Optimal MBTA Green Line Stops

Using the k-means clustering algorithm to see how the MBTA could speed up the Green Line.

We were motivated by a belief that the MBTA’s Green Line stops too frequently. This interactive map shows optimal stops based on different calculated k-means scores for clustering the Green Line T stops in Boston. The variant of the clustering algorithm takes into account a given weight for each point, and the weight is a function of two primary characteristics: the stop’s current popularity and the proximity to the nearest alternative stop (measured by walking distance).

Initally, the map shows the current existing Green Line stops (where k = 0). Call those x. By playing with the sliders and changing the value of k, you can see the optimal k stops for that branch, suggesting all other x - k are the ones to eliminate if aiming for that level of scaling back.

     GLB      GLC      GLD      GLE

k Sliders