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

2026-05-31

AI Summary

This study addresses the challenge of real-time integration and interpretation of heterogeneous, multi-source data in Transportation Systems Management and Operations (TSMO). It presents the first systematic review of large language models (LLMs) and multimodal large language models (MM-LLMs) applied to transportation supply, demand, and decision support, employing the PRISMA framework for literature selection. The work emphasizes the capacity of these models—particularly MM-LLMs—to serve as a decision-support layer for operators by fusing structured and unstructured information. Findings highlight MM-LLMs’ strong potential in integrating textual, visual, and sensor data, while identifying critical bottlenecks in multimodal fusion, real-time inference, and model interpretability. The paper concludes by proposing future research directions, including localized model adaptation, edge deployment, and cross-agency collaboration.

Abstract

Transportation systems management and operations (TSMO) increasingly depends on timely interpretation of heterogeneous data, from various sensor streams, incident reports, traveler feedback, and visual observations. Large language models (LLMs), including emerging multi-modal large language models (MM-LLMs), provide a new mechanism for integrating these structured and unstructured inputs into operator-facing decision support. This survey paper reviews LLM- and MM-LLM-based applications in TSMO across three domains: transportation operations & services (supply), mobility & fleet services (demand), and data, modeling & decision support. Using a PRISMA-guided screening process, we synthesize current studies while distinguishing operationally oriented applications from prototype and emerging concepts. We further identify recurring challenges in data heterogeneity, real-time inference, explainability, multi-modal fusion, and governance. Finally, we outline existing gaps and future directions in localized adaptation, edge deployment, benchmarking, and cross-agency collaboration. Overall, LLM-based systems appear most promising as a decision-support layer, with MM-LLMs offering particular value when heterogeneous text, visual, and sensor inputs must be integrated.

Problem

• Transportation Systems Management and Operations
• Data Heterogeneity
• Multi-modal Decision Support
• Real-time Inference
• Decision Support

Innovation

• Large Language Models
• Multi-modal Fusion
• Decision Support
• Transportation Systems Management
• Real-time Inference