The MBTA usually evaluates bus routes on the “route” level, with the entire route being considered as one unit. This makes sense for most of the MBTA bus routes, which function as “feeder” routes that take riders to a transfer connection at one end of the route. For these routes, it makes sense to consider the route as an entity, because ridership tends to be homogenous, with the same group of riders boarding the vehicle and traveling the length of the route.
However, there are several bus routes that, by design, are likely to have a lot of rider turnover. Long cross-town routes with multiple transfer/connection options tend to be especially likely to have different riders along the routes, with relatively few people riding the entire length of the route. In terms of design and function, these routes function more like separate services than as a coherent entity: for example, very few people travel the entire length of routes 1, 66, and 86.
We wanted to evaluate whether it makes sense to treat these routes as a single service, or if it would be more meaningful to evaluate them at a route segment level that would more closely resemble the service provided on our other bus routes.
We took route 1 as an example, as its ridership is high and it travels a long distance from Cambridge to Nubian Square. The boardings and alightings are distributed fairly well along the route (not, for instance, with most of the riders boarding at one end and alighting at the other). Additionally, the neighborhoods through which this route runs are very distinct in terms of population demographics, and there are multiple transfer points to rapid transit lines. This led us to hypothesize that there are certain zones/stops on the route where there could be a clear shift in the composition of riders as people board and alight at different stops.
We used the MBTA Rider Census data to create a “line profile” of the demographics along the bus route. To have a high-level overview of the data, all the stops on the route were categorized into 3 zones- north zone, transit core, and south zone. We thought we might be able to segment the route and establish different demographics for each “zone.” Each of the zones had a sufficient sample from our Rider Census to effectively count as separately evaluable (if each of these segments were their own route, then we could compare them to each other and to other routes on our system).
We chose to look at minority status information because we had a high response rate for that question and it was easy to categorize riders as either “minority” or “nonminority” based on their response to the survey. Route 1’s bus ridership overall is 37% minority.
We dug into the data by looking into the boarding/alighting behavior of riders in these zones. The graph below depicts the average daily load profile (from automated passenger counters) of the Route 1, heading southbound on a weekday. The ‘load’ of a bus is defined as the number of people who are on the bus at a given stop. The stops of interest are those where we see large peaks and dips in the number of riders. Note the three large “alighting” stops, indicating turnover of riders at those locations.
We then proceeded to recreate a similar load profile chart for our two demographic groups of interest. We arrived at the visual below that shows the behavior of minority and non-minority riders on route 1 (click to enlarge).
As noted above, the ridership of Route 1 as a whole is 37% minority (excluding respondents who did not report their minority status); if we examine the graph, we can see that there are some stops where there are sudden dips and peaks in the number of riders in each demographic group. We also see that there is a small population of riders who fall in the ‘unknown’ group. This refers to the portion of survey respondents who chose not to disclose their Minority/Non-Minority status.
At the bus stop at Mass Ave and Pearl St in Cambridge, we see a small peak in the percentage of Minority riders. At the stop at Mass Ave and Harrison Ave, we see a distinct increase in Minority riders with a simultaneous decline in Non-minority riders. This stop falls in the south zone, near the Lower Roxbury neighborhood of Boston. In the transit core, we see that the trend line remains almost constant for both categories. This could be because the stops in the transit core are all major bus stops, with plenty of connections to other buses and light rail (at the Symphony and Hynes stations). Riders would be typically making transfers here, either onto the route 1 or to some other service.
If we were to evaluate each of the segments separately, the North Segment would be similar to Route 57; the Transit Core Segment would be similar to the 39; and the South Segment would be similar to other routes that serve the area near Nubian Square (8, 15). Percent minority ridership is spatially distributed, so it is not surprising that the MBTA routes that cross multiple distinct neighborhoods and have many points of transfer or destination, percent minority ridership changes along with the path of the route. And in practice, all parts of Route 1 would likely still be classified as “minority” bus routes for Title VI purposes. However, this pattern provides further evidence that Route 1 functions as separate segments rather than as a single entity.
In terms of certain measures of service quality, riders may have different experiences on different segments of Route 1 (or any long route). For example, one section of a route may have frequent crowding as the route approaches a transfer point, while another segment would usually be less crowded. Since an individual rider is unlikely to travel across multiple segments, their experience is best reflected by the quality of service on the segment they do travel rather than on the route as a whole. Where there is sufficient rider turnover in the middle of a route and if data exists to make evaluation feasible, it may make sense to evaluate performance in segments.
Other routes that we expect to have distinct segments would be the CT1, CT2, and CT3, along with 39, 66, and 86.
The next steps to moving to this reporting structure would be to analyze all the routes to see which ones should be subject to the segmentation treatment; and to investigate whether the measures of interest, like crowding, can be adjusted to work on the segment level.