Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support

2026-05-31 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mobility Services & Technology, Transportation Infrastructure · Depth: Advanced, quick

Summary

A survey paper published on 2026-05-31 examines the application of Large Language Models (LLMs) and multi-modal LLMs (MM-LLMs) in Transportation Systems Management and Operations (TSMO). It reviews how these models integrate heterogeneous data, including sensor streams, incident reports, traveler feedback, and visual observations, into operator-facing decision support systems. The review covers three key domains: transportation operations & services (supply), mobility & fleet services (demand), and data, modeling & decision support. The authors identify recurring challenges such as data heterogeneity, real-time inference, explainability, multi-modal fusion, and governance. Future directions include localized adaptation, edge deployment, benchmarking, and cross-agency collaboration. The paper concludes that LLM-based systems are most promising as a decision-support layer, with MM-LLMs particularly valuable for integrating diverse text, visual, and sensor inputs.

Key takeaway

For AI Architects designing transportation decision support systems, you should prioritize multi-modal LLMs (MM-LLMs) to effectively integrate diverse inputs like sensor data, visual observations, and text. Focus on addressing challenges such as real-time inference and data heterogeneity early in your design. Consider future directions like localized adaptation and edge deployment to ensure robust, scalable solutions for TSMO.

Key insights

LLMs and MM-LLMs offer a new mechanism for integrating diverse transportation data into operator-facing decision support systems.

Principles

• TSMO relies on timely heterogeneous data interpretation.
• LLMs excel at integrating structured and unstructured inputs.
• MM-LLMs are key for multi-modal data fusion.

In practice

• Integrate sensor streams, incident reports, traveler feedback.
• Address data heterogeneity and real-time inference.
• Explore localized adaptation and edge deployment.

Topics

• Large Language Models
• Multi-modal LLMs
• Transportation Systems Management
• Decision Support Systems
• Data Heterogeneity
• Edge Deployment

Best for: AI Scientist, Research Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.