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 https://zihaosheng.github.io/TMP-LLM/.