Predicting and managing the risk of crashes on city roads

Predicting and managing the risk of crashes on city roads

Data for Democracy is building Insight Lane - a crash prediction model that helps cities in their mission to build safer roads by combining multiple sources of open data with the power of data science.

Insight Lane supports cities in achieving their mission in 3 ways:

1. Identifying high risk locations

2. Understanding the contributing factors of risk

3. Assessing the impact of intervetion

Any city with open data can participate. Our latest release builds predictions based on historical crash data, road features and citizen-reported safety concerns, but this is only the beginning - traffic volumes, average speeds, construction events, weather and more can all help understand what creates risk on roads.

We're interested in speaking with any city that shares our vision for improving road safety through data science. As more cities become involved and the depth of data increases, we're looking to unlock the potential of sharing insights between cities, in a way that allows for true collaboration on a global problem rather than siloed, single-city efforts.

Project repo