eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

Artificial Intelligence-Driven Transformation of Fleet Management and Sustainable Transportation: Integrated Strategies, Theoretical Foundations, and Practical Implications

DOI
pdf
Dr. Miguel Alvarez 1 ,
4 Global Institute for Transport Systems, Universidad Internacional

Abstract

Background: The convergence of artificial intelligence (AI), cloud computing, and telematics is reshaping fleet management, route optimization, emissions monitoring, and the broader logistics ecosystem. Recent market analyses predict rapid expansion in AI adoption for transportation systems, while applied studies report gains in cost-efficiency, predictive maintenance, and operational resilience (Mahajan, 2025; Kaluvakuri, 2023). This paper synthesizes multidisciplinary evidence from market reports, technical blogs, case-based industry sources, and peer-reviewed studies to produce an integrated, theory-driven account of AI’s role in modern transportation and fleet management.

 Methods: Using a rigorous, theory-explicit narrative synthesis grounded in the provided references, this study reconstructs methodological pathways employed across industry and academic contributions, translating disparate empirical findings into a unified explanatory framework. We employ a conceptual meta-methodology that traces data pipelines, analytics architectures, and decision-making loops commonly reported in telemetry-driven fleet systems (Microsoft Azure, 2024; Drozdov, 2024).

 Results: AI interventions manifest across six core domains: predictive maintenance, dynamic route optimization, energy and emissions management, demand-responsive logistics, autonomous vehicle integration, and strategic financial planning. Evidence indicates that real-time telemetry plus AI yields measurable reductions in idle time, fuel consumption, and operating costs while increasing fleet uptime and planning accuracy (Drozdov, 2024; Paul et al., 2025; Patil & Deshpande, 2025). Market projections suggest significant growth in AI-in-transportation spending through 2032 (Mahajan, 2025).

 Conclusions: The AI-enabled transition is both technologically tractable and institutionally complex. Successful deployment requires interoperable data architectures, clear performance metrics, ethical governance, and alignment with decarbonization goals. We propose an integrative research agenda to address measurement standardization, socio-technical risk, and regulatory harmonization, and outline practical recommendations for fleet operators, policymakers, and researchers.

Keywords

artificial intelligence, fleet management, route optimization, predictive maintenance

References

📄 Gautam Mahajan. "Artificial Intelligence in Transportation Market Size and Share Analysis - Growth Trends and Forecasts (2025-2032)," 2025. Available: https://www.coherentmarketinsights.com/industry-reports/artificial-intelligence-in-transportationmarket
📄 Venkata Praveen Kumar Kaluvakuri. "The Impact of AI and Cloud on Fleet Management and Financial Planning: A Comparative Analysis," 2023. Available: https://www.researchgate.net/publication/382622809_The_Impact_of_AI_and_Cloud_on_Fleet_Management_and_Financial_Planning_A_Comparative_Analysis
📄 RishabhSoft. "AI and Machine Learning in Fleet Management," 2024. Available: https://www.rishabhsoft.com/blog/machine-learning-in-fleet-management
📄 Alex Drozdov. "Real-Time Route Optimization with AI Solutions," 2024. Available: https://yellow.systems/blog/real-time-route-optimization-with-ai
📄 Microsoft Azure. "Data analytics for automotive test fleets," 2024. Available: https://learn.microsoft.com/en-us/azure/architecture/industries/automotive/automotive-telemetry-analytics
📄 Ejaz M, Naz A. "Role of Logistics and Transport Sector in Globalization: Evidence from Developed and Developing Economies." Sir Syed University Research Journal of Engineering & Technology, 2023 Jun 28;13(1):48–52. Available from: https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/534
📄 IEA Agency. "CO2 Emissions in 2022." IEA; 2023. Available from: https://www.iea.org/reports/co2-emissions-in-2022
📄 Greene S. "Freight Transportation." MIT Climate Portal. 2023. Available from: https://climate.mit.edu/explainers/freighttransportation
📄 Raj GD, Thandayudhapani S. "Evolution of E-Commerce Logistics: Global Trends and Implementations." ComFin Research. 2024;12(2):42–45.
📄 Romero CA, Correa P, Ariza Echeverri EA, Vergara D. "Strategies for Reducing Automobile Fuel Consumption." Applied Sciences. 2024 Jan 1;14(2):910. Available from: https://www.mdpi.com/2076-3417/14/2/910
📄 Brand C, Marsden G, Anable JL, Dixon J, Barrett J. "Achieving deep transport energy demand reductions in the United Kingdom." Renewable and Sustainable Energy Reviews. 2024 Oct 7;207:114941–1.
📄 George A. S. "AI-Enabled Intelligent Manufacturing: A Path to Increased Productivity, Quality, and Insights." ResearchGate. 2024 Aug 25;02(04):50–63. Available from: https://www.researchgate.net/publication/383212034_AIEnabled_Intelligent_Manufacturing_A_Path_to_Increased_Productivity_Quality_and_Insights
📄 Paul J, Alli OD, Adegbola JO. "AI-Powered Route Optimization Reducing Costs and Improving Delivery Efficiency." ResearchGate. 2025. Available from: https://www.researchgate.net/publication/389987796_AIPowered_Route_Optimization_Reducing_CostS_AND_IMPROVING_DELIVERY_EFFICIENCY
📄 Wang C, Atkison T, Park H. "Dynamic adaptive vehicle re-routing strategy for traffic congestion mitigation of grid network." International Journal of Transportation Science and Technology. 2023 Apr 18;14. Available from: https://www.sciencedirect.com/science/article/pii/S2046043023000321
📄 Adeoye Y, Onotole F, Ogunyankinnu T, Aipoh G, Osunkanmibi A. A., Egbemhenghe J. "Artificial Intelligence in Logistics and Distribution: The function of AI in dynamic route planning for transportation, including self-driving trucks and drone delivery systems." World Journal of Advanced Research and Reviews. 2025 Feb 5;25(2):155–67.
📄 Patil, A. A., & Deshpande, S. "AI-Enhanced Fleet Management and Predictive Maintenance for Autonomous Vehicles." International Journal of Data Science and Machine Learning. 2025;5(01):229-249.
📄 Chukwunweike Joseph, Salaudeen Habeeb Dolapo. "Advanced Computational Methods for Optimizing Mechanical Systems in Modern Engineering Management Practices." International Journal of Research Publication and Reviews. 2025 Mar;6(3):8533-8548. Available from: https://ijrpr.com/uploads/V6ISSUE3/IJRPR40901.pdf

How to Cite

Dr. Miguel Alvarez. (2025). Artificial Intelligence-Driven Transformation of Fleet Management and Sustainable Transportation: Integrated Strategies, Theoretical Foundations, and Practical Implications. Global Multidisciplinary Journal, 4(11), 60-68. https://www.grpublishing.org/journals/index.php/gmj/article/view/215

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

61-70 of 79

You may also start an advanced similarity search for this article.