Start a Decision Case →
🐾3Dogs NexusStructured Decision Intelligence
Case study · public-sector decision support

Which road-safety projects should a state DOT fund first? A 12-model AI panel built the ranking — and the audit trail.

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.

161 API calls · 12 AI models11-analyst panel: 11–0 proceed-with-conditions5m 46s end to endModerate confidence · dissent-preserving

The call, verbatim

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 →