Traffic safety intelligence
Segment-level
Risk scores mapped to roads, not county blobs.
24-hour
Precomputed nationwide forecast frames.
Weather-aware
Live conditions fused with learned baselines.
From crash archives to live risk assessment at national level
Road Risk Monitor transforms historical incidents, road geometry and live weather into a continuously refreshed 24-hour risk outlook for major U.S. road segments. It does not wait for incidents to accumulate. It learns where risk normally lives, watches the atmosphere change and turns that signal into a road-level forecast that is fast enough for real operational use.
Major-road risk overlay
What it is
Road Risk Monitor is the missing intelligence layer between weather maps and crash reports
Most transportation analytics stop at summaries of what already happened. Road Risk Monitor does the harder work - it converts static historical records into a forecast surface that operators can browse, audit, and use.
Road-segment intelligence
Major corridors are represented as scored roadway segments, preserving the geography that county-level summaries erase.
Weather-aware forecasting
Historical crash patterns are adjusted with current and forecast atmospheric signals to detect when conditions drift away from normal.
Operational delivery
Forecast frames are precomputed and published as map-ready tiles, so the interface feels like a live instrument instead of a research notebook.
Data sources
Public national datasets feed the forecast stack
This system integrates multiple public datasets so roadway geometry, crash history, weather archives, and live forecast signals can be scored together in one national pipeline.
FARS
Fatal crash records published by NHTSA provide high-severity historical incident labels.
US-Accidents
Large-scale roadway incident records expand the historical sample beyond fatal crashes alone.
NOAA ISD-Lite
Hourly historical weather observations anchor the training data with temperature, wind, and wet-hour context.
TIGER/Line roads
National roadway geometry is simplified into machine-readable segments that can be forecast consistently.
National Weather Service
Live forecasts update the current 24-hour outlook so the map reflects changing atmospheric conditions.
Methodology
Dual-layer modeling balances national coverage with road-level detail
The system uses a dual-layer approach: a nationwide H3-based baseline model and a road-segment level forecasting model. Predictions incorporate historical incident frequency, temporal features such as hour, day, and seasonality, and weather conditions including temperature, precipitation, wind, and related atmospheric context. Live predictions are updated using real-time weather data.
Nationwide baseline
The H3 layer provides a coarse national prior so broad spatial patterns can be learned consistently across the country.
Segment forecasting
The road model refines that prior at segment scale, preserving corridor-level variation that county or state summaries lose.
Live adjustment
Current and forecast weather conditions shift the learned baseline so each frame responds to changing operating conditions.
Limitations
This is a research and engineering prototype
The system is useful for exploration and hypothesis testing, but it should not be treated as production-grade operational guidance without deeper validation.
- Historical datasets may be incomplete or uneven across regions and time periods.
- Exposure is simplified because the current system does not incorporate traffic volume data.
- Incident reporting datasets can contain structural bias in what gets recorded and how it is classified.
- Evaluation is difficult because traffic incidents are highly imbalanced and spatially persistent.
Predictions should not be used for operational decision-making without further validation.
Purpose
Why build this system at all
The goal of this project is to explore how machine learning systems can support transportation safety, disaster response, and infrastructure resilience at national scale. It is intended to help people think more clearly about where predictive risk layers can be useful, where they remain fragile, and what better public safety tooling could look like.
System architecture
How raw records become a national risk layer
Road Risk Monitor combines geospatial processing, time-series alignment, predictive modeling, and tile publishing into one repeatable production pipeline.
Road graph
National roadway geometry is cleaned, simplified, and split into major-road segments that can be scored consistently.
Incident labeling
Historical roadway incidents are projected onto nearby segments and assigned temporal context.
Weather alignment
Hourly weather observations and forecasts are spatially matched to representative road locations.
Model training
The model learns baseline risk, weather sensitivity, seasonality, and same-hour patterns by segment.
Tile publishing
Forecast scores are converted into vector and raster tiles for fast national map rendering.
Inference
End-to-end pipeline
Operational loop keeps the forecast current and presents it in a format people can use.
Forecast ingestion
Fresh weather forecasts are pulled into the scoring pipeline and aligned to relevant road locations.
Segment-hour scoring
Every forecast frame is evaluated against pretrained priors and live atmospheric context.
Tile generation
Scores are packaged into tiled layers so nationwide browsing stays fast and smooth.
Map delivery
Operators can inspect risk overlays, click roads, and compare present conditions against normal.
Why it matters
Earlier awareness for agencies, planners and operators
Road Risk Monitor helps transportation organizations move from reactive reporting to proactive attention. It gives teams a common risk layer for planning, weather response, staffing, maintenance coordination and executive communication.
- Turns passive crash archives into forward-looking awareness
- Connects atmospheric change to roadway-specific exposure
- Supports planning and monitoring from the same forecast base
- Packages scientific modeling as a product people can actually use
Low
Conditions are near or below the learned normal for this segment and hour
Elevated
Risk is drifting upward and deserves routine awareness
High
Weather, timing, and segment history combine into a stronger warning signal
Severe
The system sees a meaningful departure from normal operating conditions
About
Road Risk Monitor is the missing intelligence layer between weather maps and crash reports.
It does not wait for incidents to accumulate. It learns where risk normally lives, watches the atmosphere change, and turns that signal into a road-level forecast that is fast enough for real operational use.