Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System

Summary

Researchers propose an LLM-assisted framework to automate content generation for Transportation Management Plans, using the WisDOT WisTMP system as the application context. They fine-tune multiple open-source LLMs at 7B/8B and 14B scales, deploy them locally for data security, and train on a dataset built by converting historical WisTMP PDFs into JSON question-answer pairs. Fine-tuning significantly improves standard text generation metrics, but models over-generate strategies and struggle with project-specific justifications and accurate cost estimates, and scaling from 7B/8B to 14B yields limited gains.