Multivariate Statistical Model for Predicting Occurrence and Location of Broken Rails

Transportation Research Record - Tập 1825 Số 1 - Trang 48-55 - 2003
C. Tyler Dick1,2, Christopher P. L. Barkan2, Edward R. Chapman3, Mark P. Stehly3
1HDR Engineering, Inc., 512 Main Street, Suite 600, Fort Worth, TX 76102
2Railroad Engineering Program, University of Illinois at Urbana–Champaign, 1201 Newmark Civil Engineering Laboratory, 205 North Mathews Avenue, Urbana, IL 61801
3Hazardous Materials, Burlington Northern Santa Fe Railway, 2600 Lou Menk Drive, Fort Worth, TX 76131

Tóm tắt

Broken rails are the leading cause of major accidents on U.S. railroads and frequently cause delays. A multivariate statistical model was developed to improve the prediction of broken-rail incidences (i.e., service failures). Improving the prediction of conditions that cause broken rails can assist railroads in allocating inspection, detection, and preventive resources more efficiently, to enhance safety, reduce the risk of hazardous materials transportation, improve service quality, and maximize rail assets. The service failure prediction model (SFPM) uses a combination of engineering and traffic data commonly recorded by major railroads. A Burlington Northern Santa Fe Railway database was developed in which the locations of approximately 1,800 service failures over 2 years were recorded. The data on each location were supplemented with information on other engineering and traffic volume parameters. A complementary database with the same parameters was developed for a randomly selected set of locations at which service failures had not occurred. The combined databases were analyzed using multivariate statistical methods to identify the variables and their combinations most strongly correlated with service failures. SFPM accuracy in predicting service failures at specific locations exceeded 85%. Although further validation is necessary, SFPM is promising in the quantitative prediction of broken rails, thereby improving a railroad’s ability to manage its assets and risks.

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Transportation Research Record

Tập 1825 Số 1

48-55

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