AI predicts accident hot-spots from satellite imagery and GPS data
It’s interesting that they are doing this, but it’s not particularly novel. The vast majority of accidents happen on roads that meet certain criteria:<p>- Speed Limit > 30mph and < 60 mph<p>- lots of driveways and left turns<p>- moderately heavy traffic at some times of day but not constant gridlock<p>Just picture the commercial drag strip and the collector roads that wind between huge garden apartment complexes or gated subdivision.<p>Accidents are especially likely where two such streets cross.<p>Why?<p>These are the futon of transportation. They’re designed first for high speed and volume of traffic, but also high direct access to the surrounding businesses. These objectives are horribly in conflict and not safe to combine. People want to drive FAST on these, and they do, but they are frustrated by the stop and go nature and there is a constant stream of “surprises” when other drivers take a chance on a turn through a tight gap or run a light that just turned red because they’re sick of waiting. One moment of distraction for either party and BAM, a crash.<p>Good streets for local access are small and slow moving (high volume can be accommodated by a parallel network of such streets, ie “a grid” but it doesn’t have to be perfect squares). Good roads for high speed are wide, clear, and simple, with lots of extra room to cushion mistakes (medians, shoulders, etc.)<p>All this model has really done is learned to recognize new “stroads” that don’t have a crash history yet, but will soon.