Road Risk Monitor: A Deployable U.S. Road Incident Forecasting System with Live Weather and Road-Level Tiles

TL;DR

Road Risk Monitor is a comprehensive, deployable U.S. road incident forecasting system integrating historical data, live weather, and road geometry to provide real-time predictions via APIs and web tools.

Contribution

It introduces a nationwide road-safety stack combining data integration, model training, and live serving for real-time incident forecasting.

Findings

Trained a nationwide H3 baseline on FARS fatal-crash data.

Developed a pipeline using TIGER/Line geometry and US-Accidents events.

Serves predictions through APIs, raster tiles, and a web application.

Abstract

Nationwide road-incident forecasting is a systems problem before it is a modeling problem. A usable service must connect historical incident archives, historicalandliveweather,nationalroadgeometry, offline model training, tile generation, web serving and runtime handoff. This paper presents Road Risk Monitor, a U.S.-wide road-safety stack that combines a nationwide H3 baseline trained on FARS fatal-crash data with a road-segment forecasting pipeline trained from TIGER/Line geometry and US-Accidents events, then serves predictions through live APIs, raster tiles, JSON road tiles, and a public web application.