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Survey details LLM and MM-LLM use in transportation operations

By PulseAugur Editorial · [1 sources] ·

A new survey paper explores the application of large language models (LLMs) and multi-modal large language models (MM-LLMs) in transportation systems management and operations. The research synthesizes current studies across operations, mobility services, and data support, identifying challenges such as data heterogeneity and explainability. The paper suggests LLMs are most promising as a decision-support layer, particularly MM-LLMs for integrating diverse data inputs. AI

IMPACT LLMs show potential to enhance decision-making in transportation by integrating diverse data sources.

RANK_REASON This is a survey paper published on arXiv, detailing the application of LLMs in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details LLM and MM-LLM use in transportation operations

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This is a survey paper published on arXiv, detailing the application of LLMs in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Siyan Li, Zehao Wang, Jiachen Li, Kanok Boriboonsomsin, Matthew J. Barth, Guoyuan Wu ·

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

    arXiv:2606.00991v1 Announce Type: new 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 m…

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