Monday, Feb 03Feb_3

Tuesday, Feb 04Feb_4

Wednesday, Feb 05Feb_5

Thursday, Feb 06 Feb_6

Friday, Feb 07Feb_7

Saturday, Feb 08 – People from New Jersey and East New York visiting the city? Start clusters (blue) are near the Path and NEC trains.Feb_8

Sunday, Feb 09 – It seems like the system is “artificially balanced on Sundays”Feb_9

These visualizations attempt to show a macro view of Citi Bike imbalances in the Feb 03-09 2014 week. I am overlapping the data of start and end stations in two layers to have a macro view of the system behavior: blue showing the clusters of stations where trips start, red showing the clusters of stations where trips end, and purple showing where there are intersections of red and blue (balanced clusters?). It is interesting to realize that the center of the system seems balanced, while areas in the periphery, such as Brooklyn, show more variation throughout the week. Also, Saturday and Sunday show patterns completely unrelated to those of weekdays. Both in Saturday and Sunday, the largest blue clusters (start stations) are in the areas of Manhattan where people from New Jersey (Path and NEC train) and Brooklyn would start their trips. On Saturday, the red clusters (end stations) are distributed throughout the city with two large clusters near Path stations, and other two large clusters near the East River. For me, the most interesting pattern from these visualizations, is the red layer from Sunday end stations, which shows a balanced system, ready to start a new week. I believe that this suggests that Sundays are tough days for Citi Bike workers, because they have to set the system ready for the next week. Zooming in and looking at the system from a micro viewpoint is also interesting, however, I believe that the most interested hypothesis from this visualization can be extracted from a macro viewpoint of the system. These visualizations were made using CartoDB.


Proposal for Mid-term Project

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Example of an Intelligent Urban System

Tappsi was the first app for ordering a taxi service using mobile devices in Colombia. The app pairs up nearby passengers and taxi drivers using the GPS of mobile phones and the mobile network. What started as a free app for a double-sided market in Bogota, soon became popular and expanded to other major cities in the country. One year after its debut, it started charging monthly fees to drivers, charging passengers for priority services, and offering the possibility to pay fares using credit cards. In a city such as Bogota, where finding a cab can be a nightmare, Tappsi is a great example of an app that found leverage points in the system, improving the experience for both drivers and customers. The system is controlled by Tappsi, a private company that—according to themselves—acts as a filter, accepting or rejecting drivers into their database after carefully reviewing their profiles, cars, and documentation. The app is very active in social networks, always looking for ways to improve its services, and occasionally sending surveys to its subscribers.


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