Considerations regarding the "Shortest Warning Time" in the range of vehicles
We cannot predict the unknown.
Return to "How do you know what are the shortest warning times for vehicles for a given situation?
However, if we and our students avoid analysis because we can't be certain about the future, we have only two choices:
Neither of these choices seem acceptable. Neither choice seems better than preparing our students to analyze and draw conclusions as well as they can, in order to make informed decisions about crossing.
If Lorraine and Dick, Gordon and Sue had been able to analyze their crossings in this manner, they might not have made the decisions they made.
How can we analyze situations?
If we decide to analyze crossing situations as well as we can, that brings us back to our original question:
How can we be sure our assessment includes at least one of the vehicles with the shortest warning time for that situation?
There is no definitive answer to that question -- we each need to make our best judgment as to how thoroughly we should observe and measure vehicle detection-to-arrival times before making any conclusions.
But page 10 has some ideas that may help guide us
-- ideas which came from O&M participants at workshops and were formalized into a flow chart by Jolene Marie Troisi at the University of Pittsburgh.
In the meantime, you might enjoy reading about the results of a study about this issue that was done at Peabody many years ago.
At the request of Dr. Everett ("Butch") Hill shortly before he died, Dr. Mary-Maureen Snook-Hill and the O&M Department at Peabody College did a preliminary study of this issue in 1995 (Snook-Hill, M and Sauerburger, D. 1996).
We were interested in how to ensure that in our assessments we were including the vehicles with the shortest warning time (what I'll call the "shortest-warning" vehicles)
** NOTE: The vehicles with the shortest warning times are NOT necessarily the fastest cars, or even the quietest cars!
When measuring the detection-to-arrival time of 20 vehicles in 24 situations, we found that we could be 95% sure of observing a "shortest-warning" vehicle approaching from a given direction by observing at least 12 vehicles from that direction.
The questions that remain are whether measuring detection-to-arrival time of 20 vehicles in each situation was enough for this study and if so, whether 95% certainty is sufficient to draw any conclusions when analyzing situations.
In most cases, it takes an inordinate amount of time to measure detection-to-arrival times of 12 vehicles from each direction.
Also, if the first 12 were obviously not "shortest-warning" vehicles (such as trucks coming from a nearby construction site) then the "95% certainty" of our statistics wouldn't apply.
But if we could reliably recognize the "shortest-warning" vehicles when they pass, we could draw more accurate conclusions with less time than we could by measuring the detection-to-arrival times of 12 vehicles in each direction.
Thus we decided to study whether it is possible for people to be able to identify which vehicles are the "shortest-warning" vehicles.
We found that when testing in one condition with no practice, several people did identify the "shortest-warning" vehicles as they passed, but whether they could do so repeatedly and in other conditions remains to be seen.
We also don't know whether the two who failed to identify the shortest-warning vehicles could learn to do so with practice and feedback.
It would be helpful if more research could address this question.
- Research indicated the detection-to-arrival time was not affected by how quiet the car was, and it had little relation to the speed of the cars (Wall Emerson and Sauerburger, 2008).
- At the crossing where Dick and Lorraine were killed, one of the two cars I couldn't hear until they were 3 seconds away was going very slowly and the other was going very fast.
Snook-Hill, Mary-Maureen and Sauerburger, Dona (1996). "Teaching students to assess safety for crossing streets which have no traffic control," in Proceedings of International Mobility Conference VIII, Tambartun National Resource Centre, Melhus, Norway, pp. 535-540
Wall Emerson, Rob, and Sauerburger, Dona (2008). "Detecting approaching vehicles at streets with no traffic control." Journal of Visual Impairment and Blindness, AFB Press, Volume 102, Number 12, pp. 747-760