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Tricky Transit Data. Is Transiki the Answer?

I have always loved working with transit data. It is so similar to road data like TIGER or OSM that we work with so often, but at the same time...

I have always loved working with transit data. It is so similar to road data like TIGER or OSM that we work with so often, but at the same time it is so much different. While a road network stays the same throughout time, a transit network can change depending on the time of day, or day of the week. This makes working with transit data a much more difficult problem than working with street data, in fact transit networks generally include street networks within them!

Services on top of transit data are somewhat more rare than road network services. Google has had Google Transit for several years now and is slowly expanding to include more cities. Microsoft recently added transit data for a few cities to Bing Maps. These services are great, I honestly don’t think I would be capable of using transit anymore without Google Transit on my iPhone, but what about the data?

Google has opened the General Transit Feed Specification interchange format they use to load data from transit companies into their system, and there are some efforts to make public GTFS data easy to get, as well as a number of very powerful tools that can suck in a GTFS dataset along with an Open Street Map road network for analysis and routing (BTW, GTFS is just a standard schema represented in CSV files, so working with it in FME is very easy too – kudos to Michael Grant of BC Transit who had a great presentation on this topic last week).

Transiki is a new project from Steve Coast (yes, the same Steve Coast who started OpenStreetMap) to bring the OSM model to transit data. Steve says he had the idea when Google Transit failed him, leaving him on a platform waiting for a train that did not exist, except in Google’s datasets. With Transiki, Steve may still have been stranded, but at least he could update the dataset to prevent others from being in the same situation. A wiki-esque transit network could also bring the real-time transit data in The Bay Area or Portland to other cities through a Waze like application. The uses for a large, free transit data network are almost limitless.

Will Transiki prove to be the answer to all of our transit woes? The short answer is nobody knows for certain. As with all crowdsourcing initiatives, only time will tell if the support from the community remains and grows. Given the huge success of OpenStreetMap, I am excited and hopeful. To get involved with Transiki, come join me on the mailing list!

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