Predicting Snowmobile Speed from Visible Locked-Track and Rolldown Marks in Groomed/Packed Snow Conditions

Event
SAE 2016 World Congress and Exhibition
Authors Abstract
Content
The ability to accurately calculate a snowmobile’s speed based on measured track marks in the snow is important when assessing a snowmobile accident. The characteristics and length of visible snowmobile track marks were documented for 41 locked-track braking tests and 38 rolldown tests using four modern snowmobiles on a groomed/packed snow surface. The documented track mark lengths were used to quantify the uncertainty associated with using track mark length to estimate initial speed. Regression models were developed for both data sets. The regression model of the locked-track tests revealed that using an average deceleration of 0.36g over the length of the locked track mark provides a good estimate of the best-fit line through the data, with the upper and lower 95th percentile prediction interval bounds best represented by using deceleration rates of 0.23g and 0.52g respectively. For the rolldown tests, using an average deceleration of 0.23g over the length of the measured rolldown mark provides a good estimate of the best-fit line through the data, with the upper and lower bounds of the 95th percentile prediction interval best represented by using deceleration rates of 0.13g and 0.40g respectively. For our locked-track braking tests, we also calculated a 0.38 ± 0.19s delay between initial brake application and track lock. The results of this study will allow investigators to quantify the accuracy of their calculated speeds using measured track mark lengths on similar snow conditions.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1477
Pages
6
Citation
D'Addario, P., Iliadis, K., and Siegmund, G., "Predicting Snowmobile Speed from Visible Locked-Track and Rolldown Marks in Groomed/Packed Snow Conditions," SAE Int. J. Trans. Safety 4(1):128-133, 2016, https://doi.org/10.4271/2016-01-1477.
Additional Details
Publisher
Published
Apr 5, 2016
Product Code
2016-01-1477
Content Type
Journal Article
Language
English