One of the most troubling things about the information emerging about this faulty sensor is the ways in which Metro indicates they might have expected to detect it. John Catoe’s press release from July 1 described the situation somewhat vaguely. “This is not an issue that would have been easily detectable to controllers in our operations control center. What the analytical profile showed was that the track circuit would fail to detect a train only for a few seconds and then it appeared to be working again.” Why it wouldn’t be easily detectable isn’t clear from his statement, but a Washington Post piece from July 2 credited the following information to Metro’s rail chief, Dave Kubicek.
Instead of completely failing, the track circuit “fluttered” on and off so quickly that, Kubicek said, the failure would not have been obvious in Metro’s downtown operations center, where controllers monitor real-time movement of trains by watching an illuminated graphic depiction of the 106-mile railroad.
“It was happening so fast, you would just blink and miss it,” he said. “Realistically, you had to be looking at the exact area at the exact place” at the exact time.
A controller would have to be staring at something the size of “a button on a BlackBerry.”
A fair number of engineers are going to read this section of text and grind their teeth, but the underlying problem isn’t intuitive to most people. If you eavesdropped on a conversation between two grad students considering writing about this situation for a paper you might hear them say something like this:
Metro’s problem here revolves around the challenge in displaying a digital result in an analog method and inability to detecting a problem using insufficiently granular data.
That’s a complicated phrase which you can explain with a $5 table lamp. Continue reading


















