This paper introduces the application of a data science algorithm in analyzing Big Data related to the volume of missing ballast in a track section and the development of track geometry defects. The data intensive algorithms are necessary in order to effectively analyze over 100,000 segments of track representing nearly 1000 miles (1700 km) of data containing over 23,000 track geometry exceptions or defects. The specific analysis tool used was logistic regression, which allowed for modeling a dichotomous output, and this tool was implemented with encouraging results. Missing ballast data was obtained from a hy-rail-mounted LIDAR-based ballast profile measurement system and correlated to track geometry defects that developed along the inspected track locations on a major US class I railroad. Analyses were conducted to provide a determination of the proportion of segments that develop geometry exceptions as a function of missing ballast volume. Parameter studies were performed for curvature and annual millions of gross tons (MGT) as well. The logistic regression analysis was then performed and the resulting model was used to calculate the probability that a given track segment will contain or develop track geometry exceptions. The relationship between an increase in missing or deficient ballast and increased probability of developing a track geometry defect was developed and confirmed.
Transportation Infrastructure Geotechnology – Springer Journals
Published: Apr 20, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera