If you just want to know ridership patterns on a more general level – such as how many riders board and alight at each stop – we already have much more substantial data for that. The MBTA publishes detailed bus ridership data for each fall since 2016, with breakdowns by bus route, bus stops, and times of the day (or, if you want, by each trip). The data for around 2016-17 is also visualized in MBTA’s Bus Route Profiles.

So why am I even using this highly labor-intensive method with much less data and much lower accuracy?

Because I want to understand how riders use the buses, from where to where. And ideally, for what.

Look at the 66’s ridership profile above. We can see a lot of people boarding at Harvard, and then many people get on and off around Union Square Allston, Coolidge Corner, Longwood Medical Area (LMA), Roxbury Crossing, and finally a lot of people alight at Nubian. But this can imply many different things:

• Are riders boarding at Harvard taking it all the way to LMA or the Orange Line? Or are they getting off at Allston and Coolidge Corner?
  • If it’s the latter, do they make a transfer on the B, C and even D branches? Or do they live along the 66’s route?
• Are Allston residents using the 66 for inter-neighborhood travel to Coolidge Corner? Or to LMA for work? Or to Harvard and Roxbury Crossing?
  • If it’s the latter, are they connecting to the Red and Orange lines towards downtown (or even outbound)? Or is Harvard their destination – for example, either shopping at Harvard Square, or working or studying at Harvard University?

While there is some very useful existing data that hint at these questions – US Census OnTheMap provides origin-destination employment data, and the Bus Network Redesign (BNRD) provided origin-destination data on travel demands on all modes – I think they fall just short of my full list of curiosities specific to each bus route. My initiative aims to address these questions.

Currently, I focus on a small subset of bus routes (see sections below) due to time constraints. These are also the ones that I am especially curious about, and/or ones whose full ridership patterns are hard to capture using summary statistics like the bus route profiles. Many of them are crosstown routes (unsurprisingly).

For each bus route, I plan to take at least 3 trips (more to be added over time) during each of the three general time periods of a weekday: AM peak, midday and PM peak. (I have not yet decided if I will do weekends.) While I will focus on directions with high ridership (typically AM peak inbound and PM peak outbound), I will do both directions if the route has strong bidirectional demand. The specific times during each period will be chosen to capture the time with the greatest ridership (or a representative one for midday), using the bus route profiles as guidance.

Each trip will be from the first stop to the last. During the trip, I will keep track of each and every passenger on the bus, and record the bus stops at which they got on and off the bus. All data will be anonymized when shared on this website.

It should be stressed that due to the limited sample size, BRFT is not meant to be an accurate measure of the absolute ridership numbers (i.e. how many riders there are), which are best obtained using MBTA’s data. Rather, it aims to provide a coarse estimate on the relative distributions of trips. Also, on heavily utilized bus routes (many of which are on this list), there will likely be inaccuracies as I may not be able to keep track of all passengers.