Great story in today’s New York Times about maintenance/repair issues with subway elevators in the New York City Transit system. Umibot finds this interesting because it draws attention to the hidden world of attribute data. While spatial elements (station points, entrances, routes, etc…) are valuable, attribute data allow users to act on information–hours of operation, are elevators in operation (today), real-time scheduling/routing, parking facilities, etc…Last week we announced our mass transit initiative, and lots more about that coming soon…

Great Flash graphic showing elevator entrapments and downtime.

Phew! After more than a year in development and two years deep in Umibot’s RAM, today we unveil a grand plan: normalized mass transit data for (today) 53 public transportation systems in the US, Canada and UK. To get here we had to develop other pieces–a data intake platform and a schema. Some more info on all of these:

Web-based Mass Transit Data Intake Platform (no acronym yet) Umibot believes the greatest cost in data collection is identifying and purging the system of dirty data. By auto-validating data at point of input, we’re able to significantly reduce this cost. UMI’s proprietary web-based platform is flexible and captures the vast collection of spatial and attribute data we manage. This includes things like routes, station footprints, exits (you can’t generally exit at a ‘station’), hours of operation, handicap accessibility, elevator location, amenities (retail, bathroom, telephone, etc…) and a great deal more. We then associate this attribute data with the ‘spine’ of spatial data and then compute a graph network, making the data ‘routing ready’ across a variety of platforms.

Transit agencies can take advantage of this platform by using UMI’s infrastructure as a platform to inventory their own data. It’s a well-known fact that transit agencies face bureaucratic, technical and legal challenges to releasing data, and this platform is one more reason for transit agencies to partner with industry to increase data distribution and support increased ridership by driving awareness.

Normalized schema Before we began data collection, a uniform schema that recognizes transit nuances and complexities needed to be developed. For example, scheduling for the London Tube operates on a headway, meaning trains depart every Xish minutes. New York’s MTA operates on a tabular schedule, with scheduled departure times. Sounds like a detail, and it that’s exactly what it is–multiply this nuance 100 times and there’s a great deal of data definition that matters. What we’ve developed is internal to UMI and offers tremendous flexibility to add new mode types (ferry, funicular, etc). It has nothing to do with the output customers receive, and we’ll have more news about that soon.

Coverage The map below reflects current US coverage. Across the 53 transit systems, UMI has defined over 14,000 individual stations and over 100,000 data attributes. Stay tuned for increased coverage, attributes, service delivery and partnerships!

And some fun transit statistics for current coverage:

• 22% of transit stations have bathrooms (they may not be operable/accessible, but they exist)

• 35% of transit stations have dedicated parking

FYI: Wire release

It happened a few weeks ago, but Umibot is just now getting around to posting…UMI is one of 15 companies nominated for the semi-finals of the annual NAVTEQ Global LBS Challenge. We aren’t sure how many entrants there were, but we are privileged to be included in this group.

The UMI application is based on the highly structured data than comprises our Urbanware: Mass Transit data product. Built on the where.com platform, Urban Mapping was able to quickly develop for mobile using uLocate’s location-aware platform.

Finalists will be announced April 2 in Las Vegas during CTIA Wireless.