Transit ridership is often discussed as a “national trend” - but each transit agency has its own ridership story. Transit Insights can help you tell those stories. We’ll show you how in the following slides. (Note: This slideshow displays best on large screens.)
To view data from multiple agencies, select the “Compare” function, then click on the agencies you want to include. The data from those agencies will display on the left side panel as “Sparklines” -- graphs showing trends over time.
The chart below shows ridership changes at six agencies: Houston Metro, Baltimore’s MTA, LA Metro, Seattle’s King County Metro, WMATA in DC, and DART in Dallas.
You can also choose to display data in a “Parallel Coordinate Plot,” which compares multiple indicators from different agencies over time. So for the same six agencies, we can view the scale of change in ridership, operating budgets, fares, and other indicators.
For instance, we can see that between 2012 and 2017, bus ridership grew in King County and Houston, where recent bus network redesigns improved service frequency on high-demand corridors. Network redesigns must be carefully implemented and may take time to show results; the Maryland Transit Administration redesigned Baltimore’s network in 2017 but has not grown bus ridership yet.
In addition to ridership comparisons, you can use Transit Insights to compare indicators of each agency’s operations. For instance, you can see that operating expenses have recently increased at LA Metro and King County Metro, two agencies where residents recently voted to increase transit funding.
By clicking a circle on the Transit Insights map, you can view neighborhood-level demographic changes within regions. (You must deselect the “Compare” function first.) The closer you zoom in, the more detail you’ll see about where transit routes run. This feature enables you to overlay transit routes with changes in population and job density.
In Dallas, for instance, you can see DART’s 2012 rail expansion goes through areas with slow or negative growth, which helps explain paltry rail ridership growth. Note: The map is showing change in population and job density from 2010-2012.
In the local map view, you can use the “Filter tracts” function to exclusively display neighborhoods within walking distance of frequent transit routes. This can more clearly convey the link between demographic change and access to transit.
For example, in Portland, higher-income people have moved to transit-rich neighborhoods, pushing lower-income people, who are more frequent transit riders, farther from transit. Note: The map is showing change in median household income from 2010-2016.