Big Data Application

Each day I take the subway, like thousands of New Yorkers, to reach my workplace. The commute can be stressful, from anticipating the time of the subway rides to dodging the crowd, the morning trip is fraught with tense moments.

So on a cold winter day while I was waiting for my train, my mind furiously trying to predict which subway would show up first, the E or the F, I had a thought. An app that could help me and the other commuters, an app that could take the guessing out of the rush hour game.

My travel itinerary includes switching subways to reach Grand Central where I hop aboard the Metro-North to arrive in Connecticut. I am constantly trying to make the best guess on which subway will be faster, whether one queue will move faster than the other. See, the difference in saving a few seconds can mean whether I get on the Connecticut bound train or not.

My trip is all of two hours one way, so it can be very frustrating when I reach the station to only see the Metro-North pull away. Not only that when I take the E I have to transfer to the 6 at 53rd and Lex. And inevitably there is always a queue I have to navigate to get to the upper level. I have to make the bet the line that I pick will move quicker than the other.

My commuting experience and the numerous articles I have been reading on big data sparked the idea that cold winter day. An app that would analyze all the happenings on the track, on the platform, up the escalator, down the stairs, every movement, each foot step carefully documented to predict what my commute should be. I was fascinated by the Ted Talks video below, which inspired me to believe that such an app could be possible – one that could even out the traffic while reducing the congestion. It is an app that could finally take the “What if” out of the rush hour equation.

 

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