4 ms·

this is all wrong. first you don't need to normalize by r ruining vehicles, both metrics are yearly. second the real signal is death per mile driven, now that's

this is all wrong. first you don't need to normalize by r ruining vehicles, both metrics are yearly. second the real signal is death per mile driven, now that's something you should look for, not abstract drivers. third using drivers as base assume all cars vehicles have no passenger. this should at least get you in the right direction, but you'll likely want to put motorbikes into their class at some point. anyway short story long you want the cumulative chance of getting killed, basing the estimate on the average person lifetime miles, which varies widely anyway one person to another making a synthetic number over such variability unfit for any purpose

AdieuToLogic 7y ago
> this is all wrong. Okay, let's go through what you identify as being wrong point-by-point. > first you don't need to normalize by r ruining vehicles, both metrics are yearly. I provided the (active/daily) running vehicles metric in order to establish a (very) conservative estimation of 54m distinct human drivers in calendar year 2016. This was to ensure subsequent analysis was reasonably well founded by way of using common base units. > second the real signal is death per mile driven, now that's something you should look for, not abstract drivers. Perhaps that signal is significant for other premises, but mine was to provide a probability of any given human driver causing or being involved in a vehicular fatality. This was to ensure that the derived probability was reasonably correct based on referenced data. > third using drivers as base assume all cars vehicles have no passenger. Perhaps my use of the descriptor "driver" was improper for the entirety of the analysis. Given the limitations of the medium, it was a trade-off I was willing to make. Your point is correct and, yes, it would affect the probability iff the intent was to determine driver liability. > this should at least get you in the right direction, but you'll likely want to put motorbikes into their class at some point. > anyway short story long you want the cumulative chance of getting killed, basing the estimate on the average person lifetime miles, which varies widely anyway one person to another making a synthetic number over such variability unfit for any purpose Your analysis is keen and one which is goes beyond what I was trying to illustrate to the GP. To wit, the specific point being addressed by my terse analysis was: > U give far to much credit to human drivers.
croon 7y ago
Highways rack up a lot of miles, but is a really easy environment to drive in. Intersections, pedestrian crossings, busy streets, parking etc don't yield a lot of miles to the statistics, but is where most interaction with other people, and most edge cases show up. So I'm not sure that accidents per mile is necessarily the best metric either.
- LoSboccacc 7y ago
  parent was specifically talking fatalities, so that changes the incident mix from just generic accidents to favour again rural roads

AdieuToLogic 7y ago

> this is all wrong. Okay, let's go through what you identify as being wrong point-by-point. > first you don't need to normalize by r ruining vehicles, both metrics are yearly. I provided the (active/daily) running vehicles metric in order to establish a (very) conservative estimation of 54m distinct human drivers in calendar year 2016. This was to ensure subsequent analysis was reasonably well founded by way of using common base units. > second the real signal is death per mile driven, now that's something you should look for, not abstract drivers. Perhaps that signal is significant for other premises, but mine was to provide a probability of any given human driver causing or being involved in a vehicular fatality. This was to ensure that the derived probability was reasonably correct based on referenced data. > third using drivers as base assume all cars vehicles have no passenger. Perhaps my use of the descriptor "driver" was improper for the entirety of the analysis. Given the limitations of the medium, it was a trade-off I was willing to make. Your point is correct and, yes, it would affect the probability iff the intent was to determine driver liability. > this should at least get you in the right direction, but you'll likely want to put motorbikes into their class at some point. > anyway short story long you want the cumulative chance of getting killed, basing the estimate on the average person lifetime miles, which varies widely anyway one person to another making a synthetic number over such variability unfit for any purpose Your analysis is keen and one which is goes beyond what I was trying to illustrate to the GP. To wit, the specific point being addressed by my terse analysis was: > U give far to much credit to human drivers.

croon 7y ago

Highways rack up a lot of miles, but is a really easy environment to drive in. Intersections, pedestrian crossings, busy streets, parking etc don't yield a lot of miles to the statistics, but is where most interaction with other people, and most edge cases show up. So I'm not sure that accidents per mile is necessarily the best metric either.

LoSboccacc 7y ago
parent was specifically talking fatalities, so that changes the incident mix from just generic accidents to favour again rural roads