--- title: Which Road-Safety Projects Should a State Fund? | 3Dogs Nexus description: A 12-model AI panel ranked state DOT road-safety projects and produced the audit trail behind the ranking, including a Bayesian crash-modification-factor condition. url: https://3dogs.ai/case-studies/dot-safety-prioritization/ --- - Which Road-Safety Projects Should a State Fund? | 3Dogs Nexus Start a Decision Case → 🐾3Dogs NexusStructured Decision Intelligence ← Case studies YouTube Start a Decision Case Case study · public-sector decision support # Which road-safety projects should a state DOT fund first? By Alan Finney — Founder, 3Dogs Nexus Transportation agencies rank safety projects with limited funds and heavy scrutiny: every choice must be defensible to legislators, engineers, and the public. This demonstration run asked 3Dogs Nexus to act as the decision-support layer above a DOT's own data — prioritizing candidate projects (rumble strips, cable barriers, crosswalk upgrades) by injury reduction, cost, readiness, equity, and community support, with a documented rationale for every ranking. Can AI help a public agency prioritize which projects to fund? Yes, and the audit trail matters as much as the ranking. A 12-model panel ranked the candidate road-safety projects, returned an 11–0 conditional recommendation, and attached the condition — Bayesian treatment of crash modification factors — that a reviewer would need to accept for the ranking to hold. Public spending decisions need a defensible rationale, not just a number. 161 API calls · 12 AI models11-analyst panel: 11–0 proceed-with-conditions5m 46s end to endModerate confidence · dissent-preserving ## The call, verbatim “Fund the top safety projects—weighted by injury reduction, cost, readiness, equity, and community support—immediately.” Behind that one sentence: an 11-analyst adversarial debate across 12 models, a unanimous proceed-with-conditions vote, and a set of named conditions an agency could hand straight to its program office. ## What the evidence showed The panel grounded the ranking in proven countermeasures: rumble strips, cable median barriers, and crosswalk upgrades. States such as Michigan and Washington have used comparable prioritization methods to cut serious crashes by nearly a quarter — and every dollar spent returns roughly $4–$8 in avoided medical costs and losses. The recommendation leads with the projects where that return is strongest and readiness is real. ## The conditions a real DOT would care about ### Independent score validation Third-party validation or independent audits of project readiness scores — so the ranking survives legislative scrutiny and the scores can't be quietly gamed. ### Bayesian crash-factor adjustment A hierarchical adjustment to Crash Modification Factors that pools national defaults with local covariates (road type, traffic volume, rural/urban status) — reducing bias exactly where the data is sparsest: rural, low-volume projects. The dominant risk it named: inaccurate or inconsistent equity and readiness scoring. If the scoring rules fail to reflect real-world outcomes, top-ranked projects underdeliver, public trust erodes, and legislative scrutiny forces reallocation. The recommendation's conditions exist to close that specific failure mode — not as boilerplate. ## How does a multi-model AI decision-support layer help a transportation agency? It sits above the agency's own data and models — it doesn't replace traffic engineers or crash databases. It consumes their outputs and produces the thing agencies actually struggle to produce: a transparent, adversarially-tested, dissent-preserving prioritization with a written rationale for every ranking, in minutes. That documented rationale trail is precisely what state and federal reporting requirements ask for. ## The delivered report The complete recommendation as delivered — the call, the conditions, the panel vote, the risk analysis. Download the full PDF report → "We don't make your decisions. We make them better." 3Dogs Nexus · Structured Decision Intelligence · 3dogs.ai · Alan Finney · alan@3dogs.ai · (702) 845-2886 Internal demonstration run by 3Dogs staff on the production engine, 2026-07-22 (case 2026-9401, mode: direct / no-follow-up). Not a client engagement; no agency or city supplied data. All verdicts, vote tallies, conditions, run statistics, and quotes are drawn directly from the delivered report and run logs. The full PDF is embedded above, unedited. Terms · Privacy · Security ### Try this on your own question. Free, no card. Bring a real decision — ideally one where you already know the answer — and see what the panel does with it. Start a decision case ## Questions this case answers ### Why does a public agency need the reasoning and not just the ranking? Because the ranking will be challenged. A documented rationale with stated assumptions, preserved dissent and explicit conditions is what survives scrutiny from a board, an auditor or the public. ### Is this a decision-support layer rather than a replacement for engineers? Yes. It consumes the technical outputs and helps prioritise; the professional judgement and sign-off stay with the humans. ### Try this on your own question. Free, no card. Bring a real decision — ideally one where you already know the answer — and see what the panel does with it. Start a decision case ## Related decision case studies - Can AI Read 500,000 Emails and Find Fraud?We read 45,320 real Enron executive emails blind - deduplicated from 517,401 - in about 2.5 hours for roughly $69, and surfaced LJM, Ra - AI Platforms Reviewed by Rival AI ModelsWe let Google's Gemini act as a hostile client, drive a full decision case, and then write its own review of the output. We published i - Should a City Fund a Grocery-Access Study? 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