Turn traffic risk data into life-saving decisions.

We use AI-powered analytics to help cities and road operators identify danger zones, reduce crashes, and design safer streets.

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The road to safety is blocked by poor data.

Bad data costs lives.
We give cities the clear, real-time insights they need to act before the next accident happens.

Blind Decisions

Cities often rely on outdated crash reports, missing real-time dangers and failing to act before the next accident.

One-Size-Fits-All Fixes

Without street-specific risk data, road interventions are reactive, costly, and often miss the real cause.

Invisible Risk Zones

Dangerous curves, poor lighting, or missing signs don’t show up in stats — but our data sees what others can’t.

Smarter Street Safety Starts with Predictive Insight

Predict Where Risk Will Rise

Instead of reacting to past accidents, use multi-source data to forecast dangerous zones before they cause harm.

Powered by: Crash history + geometry + traffic + weather + custom models

Get Actionable Recommendations

Each high-risk zone includes the top contributing factors and suggested interventions — from signage to structural improvements.

Powered by: Root cause AI model + transport expertise

Zoom Into Every Intersection

Hyperlocal analysis shows risks at the street level — curves, crossings, blind spots — not just city-wide trends.

Powered by: Map overlays + dynamic location-based scoring

Use Cases

Use advanced data analytics to cut rural‑road crashes

iNOWAYTiON researchers applied decision‑tree models to thousands of rural‑road records and found just two variables—traffic flow and wind speed—explain most serious crashes. By mapping those patterns we can:

Pinpoint high‑risk corridors before the next crash
Visualise root causes for quick, evidence‑based fixes
Recommend counter‑measures that suit each road’s conditions

Data‑driven insight means safer journeys for every driver who leaves the city limits.

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Stop accidents before they happen again

Gas stations, fast-food drive-thrus, and shopping center exits are often hotspots for repeat accidents — especially when visibility is poor or merges are confusing.

Our system flags these locations using historical crash clustering, AI-driven root cause analysis, and environment context (e.g., signage gaps or lane design flaws).

Detect risk buildup before the next crash
Support precise interventions like mirror placement, lighting upgrades, or turn re-designs
Help businesses and councils fix pain points together