A Comprehensive Transit Accessibility and Equity Dashboard

Findings The TransitCenter Equity Dashboard tracks how well public transit systems in seven densely populated urban regions in the United States serve their riders and how changes to transit service affect riders over space, time, and cost constraints. The dashboard presents a series of charts and interactive maps that can be used to evaluate variations in transit accessibility and equity. It was created using publicly available data and primarily open-source software. All measures can be accessed by users seeking to conduct their own analyses. Results demonstrate differences in agency responses to COVID-19 as well as baseline transit service levels provided to different demographic groups.


Findings
The TransitCenter Equity Dashboard tracks how well public transit systems in seven densely populated urban regions in the United States serve their riders and how changes to transit service affect riders over space, time, and cost constraints. The dashboard presents a series of charts and interactive maps that can be used to evaluate variations in transit accessibility and equity. It was created using publicly available data and primarily open-source software. All measures can be accessed by users seeking to conduct their own analyses. Results demonstrate differences in agency responses to COVID-19 as well as baseline transit service levels provided to different demographic groups.

Questions
The TransitCenter Equity Dashboard 1 is a database and interactive visualization platform detailing monthly transit accessibility 2 and equity measures for the areas of Boston, Chicago, Los Angeles, New York City, Philadelphia, San Francisco-Oakland, and Washington D.C. from February 2020 onward. We developed this database and platform to investigate the following questions: • How does transit accessibility vary within these cities, between population groups, with constraints on fares, and over time?
• How do transit service changes, like those undertaken in response to the COVID-19 pandemic, affect transit accessibility and equity in these cities? To provide a comparison of fare-constrained and fare-unconstrained transit trips, fares are estimated by first using OTP to generate detailed transit itineraries for the shortest trip between all census tracts in each region. These itineraries are passed to a fare calculator 4 , which estimates the cost of each itinerary using a database of manually calibrated rules representing fare information and agreements across multiple transit agencies in the same region. The fastest transit travel time between each pair of tracts in the region and its accompanying fare is then estimated for two different transit networks: one that includes only "low-cost" modes (local bus plus comparable-cost modes) and the other including all modes (all available public transit options). When determining the fare-constrained travel time between two locations (e.g. jobs reachable subject to a $5 total fare), we select the shortest of the two travel times that meets the fare constraint.
The dashboard also includes transit reliability measures that represent the ontime performance of transit vehicles, calculated as the fraction of vehicles that are between 1 minute early and 5 minutes late. Transit reliability is included for transit operators that report the status of vehicle delays in their real-time GTFS feeds.
Viewing and analysis of the results is implemented via an interactive web visualization platform built using open-source Python and JavaScript tools. Web mapping is done via Leaflet (Agafonkin 2011), and the populationweighted summary charts for individual regions are produced dynamically using the data visualization library D3 (Bostock 2012 Figure 1. Travel times to the 3rd closest grocery store on weekend mornings in the Philadelphia Metropolitan Statistical Area during the week of April 19, 2020, overlaid with dots representing 50 essential workers.

Findings
Each region's mapping application displays the spatial distribution of relative transit accessibility. These maps provide insights into the relationship between transit infrastructure, land use, density, and access and facilitate comparisons between populations and neighborhoods in a region. A user can visualize the effects of density, transit, and the spatial distribution of various socioeconomic groups on transit accessibility at the same time. For example, Figure 1 illustrates how a viewer can compare the spatial distribution of travel times to grocery stores, major transit lines, and the location of essential workers. In Figure 2, transit service intensity across the entire region and multiple agencies can be visualized and inspected by agencies and advocates to identify areas with less transit service. The mapping platform allows visitors to customize which data to view, including access measures, date, time of day/week, region subset, fare restrictions, and comparisons with automobile access.
Each region also has a story page containing dynamic charts with specific measures, allowing users to learn about key accessibility and equity issues in the chosen region. For example, Figure 3 compares population-weighted average travel times to key destinations in Chicago for residents living below the poverty line. It highlights that travel times are substantially longer by transit compared to by auto. A time-series plot of access to jobs over time illustrates the impact of service changes on job access for different population groups in Washington D.C., drawing attention to lower levels of access to jobs by Asian, Black, and Latinx transit riders when compared to white transit riders (  The platform can be used by transit agencies, advocates, decision makers, and residents to identify transit equity issues in their regions and compare across geographical areas. The data shown on the dashboard will be updated periodically so that users can continue to trends and progress. Users can also access data for their own analysis by downloading data shown in the map or posted on each region's download page. Aside from the auto travel times, all code and data are open source, meaning the majority of the the dashboard can also be reproduced in other regions.

A Comprehensive Transit Accessibility and Equity Dashboard
Findings