[Submitted on 7 Oct 2026]
Title:Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System
View a PDF of the paper titled Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System, by Zihao Sheng and 8 other authors
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Abstract:Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility. However, TMP preparation remains labor-intensive and heavily dependent on practitioner expertise. This paper proposes a Large Language Model (LLM)-assisted framework to automate TMP content generation, leveraging the WisDOT WisTMP system as the application context. The framework fine-tunes multiple open-source LLMs across different model scales and deploys them locally to ensure data security. To support model training, we construct a domain-specific dataset from historical WisTMP documents by converting PDF files into structured question-answer pairs in JSON format. Experimental results show that fine-tuning significantly improves performance across standard text generation metrics. Further section-wise and strategy-level analyses reveal that, while LLMs achieve strong overall performance, they tend to over-generate strategies and struggle to produce project-specific justifications and accurate cost estimates. In addition, scaling from 7B/8B to 14B yields limited gains. These findings demonstrate the potential of LLMs to improve TMP preparation efficiency while highlighting remaining challenges in LLM-assisted TMP development. The source code and demo videos will be publicly available at this https URL.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.10650 [cs.CL]
(or arXiv:2610.10650v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.10650
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zihao Sheng [view email] [v1] Wed, 7 Oct 2026 15:51:01 UTC (1,682 KB)
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