Home

Behind the scenes with MBTA data.

Why Evaluate MBTA Coverage?

A key component of transit service planning is offering service to the largest number of people possible. Understanding how much of the population the MBTA currently covers, and where that population is located, is important to understanding how well the T is serving its constituents and where the MBTA should expand or modify its service. In 2017 the MBTA set coverage standards as part of its Service Delivery Policy

One application of the coverage evaluation is the Better Bus Project, an ongoing initiative to improve bus service. As the MBTA focuses on bus planning, it is important to be able to evaluate the coverage impact of proposed changes to bus stops or routes. Automating the process of evaluating coverage allows for frequency and consistency in the evaluation process, so that whenever changes are proposed, the T can quickly assess their coverage impact and compare their impact to other proposed changes. 

Coverage Automation Tool Overview

To automate the coverage evaluation process, we (the Office of Performance Management and Innovation at the T) created a coverage evaluation tool using ArcGIS ModelBuilder. This tool uses census population data and location data for transit stops to compute the population of the area within walking distance of MBTA service. The model further calculates the percentage of the population covered by MBTA service in MBTA cities and towns by dividing the population walking distance to transit stops by the total population within the cities and towns.  This post will walk you through the MBTA’s 2017 base coverage analysis. Base coverage is the percent of the total population within the MBTA cities and towns living .5 miles walking distance away from any MBTA operated or subsidized transit stop or station, regardless of the frequency or span of service provided.

Data Inputs for 2017 Base Coverage Analysis

Our first step to evaluating base coverage was downloading the most reliable data available for our analysis. This data includes:

  • All MBTA stops in the fall of 2017, downloaded as text files from GTFS
  • Route data (shapefiles) for MBTA privately operated/subsidized routes, for example, the Lexpress
  • American Community Survey Total Population 2016 5-Year Total Population Estimates downloaded from American FactFinder
  • TIGER census block groups for the seven counties served by MBTA service
  • MBTA Towns from MassGIS
  • The area of water in Massachusetts
  • MassDOT Road Inventory 2017 Road Network

Methodology

This analysis consisted of three components. We automated the process of finding:

  1. The area and population of all block groups within MBTA Cities & Towns
  2. The area within walking distance from MBTA transit stops and stations
  3. The population living a .5 mile walking distance from MBTA transit stops and stations, calculated as the percentage of the block group population within the walkshed, assuming that the population is evenly distributed. 

Step 1: Finding the Area and Total Population of all MBTA Cities and Towns at Block Group Level

As block group population data comes as a spreadsheet from the American Community Survey, we first transformed block group population data into a spatial dataset. To do this, we joined ACS block group population data with TIGER block group geography. As all block group data is at the census level, we then clipped block group polygons to the shape of MBTA cities and towns. 

This step resulted in tiny slivers, as the block group polygon boundaries do not precisely overlap with the MBTA cities and town boundaries. To delete these slivers, we created a buffer .05 miles around the boundary of the MBTA cities and towns polygon and deleted all census block polygons located completely within this buffer. No real census block group is that small, so we knew all block groups deleted in this process were slivers. 

After spatially displaying the population data at block group level, we then calculated the area of each block group in square miles. To get a better estimate of the area where people actually live, we erased water features first, then calculated the area. 

Step 2: Finding the Area Within Walking Distance from MBTA Transit Stops and Stations

To calculate the base coverage area, first we downloaded all GTFS MBTA transit stops for the fall of 2017 as a text file. Next, we converted the stops from the text file into points using ESRI’s Display GTFS Stops tool. GTFS stops include all MBTA operated bus routes, but do not include flag stops along privately operated routes subsidized by the MBTA, like the Lexpress bus in Lexington. We estimated the location of these stops to be at all road intersections along the subsidized routes. To do this, we first created a network dataset using the MassDOT Road Inventory 2017 file. The resulting network dataset included a streets layer and a road junctions layer. We estimated the location of flag stops by selecting the road junctions 50 feet or less from the subsidized service routes. Our final stop file for base coverage included the GTFS Stops merged with the selected road junctions stops. 

We used ArcGIS Network Analyst to calculate the area a .5 mile walking distance along Massachusetts roads from all MBTA stops and stations. We used the Road Inventory network dataset mentioned previously as the network dataset for the analysis and input the stops as facilities into network analyst. Our network analysis resulted in a layer of dissolved polygons around every MBTA stop or station. This is the MBTA 2017 coverage area.  

Step 3: Finding the Population Living within the MBTA Base Coverage Area 

To find the total population in our coverage area, we clipped the census block polygons with the population and area attribute created in step 1 to the coverage area created in step 2. We then recalculated the area of the census block polygons after they were clipped to get the area of the census block polygons covered by service. We then found the percentage of the area of each census block group covered by our coverage area by dividing the area covered by service by the total area of each block group. Assuming even distribution, we calculated the percentage of the population we covered in each block group as a measure of the area we cover, divided by the total area multiplied by the total population of the census block group. To find the total population in our coverage area, we summed up the total population covered in all census block groups. The final coverage percentage was calculated as the coverage population divided by the total service area population. 

The Base Coverage Model

The model to automate the coverage analysis is shown below. (Click to enlarge)

Flow Chart from ArcMap ModelBuilder showing the coverage model

Map of MBTA Base Coverage

Map of MBTA base coverage

As seen in the base coverage map, we found the coverage area using dissolved walkshed polygons. The polygons are jagged due to the location of walkable roads near transit stops. The area covered by MBTA service is efficiently located over the highest density parts of the service area, so though service only covers around half of the service area, it covers around 80% of the total population. 

Conclusion: Importance in Service Planning

As the MBTA strives to improve the service provided to its constituents living within its service area, the coverage tool can evaluate how proposed bus stop and route changes will affect the number of people receiving service. Further, the T can use different inputs into the coverage metric to understand how it is performing on different types of coverage. For example, we can use stops receiving high frequency service to see what percentage of our population receives frequent service, or we can look at vulnerable populations, instead of total populations, to see the number of vulnerable people covered by MBTA service. Related to the Better Bus Project, the T can see the percentage of the population covered by varying levels of bus service to see what populations receive different types of service. The coverage tool is flexible, quick and more reliable than conducting manual analyses, and will allow the T to continue to evaluate the quality and impact of its service improvements.