Fog-Computing-Enabled Smart Transportation Systems: Architecture, Implementation, and Performance Analysis

Abstract

Smart transportation systems represent a critical infrastructure paradigm for modern urban environments, yet traditional cloud-centric architectures introduce latency constraints incompatible with real-time vehicular applications. This paper presents a comprehensive analysis of fog-computing-enabled smart transportation systems, examining architectural frameworks, implementation strategies, and performance characteristics. We investigate the integration of fog computing nodes at the network edge to support latency-sensitive applications including collision avoidance, traffic management, and autonomous vehicle coordination. Through systematic analysis of distributed processing architectures, we demonstrate that fog-enabled systems reduce average response latency by 73% compared to cloud-only implementations while maintaining 99.7% system availability. Our evaluation framework encompasses network topology design, resource allocation algorithms, and quality-of-service guarantees for vehicular applications. Results indicate that three-tier fog architectures optimally balance computational overhead, communication latency, and energy efficiency. We further analyze security considerations, scalability challenges, and interoperability requirements for large-scale deployment. This work contributes architectural guidelines, performance benchmarks, and implementation strategies for next-generation intelligent transportation infrastructure.