Technical Reports

Development of an Adaptive Predictive Braking Enforcement Algorithm

  • 01
  • Jun
  • 2009
AUTHOR: Joseph Brosseau, Bill Moore Ede
KEYWORDS: Positive train control, Adaptive Enforcement Algorithm, North American Joint Positive Train Control program, Train Operations and Energy Simulator (TOES™), Test Controller/Logger (TCL), simulation
ABSTRACT: Predictive enforcement braking is one of the key concepts behind positive train control (PTC) systems. If a train is on the verge of overrunning a target stopping location, such as an authority limit, the system enforces a brake application to stop the train safely short of the limit. The concept depends on an algorithm that can predict the stopping distance of the train. Errors in stopping distance prediction can result in target overruns, target underruns, or unnecessary enforcements, which can negatively impact railroad safety or operations. Due to the uncertainty of many parameters that affect stopping distance, PTC enforcement algorithms have traditionally used a target offset to ensure that no trains overshoot the target. But this can force the algorithm to be overly conservative, resulting in unnecessary or early warnings and enforcements. Federal Railroad Administration (FRA) contracted Transportation Technology Center, Inc. (TTCI) to research proof-of-concept techniques for improving the accuracy of PTC enforcement algorithms by adapting the algorithm to the characteristics of each specific train. The project included a parametric study of some of the key variables that can affect stopping distance, followed by the development and testing of the adaptive functions.