Model Context Protocols in Adaptive Transport Systems: A Survey

2025-08-26

AI Summary

Adaptive traffic systems face fragmentation challenges—including protocol heterogeneity, semantic disintegration, and contextual isolation—arising from interconnected devices, autonomous systems, and AI applications. To address this, we propose the Model Context Protocol (MCP) as a unified integration paradigm, establishing a five-category taxonomy encompassing semantic interoperability, dynamic adaptation, context awareness, AI collaboration, and cross-domain integration. Through systematic literature review and architectural analysis, we identify critical adaptability bottlenecks of conventional protocols in context-sensitive scenarios and demonstrate MCP’s core advantages in ensuring semantic consistency and enabling AI-driven decision-making. We further present an MCP-enabled integration architecture and a phased evolution roadmap. This work provides a rigorous theoretical framework and actionable technical pathway for building next-generation intelligent transportation infrastructure that is adaptive, context-aware, and interoperable.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport systems, where diverse protocols and context sources remain isolated. This survey provides the first systematic investigation of the Model Context Protocol (MCP) as a unifying paradigm, highlighting its ability to bridge protocol-level adaptation with context-aware decision making. Analyzing established literature, we show that existing efforts have implicitly converged toward MCP-like architectures, signaling a natural evolution from fragmented solutions to standardized integration frameworks. We propose a five-category taxonomy covering adaptive mechanisms, context-aware frameworks, unification models, integration strategies, and MCP-enabled architectures. Our findings reveal three key insights: traditional transport protocols have reached the limits of isolated adaptation, MCP's client-server and JSON-RPC structure enables semantic interoperability, and AI-driven transport demands integration paradigms uniquely suited to MCP. Finally, we present a research roadmap positioning MCP as a foundation for next-generation adaptive, context-aware, and intelligent transport infrastructures.

Problem

• Fragmentation in adaptive transport systems from isolated protocols
• Bridging protocol-level adaptation with context-aware decision making
• Standardizing integration frameworks for AI-driven transport demands

Innovation

• Unifying protocol-level adaptation with context-aware decision making
• Client-server and JSON-RPC structure enabling semantic interoperability
• Integration paradigm for AI-driven transport systems