Analysis of one month of subway data – part 2: correlation between the total number of trains and the probability of delays

When I first decided to track trains in the NYC subway system I was sure I would find a correlation between the number of trains in the system and the percentage of trains running with delay. I was also sure it would be a positive relationship. The more trains there are in the system, the more traffic jams, and the more delays one should find — simple, right? Surprisingly, as we will see in the…

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One month of subway tracking – part 1: an overview of the recorded data

What is the best time to catch a subway to beat rush-hour delays? What predictors have the largest impact on whether your train will get to its destination on time? To answer these questions I have been tracking the positions of all trains in the NYC subway system for several months in 20 s intervals. From these data, trains that run with delays can be identified, and, after some data-wrangling (not shown here), the following…

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Installing CUDA and cuDNN on openSUSE Tumbleweed

SuSE Linux 6.2, released 20 years ago, was my first experience with a linux-based operating system. Even though I soon thereafter switched to Gentoo and later Ubuntu, I have always retained a soft spot for SUSE — so when I recently built a new data science machine I decided to give openSUSE Tumbleweed a try. Tumbleweed is a rolling release distribution, providing frequent updates of all packages without making you update the entire distribution in…

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Crossing the Manhattan bridge (or applying single-molecule models to transit data)

About a year ago I moved from the Upper East Side (where Rockefeller University and my research job are located) to Brooklyn. This move came at the expense of a much longer commute using the Q line of the infamous NYC subway system. In recent years subway delays had got so out of hand that in 2017 New York Governor Andrew Cuomo declared a state of emergency to accelerate repairs of the decaying system. Even…

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