthesizing insights from recent advancements, the framework outlines how real-time data collection, predictive analytics, and intelligent systems can be systematically applied to optimize logistics routing, fleet management, maintenance scheduling, and infrastructure lifecycle management. The study addresses key challenges such as data integration, scalability, interoperability, and privacy concerns, while highlighting enabling factors including cloud computing, digital twins, and blockchain technology for secure data sharing. The conceptual framework not only bridges theoretical constructs and practical applications but also guides policymakers, engineers, and researchers in adopting data-driven approaches to foster resilient, adaptive, and efficient transportation systems. The paper concludes with directions for future research, advocating for collaborative, cross-disciplinary efforts to further refine and operationalize the proposed model.

Keywords

Data-driven optimization, transportation logistics, infrastructure asset management, big data analytics, machine learning.

Citations

IRE Journals:
Francess Chinyere Okolo , Emmanuel Augustine Etukudoh , Olufunmilayo Ogunwole , Grace Omotunde Osho , Joseph Ozigi Basiru "A Conceptual Framework for Data-Driven Optimization in Transportation Logistics and Infrastructure Asset Management" Iconic Research And Engineering Journals Volume 5 Issue 1 2021 Page 454-466

IEEE:
Francess Chinyere Okolo , Emmanuel Augustine Etukudoh , Olufunmilayo Ogunwole , Grace Omotunde Osho , Joseph Ozigi Basiru "A Conceptual Framework for Data-Driven Optimization in Transportation Logistics and Infrastructure Asset Management" Iconic Research And Engineering Journals, 5(1)