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Project # 3
Creating a Searchable Database of Texas Mobile Work Zone Safety Regulations through Web Scraping and Semantic Embedding
| Team Lead: | Venezer Odhiambo |
| Other Team Members: | |
| Faculty Advisor(s): | MD Nazmus Sakib |
| Department: | CE |
| Project Type: | Graduate Student |
| Title: | Creating a Searchable Database of Texas Mobile Work Zone Safety Regulations through Web Scraping and Semantic Embedding |
| Abstract: | Mobile work zone safety in Texas is governed by a fragmented regulatory framework spanning federal and state standards across heterogeneous document formats. This fragmentation complicates timely access to requirements. This study presents an agentic, graph-augmented Retrieval-Augmented Generation (RAG) framework for structured retrieval of Texas work zone regulations. A unified regulatory corpus was constructed from sources, including Federal Highway Administration, Texas Department of Transportation, Manual on Uniform Traffic Control Devices, and the National Work Zone Safety Information Clearinghouse. Text was extracted directly from source documents, while figures, tables, and diagrams were transcribed using a Vision-Language Model. Regulatory content was segmented into atomic units, embedded for dense retrieval, and enhanced with an entity-based knowledge graph to enable multi-hop reasoning across related clauses. Evaluation on 26 queries showed that the system’s responses were well grounded in the source documents and closely reflected regulatory intent. The proposed system significantly outperformed GPT-4o-mini in faithfulness (0.94 vs. 0.24) and answer relevancy (0.89 vs. 0.21), while also achieving higher precision (0.84 vs. 0.79), recall (0.91 vs. 0.87), and F1 (0.87 vs. 0.83). The findings show that graph-augmented, multimodal RAG system offers a dependable and traceable framework for retrieving safety-critical regulations in Texas work zones. |
| Poster: | View Poster #3 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |