Turning Traffic Data into Decisions That Save Lives
How a predictive risk model — powered by generative AI and embedded into operational workflows — enables traffic authorities to anticipate accidents and act before they happen.
How a predictive risk model — powered by generative AI and embedded into operational workflows — enables traffic authorities to anticipate accidents and act before they happen.
The challenge for any traffic authority is straightforward: holidays and adverse weather conditions create unpredictable risk spikes, yet enforcement resources are finite. Deploying teams without data is, in practice, guesswork. Volis addresses this by building a unified data foundation that breaks down silos between internal systems and external sources — transforming scattered records into actionable intelligence.
Data lineage makes the connection transparent: every source that feeds the model is traceable. Decision-makers don't have to trust the AI blindly — they can audit the logic behind every risk score, all the way back to its origin.
The model's depth comes from combining sources that conventional systems overlook. Incident reports are processed with NLP to extract critical variables — such as road type, pavement condition, or vehicle characteristics — that simply don't exist in structured database fields.
| Data Source | Data Type | Method |
|---|---|---|
| Traffic Authority / Internal CRM | Accident History & Vehicle Records | Automated |
| OpenStreetMap | Road Geometry & Curvature | Automated |
| Weather API | Rainfall & Conditions by GPS | Automated |
| Incident Reports | Free Text (via NLP) | Generative AI |
* Data pipeline operated on Palantir Foundry infrastructure.
The model assigns a risk score to every road segment, cross-referencing historical variables against projected scenarios — a holiday weekend, a stretch of heavy rain, or both combined. Decision-makers don't just receive an alert: they receive the logic behind it. Which factors are elevating risk on that specific mile? By how much? This explainability is what turns a prediction into operational conviction.
The platform lets managers drill down from the state level to a single road segment. In the João Melão Highway example, the model flagged a risk 78% above the state average during holiday periods — and surfaced the two main drivers: expressway classification (62 mph speed limit) and the high curvature of that stretch. From there, countermeasure recommendations are precise and justified, not generic.
| Risk Variable | Score Impact | Nature |
|---|---|---|
| Expressway Classification | Primary (max.) | Structural |
| Road Curvature | Secondary | Structural |
| Holiday / High Traffic | Relevant | Temporal |
| Rain / Adverse Conditions | Relevant | Temporal |
* Explainability via SHAP Values — every score is fully auditable by the manager.
Identifying risk is half the work. The other half is acting — and learning from it. The platform connects analysis to operational workflow in a single click: a manager creates an intervention, the AI validates feasibility, estimates cost-benefit, and drafts the technical requirements. Humans decide; the machine handles the heavy lifting of analysis and documentation.
Incident reports are sensitive, dense, and unstructured documents. The platform's generative AI produces sanitized summaries — stripping confidential data while preserving only the critical variables. A two-way road accident, vehicles over 15 years old, a chain-reaction collision: information that would never fit a form field, but that now feeds the model and sharpens future predictions.
Every intervention created moves through a phase pipeline visible to the entire team. AI agents run in parallel — validating, calculating, and documenting — while managers focus their time exclusively on decisions that require human judgment. The outcome is on record: each countermeasure has its impact measured, building a history that makes the model more precise with every cycle.
Each intervention moves through the phases below. AI agents handle the analytical steps; human managers own the strategic decisions.
The platform doesn't just predict — it creates an intelligence cycle that improves with every intervention carried out. Decision-makers act on data, AI handles the analytical workload, and the model learns from results. If your operation still relies on subjective criteria to allocate road safety resources — that problem has already been solved. We can do the same for you.
