Data Analysis

Finding Home with Uber

Every more so often, Uber is my designated driver for Friday night escapades. I don’t live in a properly mapped area, so I let the driver find the best way home from the destination pin. Since I’m normally in half stupor mode, I rarely check the direction untill full consciousness is regained the next morning 😉 . I decided to overlay the maps of my last three trips to find which driver attempted to take me home in the shortest time using the shortest distance.
uber_tripsFor each point of origin, I calculated the minimun spanning tree to my destination. The Minimum Spanning Tree is a concept in Graph Theory that determines the shortest route to traverse an area. Only one driver attempted to be very efficient but missed a route through the CBD that would have shortned his path. There was one miscreant who kept driving back and forth which ended up doubling the distance and the cost.

One other thing that I seeked in the data was to find how far I am with different locations of interest. The average distance from places of interest is 25 km and the average time of travel is about 35 minutes. Since the journey was made in the dead of the night with minimal traffic, it worth noting that the best case scenario of making it to the other half fo the city is 35 minutes. This is a good baseline data point that I would otherwise not have known if Uber never existed. My ETA for meetings now has a baseline time – > 35 minutes. I hope your home is not far away from civilisation like mine to warrant a mininum of 35 minutes commute in the best case scenario.

 

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