The new TransitScore algorithm is based on three major parameters:
1. How close are you to transit (distance)
2. How readily available is it? (frequency)
3. Can it get to where I need to go? (this is the hard one)
While the first two criteria can be easily satisfied with existing geolocation and open transit data, the third parameter is hardest to quantify. The solution at the heart of TransitScore is to analyze the number of transit options within a half mile radius, the frequency of service, and the total number of businesses accessible by those lines (including calculating WalkScore at all stops along the route). For a discussion of the complete algorithm click here.
While the combination of these factors works well at a base level, ideally the TransitScore would depend on your destination. For example, if you live in a dense, walkable area that is well-served by transit, but those transit lines fan out into a low-density and under-served region, then the resulting transit score would be lower than a similar area with lines only extending into a mid or high density area. Furthermore, what if you live at the top of a steep hill or mountain and all the transit options are at the bottom? Geolocation would still indicate a high transit score.
WalkScore has anticipated this problem with a second new feature: WalkScore Commute. Accessible from the "Commute" tab on the results page this feature lets you use your starting point and specify an end point. The destination could be a job location or any other destination for that matter. The result gives a map with walking, cycling, driving, and transit durations (a la google maps) and an elevation graph such as that used by Veloroutes.org for several years.
Interestingly, given that the start point and end point are finalized, the commute function does not give a definitive walk or transit score. Since the algorithm is compromised by the aforementioned limitations, it seems that the lack of a Commute TransitScore/WalkScore is a missed opportunity.
In any case, the inclusion of full transit analyses into WalkScore is a major improvement. Since its inception WalkScore has caught the eye of the media and has become an essential instrument for emphasizing the power of walkability in the real estate market (pdf link). With the greying of the baby boomer generation, the importance of walkability cannot be understated and the importance of quantifying walkability has been recognized by the Robert Wood Johnson Foundation.
So where do we go from here? WalkScore is enabling web developers to embed maps, access data, and use their API. It is my hope that this move will make walkability as ubiquitous as Google Maps has made geographic data. In the near future WalkScore will be releasing open source code for their algorithm. It is my hope that this action will drive programmers to develop the next generation of wayfinding that will not only give you options for modes of travel, but incorporate externalities and facilitate spur-of-the moment side trips via mobile application. Since many people do not currently use transit due to its complexity, the seamless integration of WalkScore with mapping software and social wayfinding services such as Foursquare and Yelp has the potential to drive a large increase in ridership. Such a sea change will have the related effect of moving walkability from the realm of theory and analysis and into everyday life.