One of the more valuable things I would like to see from this network of sensors is heat maps of traffic issues.
For example, California rolling stops are the stuff of legend, and yet failure to stop is a significant cause of accidents and injuries. A heat map of the day of week, time of day, when these incidents are most likely could provide for selective and useful enforcement.
Alternatively, that same data could be used to indicate that stop signs should be replaced with a yield sign in a particular direction or convert the entire intersection to a roundabout to ease traffic flow.
My hope is that the data streams will be used in aggregate and not for individual enforcement. Trends are your friend.
This reminds me of something that happened to me several years ago.
I was in living in a small town and a new cop moved in. He became very diligent about enforcing the law, and I was pulled over for a rolling stop along a lonely country road. He didn’t give me a ticket (I wasn’t even sure what I had done wrong), but he gave me a stern warning and let me go. I continued on my way (mildly fuming) and came to the next stop down the same road. So I “stopped” and began taking a left. I was still thinking about this when I looked to my left, then right and began to pull out. I realized I just did another rolling stop, so I hit my brakes and this time I actually stopped.
And to my surprise a fast moving car emerged out of my right side blind spot. He was right at the crossing, and had I continued he would have smacked into me.
I was in my pickup truck, and the blind spot was caused by an aftermarket plastic detail that was attached to both windows, so it made the ‘A’ frame 3 times as wide as it should have been, and the angle of the road and speed of the vehicle perfectly matched the location of this plastic piece, effectively hiding the oncoming vehicle so I didn’t catch it when I first looked right.
I met that same cop some weeks later at a social gathering and of course went out of my way to shake his hand in gratitude.
I had nearly the same experience. Leaving later than usual from work, I had to exit through an entrance gate, which left my approach to the road at a different angle than usual. I looked left and right, then started into the road (doubt that I stopped completely). A car seemed to appear from nowhere. I had time to stop, but was embarrassed and agitated. The next day I recreated the scene and realized that the window frame on the right side had a slope that matched the slope of a section of the road, blocking my view. The speed and timing of our approaches and the angles of the road and window frame hid the approaching car from my sight. A full stop would have allowed for the approaching car to move through my blind spot.
To be pedantic (and acknowledge something I think you're acknowledging), it's the failure to yield that is the cause of the collision, not the failure to stop. One can yield without stopping, and stop without yielding. This is a pet peeve of mine, as I had a commute through a neighborhood with countless 'useless' stop signs across an arterial, and yet, as I trudged through this morass for a decade, I more than once almost failed to notice a pedestrian because I was so intent on the process of the sign, the stop line, and so on.. and less on being aware of my surroundings. The laser-like focus on the stop line actually reduced my ability to 'see' what I needed to see.
One of the more valuable things I would like to see from this network of sensors is heat maps of traffic issues.
For example, California rolling stops are the stuff of legend, and yet failure to stop is a significant cause of accidents and injuries. A heat map of the day of week, time of day, when these incidents are most likely could provide for selective and useful enforcement.
Alternatively, that same data could be used to indicate that stop signs should be replaced with a yield sign in a particular direction or convert the entire intersection to a roundabout to ease traffic flow.
My hope is that the data streams will be used in aggregate and not for individual enforcement. Trends are your friend.