Broadening Access to Transportation Safety Data with Generative AI: A Schema-Grounded Framework for Spatial Natural Language Queries
arXiv:2605.21712v1 Announce Type: new Abstract: Transportation safety analysis requires integrating crash records, roadway attributes, and geospatial data through GIS-based workflows, but access remains uneven across agencies and community stakeholders. Technical prerequisites create a gap between analytical tools central to safety planning and the practitioners able to use them. Local agencies, school committees, and residents may have safety concerns but limited capacity to retrieve, filter, map, and analyze relevant data. Generative AI offers a way to narrow this divide, but its public-sect
The proliferation of generative AI capabilities is leading to exploration of its application in traditionally data-heavy, user-unfriendly public sector domains.
This development democratizes access to complex transportation safety data, potentially empowering non-technical stakeholders to contribute to public safety planning.
Generative AI can act as an intermediary, lowering the technical barrier for residents, local agencies, and committees to analyze geospatial and safety data.
- · Local government agencies
- · Community safety advocates
- · Generative AI developers
- · Public policy researchers
- · GIS software providers (if they don't adapt)
- · Consultants specializing in niche data analysis
- · Agencies resistant to new technologies
Increased engagement and data utilization by a broader range of community stakeholders in transportation safety.
Improved and more localized transportation safety policies and infrastructure changes due to diverse input.
Enhanced public trust and collaboration between citizens and government by making data-driven decision-making more accessible.
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Read at arXiv cs.CL