Today I would like to “announce” the release of an awesome project I have had the pleasure of working on with the incredibly talented Nathan Johnson since late August of this year (2015). Pretty much all of my recent posts on this blog have been with regards to exploring the data from the MTA One Bus Away feed. This has all been directed at the effort of creating a public portal to explore bus performance data for the MTA. And without further adieu, take a gander at this below screen capture of the Github splash page that redirects to the main project!
In addition to point out that Nathan is super talented, I would also like to thank Microsoft NY for their support. Without them sponsoring me as a Fellow, this would not have been a reality, so please be sure to check them out online and see what else they are up to. For what it’s worth, they have great talks and events they sponsor at Civic Hall in Midtown that I would suggest checking out. Not only are they free and usually have food (thus satisfying any requirements of the graduate school student side of my brain), but they can be quite interesting, as well!
So what is this project? For the uninitiated, here’s the boilerplate from the About page on the new project site: “This project was built by Nathan Johnson and Kuan Butts during the Fall of 2015. Kuan Butts was sponsored as a Microsoft Civic Technology Fellow in New York City and joined up with Nathan, who had been scraping and exploring the MTA’s One Bus Away real time bus location data feed since roughly August of 2014. The project involved the creation of a number of tools to handle and calculate aspects of the MTA’s bus data. When possible, these tools were generalized to have utliity with any standard GTFS file and all, including the code for this site, are housed under the Bus Data NYC working group on Github. This project runs on a large MySQL database that is running on an Azure Cloud Virtual Machine. It compiles real time data on bus locations of all buses in operation at 30 second intervals, roughly. Daily, it collects and archives 30-second scrapes, running performance metrics calculations as well that allow us to infer arrival and departure times from stops and, consequently, vehicle performance.”
Think it looks cool? Check it out for yourself. Thoughts or comments? Please reach out to me with the contact form in the above right portion of this page!