This year, MassDOT released new Statewide Bicycle and Pedestrian Plans with the goal of increasing the comfort, safety, and convenience of biking and walking for all people. The plans lay out a vision that all people, including new riders, will be able to take at least some of their reasonably distanced “everyday trips”, or non-recreational trips, on high-comfort bicycle and pedestrian infrastructure. The plans define a reasonable distance as a half-mile for walking trips, three miles for non-work bicycle trips, and six miles for bicycle commute trips.
One key piece of both plans is making it easier for people to access transit. Active modes (like biking and walking) should be viable options for people accessing a transit stations.
To measure the success of the plan at making active modes viable, the plans published the following metrics:
- Percentage of trips under 6 miles beginning or ending at a transit station that were made by bike. (Bicycle Plan)
- Percentage of short trips beginning or ending at a transit station that were made by walking. (Pedestrian Plan)
This analysis focuses on the modes of transportation that passengers used to access major MBTA stations. While the plans reference the state as a whole, the MBTA is the only agency for which we have data on access modes. Additionally, it is unlikely that any mode besides walking is used to access neighborhood bus stops because 96% of bus trips are part of a journey that the person began by walking (Systemwide Passenger Survey) and the MBTA is the only agency to offer non-bus service.
This analysis relied heavily on the MBTA’s 2017 Systemwide Passenger Survey, which asked riders on all modes and lines to report on a specific trip taken on the system. Respondents identified their approximate origin, the station they accessed, and how they arrived at the station.
The data sources used for this analysis are as follows: the 2017 MBTA Systemwide Passenger Survey, the MBTA GTFS stops file, and the MassGIS Seaports file.
It is important to note a few major decisions made pre-analysis that affected the results. We used the distance to the closest station even if that was not the station accessed because it was more philosophically in line with giving people the option to bike or walk. For example, if someone is driving to a farther Commuter Rail station because of parking constraints, parking costs, or traffic, we do not necessarily want to ensure them a direct biking path to a farther station. By providing safe and comfortable infrastructure to their nearest Commuter Rail station, we are giving them an option to use active transportation, which negates the parking issues that force them to drive farther. Additionally, we used a three-mile threshold instead of the original six-mile threshold published in the bicycle plan for this analysis to account for a realistic proportion of travel time dedicated to biking for a trip that also includes transit. Below are the steps we took to produce this analysis.
The analysis process itself was relatively simple. We started by mapping all of the applicable MBTA Rapid Transit, Commuter Rail, Silver Line, and Ferry stations. Using ArcGIS’s network analyst feature, we created half-mile and three mile buffers around the station along the road network.
We then looked at the responses from the Systemwide Passenger Survey, and identified the relevant trips that started with one of the above modes. From that filtered dataset, we geocoded the origins of reported trips and identified which trips began within a “walkable” distance, and which trips began within a “bikeable” distance. The map below shows a sample of Wakefield residents and their chosen access modes. Those inside the red outline are within a half-mile of the Wakefield Commuter Rail station.
Nearly 95% of trips that start within a half-mile of a transit station begin with walking. Only about 5% happen with a car-based mode, including carpooling and ridesharing.
When you look at trips that start within a bikeable distance of transit (between a half-mile and three miles), nearly half are made walking. However, only 4% are made on bike. Just over 45% are made in a car. There is a big opportunity for all agencies who own and operate roads to improve street design and create bicycle infrastructure to help make some of those people who choose to drive switch over to a 15-minute or shorter bike ride instead.
|Mode Used||Trips starting within half-mile (walkable)||Trips starting between half-mile and three miles (bikeable)|
The Bicycle and Pedestrian plans put a big focus on equity in order to ensure that the opportunities and benefits of bicycling and walking are equitably distributed. They identify several specific populations of interest. During metric development, we incorporated equity checks into every measure to understand how well different populations are served by bicycle and pedestrian infrastructure in the state. As a part of this analysis, we also looked at how respondents belonging to equity groups accessed transit as well. We analyzed the access patterns for people of color, low income people, people under 18 and over 65, and no-car households. Due to sample size issues in the Passenger Survey, we were unable to compile access mode results for people with limited English proficiency or people with disabilities. The table below summarizes the results of our equity analysis for walkable distances (0 – 0.5 miles).
|Mode||Minority||Low-Income||No Car||65 or over & under 18|
The equity analysis results for some other metrics in the Bicycle and Pedestrian Plans are relatively easy to interpret — for example, if there are fewer sidewalks in low-income neighborhoods, we view that result as inequitable. However, these results are a bit more complicated. When a higher percentage of people in low-income communities walk to transit, it is not necessarily is a sign of transportation equity, but could be a reflection of fewer opportunities for travelers who may sometimes legitimately need to access transit without walking. Treating these results as providing context allows us to interpret implications one by one, for example indicating a greater need for pedestrian infrastructure that connects low-income neighborhoods to transit.
There are several other measures that we are using as a part of this project; we wanted to give an example of one of the simple ones to illustrate the underlying complexity of measuring access to transit. Future blog posts will cover some of these additional measures.