Architectural Convergence of Fleet-as-a-Service, Serverless Computing, and Demand-Responsive Transportation: A Socio-Technical Framework for Sustainable Mobility Systems
Keywords:
Fleet-as-a-Service, Sustainable Mobility, Serverless Computing, Demand-Responsive TransportationAbstract
The accelerating convergence of digital infrastructure and mobility systems has fundamentally redefined how transportation services are designed, operated, and evaluated. Over the last decade, parallel advancements in cloud computing paradigms, serverless architectures, edge and fog computing, and demand-responsive transportation have created a fertile ground for new operational models that transcend traditional fleet ownership and static routing logics. Within this evolving landscape, Fleet-as-a-Service (FaaS) has emerged as a transformative concept that reframes fleets not as fixed capital assets but as dynamically orchestrated, service-oriented resources capable of supporting sustainable vehicle testing, adaptive operations, and data-driven optimization across heterogeneous mobility contexts. This research develops a comprehensive, publication-ready theoretical and methodological examination of FaaS as a unifying socio-technical framework that integrates serverless and microservice-based computing with demand-responsive and hybrid transit systems. Drawing exclusively on the provided scholarly references, the article situates FaaS within the historical evolution of cloud and edge computing, the maturation of microservices and function-as-a-service models, and the long-standing transportation research on flexible routing, first-and-last-mile connectivity, and shared autonomous fleets. Particular emphasis is placed on the role of FaaS in enabling sustainable vehicle testing and operations, as articulated by Deshpande (2024), while extending this perspective through a critical synthesis of computational scalability, economic restructuring, and socio-spatial equity considerations. The methodology adopts a qualitative, integrative research design grounded in conceptual analysis, comparative literature interpretation, and systems-level reasoning rather than empirical measurement. Results are presented as interpretive findings that reveal how FaaS reshapes cost structures, operational resilience, and environmental performance in mobility systems when combined with serverless edge platforms and demand-responsive transit logic. The discussion advances a deep theoretical debate on architectural trade-offs, governance challenges, and future research directions, arguing that FaaS represents not merely a technical innovation but a paradigmatic shift in how mobility ecosystems are governed, evaluated, and sustained. The article contributes a unified analytical framework for scholars and practitioners seeking to understand the long-term implications of digitally mediated fleet services in the transition toward sustainable, adaptive, and inclusive transportation systems.
References
1. Aldaihani, M. M., Quadrifoglio, L., Dessouky, M. M., & Hall, R. (2004). Network design for a grid hybrid transit service. Transportation Research Part A: Policy and Practice, 38(7), 511–530.
2. Adzic, G., & Chatley, R. (2017). Serverless computing: economic and architectural impact. In Proceedings of the 11th Joint Meeting on Foundations of Software Engineering (pp. 884–889).
3. Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and Internet of Things: a survey. Future Generation Computer Systems, 56, 684–700.
4. Berrada, J., & Poulhès, A. (2021). Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas. Transportation Research Part A: Policy and Practice, 150, 317–334.
5. Cab4u. (2021). First and last mile mobility solutions. Retrieved from https://www.cmacgroup.com/technology/first-and-last-mile
6. Castro, P., Ishakian, V., Muthusamy, V., & Slominski, A. (2017). Serverless programming (function as a service). In IEEE 37th International Conference on Distributed Computing Systems (pp. 2658–2659).
7. Chang, S. K., & Schonfeld, P. M. (1991). Integration of fixed-and flexible-route bus systems. Transportation Research Record, 1308, 51–57.
8. Costa, P. C., Cunha, C. B., & Arbex, R. O. (2021). A simulation-optimization model for analyzing a demand responsive transit system for last-mile transportation: A case study in São Paulo, Brazil. Case Studies on Transport Policy, 9(4), 1707–1714.
9. Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In Agent-based Models of Geographical Systems (pp. 85–105). Springer.
10. Deshpande, S. (2024). Fleet-as-a-service (FaaS): Revolutionizing sustainable vehicle testing and operations. International Journal of Applied Engineering & Technology, 6(1), 1854–1867.
11. Dragoni, N., Giallorenzo, S., Lafuente, A. L., Mazzara, M., Montesi, F., Mustafin, R., & Safina, L. (2017). Microservices: yesterday, today, and tomorrow. In Present and Ulterior Software Engineering (pp. 195–216). Springer.
12. Edwards, D., & Watkins, K. (2013). Comparing fixed-route and demand-responsive feeder transit systems in real-world settings. Transportation Research Record, 2352(1), 128–135.
13. Fagnant, D. J., & Kockelman, K. M. (2018). Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation, 45(1), 143–158.
14. Glikson, A., Nastic, S., & Dustdar, S. (2017). Deviceless edge computing: Extending serverless computing to the edge of the network. In Proceedings of the 10th ACM International Systems and Storage Conference.
15. Joy, A. M. (2015). Performance comparison between Linux containers and virtual machines. In International Conference on Advances in Computer Engineering and Applications (pp. 342–346).
16. Lynn, T., Rosati, P., Lejeune, A., & Emeakaroha, V. (2017). A preliminary review of enterprise serverless cloud computing platforms. In IEEE International Conference on Cloud Computing Technology and Science (pp. 162–169).
17. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189.
18. Nastic, S., Rausch, T., Scekic, O., Dustdar, S., Gusev, M., Koteska, B., et al. (2017). A serverless real-time data analytics platform for edge computing. IEEE Internet Computing, 21(4), 64–71.
19. Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., & Rana, O. (2019). Fog computing for the internet of things: A survey. ACM Transactions on Internet Technology, 19(2), 18.
20. Alonso-González, M. J., Liu, T., Cats, O., Van Oort, N., & Hoogendoorn, S. (2018). The potential of demand-responsive transport as a complement to public transport. Transportation Research Record, 2672(8), 879–889.
21. Baresi, L., & Mendonça, D. F. (2019). Towards a serverless platform for edge computing. In IEEE International Conference on Fog Computing (pp. 1–10).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dr. Laurent M. Verhoeven
This work is licensed under a Creative Commons Attribution 4.0 International License.
