If you’re in the business of making transportation better—like my colleagues and I at Cambridge Systematics—you know that there has been rising concern for years about ebbing ridership on many public transit systems across the country. Recently, it seems, publications have been reporting on the issue with greater depth and frequency—even major consumer outlets, such as The Washington Posti, are riding this train—both sharpening the focus on the problem and possibly promulgating narrow views of what really is a set of often overlapping but sometimes disparate challenges.
In fact, transit ridership trends differ not only by region, but also by travel markets within regions, trip purpose, and population segments. So it is critical to design context-specific solutions. For example, the Institute of Transportation Studies (ITS), in its January 2018 report prepared for the Southern California Association of Governments (SCAG),ii cites numerous factors in the six-county SCAG region’s ridership decline from 2012 to 2016. Increased car ownership is one, but that factor also has a population segment element within it: lower-income people, and especially foreign-born residents, comprise much of the frequent rider group, the report explains, and these households have attained new car ownership faster than average residents of the region.
While the ITS report on Southern California finds that fuel prices, changes in transit service, and rideshare use likely are not driving the decline there, a nationwide ITS publicationiii from October 2017 reports that shared-mobility likely is responsible for a 6% net reduction in use of bus services and a 3% net reduction in light rail use among Americans; yet for commuter rail, where ridesharing emerges as complementary rather than a substitute mode, it contributes to a 3% increase in use among Americans. The differences between these data and those in the SCAG report indicate that we cannot assume findings for Americans overall will provide insight to one particular region and its various populations.
One month later, TransitCenter published a guest blog on Portland, Oregon’s TriMet ridership loss.iv TriMet’s analysis revealed that, as in Southern California, for Portland the greatest driver of ridership decline related to the changes in travel behavior of the area’s low-income earning population. But unlike in the SCAG study, the change in behavior resulted primarily from the move of this population from inner city neighborhoods to the less transit-rich first ring suburbs—not directly from increased car ownership. High-income earners who were not frequent transit users had replaced the low-income earners in transit-rich neighborhoods. The travel patterns and preferences of TriMet’s major transit users no longer aligned with TriMet’s transit system.
In Washington, DC, the recent Washington Post article identifies a different set of factors as leading the way to decreased transit ridership. It states that Washington’s Metro attributes 30% of its ridership declines to system reliability issues, with a combination of teleworking, a reduced federal workforce, and ride-hailing services accounting for the rest.
In other words, a complex set of influences are in play, affecting individual transit systems in differing ways and to varying degrees. And while the media, along with transit agencies, political leaders and some consultants are offering up a variety of reasons, rigorous research such as the studies I have cited here are just beginning to evaluate and quantify the major drivers. It may be tempting to assert that the culprit is Transportation Network Companies (TNCs, or ride-hailing services), increased car ownership, aging millennials moving to the suburbs, lower gas prices, or transit service problems, but the only true answer that covers every market is “it depends.” Which means we must do the research to truly understand these important nuances. Because if we get it wrong, transit agencies will spend hard-fought dollars on failed strategies.
At the same time, the pure quantity of new technology options for transit—such as TNCs and an array of applications to help riders navigate transit services and operators pull effective data from them—is creating a tremendous amount of noise. These technologies may be contributing to the decline, and they may offer options to help make transit more accessible, but we have to ensure the noise does not drown out a laser focus on uncovering and directly addressing the issues.
The Washington Post article cites the handful of exceptions to the ridership trend, noting that expanding coverage and service, or overhauling the system, has brought ridership back up. In particular, it notes Houston’s decision to change rapidly from a hub-and-spoke design focused on downtown to a grid approach that equalizes service among various portions of the city. Ridership shot up in the first weekend of the new routes in 2015, reversing a decade-plus decline, by analyzing demographic and ridership data and then concentrating on service to maximize ridership rather than focusing on maximum coverage.
Once we do the local research, we can know what factors are in play, and which are most influential. For example, the results of a market research study we did in one major transit market showed:
Relocation of residences and jobs were the key factors for decreasing bus and rail ridership.
The city had experienced significant population loss, particularly in certain neighborhoods.
TNCs had emerged as a strong alternative to transit, particularly during times when service is reduced.
Riders expressed personal safety concerns, especially during off-peak periods.
Yet some of the system experienced ridership growth, particularly where there was an influx of younger, more affluent professionals.
These factors co-existed with reductions in state funding and resulting fare increases, as well as obstacles to maintaining reliable operations, leading to a range of targeted challenges for regional planners and operators.
Solving these requires a blend of integrated strategies that are at once established and yet unique to each region’s needs. This is why our transportation consultants and data modelers follow a data-intensive and market-oriented approach that focuses on rigorous analysis and incorporates operational data such as detailed passenger counts, fare card transactions and transit agencies’ schedule and associated geographic information. They combine these with innovative data sources, such as location-based services, open-trip planners and custom designed surveys, to identify key market segments, examine their preferences and attitudes, and quantify the competitiveness of serving their needs through transit.
In a large western U.S. market, for example, we are taking this approach with an agency that not only is ready and able to embark on the study of these data but also is willing to adopt market-driven policies and strategies to optimize service across its region.
As agencies grapple with the realities of an evolving transportation landscape, I suggest they keep in mind five key points:
We have a data-surplus environment for evaluating travel movement; the question to ask is: How can we pull meaningful and actionable insights from these data?
There are new means to engage potential riders for feedback, from social media, to in-app engagement, to website interactions. Because they operate in a competitive market environment, transit agencies must find ways to reach and attract customers.
Agencies need to identify their most attractive routes, understand what makes them that way, and fight hard to keep them. Because once those are lost, the battle becomes much harder.
The factors impacting transit ridership and strategies for enhancing ridership are nuanced. A one-size-fits-all approach is not likely to be successful.
With regional demographics, travel behavior and mobility options shifting at rapid rates, agencies must adopt more flexible strategies and agile methods for alternatives analysis, policy creation and service adoption.
That is a lot to juggle, so it is critical that transportation consultants can bring all of these capabilities to the table for their transit clients, along with an objective, strategic perspective that can filter out noisy data and trendy conclusions to focus on collaborative, innovative and data-driven solutions.
At Cambridge Systematics, we are investing in the development of new methods and tools to convert the burgeoning data into insights; we design customer engagement methods and measure their effectiveness; we develop software that helps transit agencies meet their goals and serve their riders; and we do all of this with research, policy and planning at our core. It takes this type of comprehensive, integrated approach to meet the demands of an issue as complex and diverse as declining transit ridership.
This piece originally was posted to LinkedIn.
i Siddiqui, Faiz (March 24, 2018) Falling transit ridership poses an ‘emergency’ for cities, experts fear. The Washington Post
ii Manville, Michael and Brian D Taylor and Evelyn Blumenberg (January 2018) Falling Transit Ridership: California and Southern California. Institute of Transportation Studies Mobility Research Program, University of California.
iii Clewlow, Regina R and Gouri S. Mishra (2017) Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States. Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-17-07
iv Mills, Tom and Madeline Steele (November 14, 2017) In Portland, Economic Displacement May Be a Drive of Transit Ridership Loss, http://transitcenter.org/2017/11/14/in-portland-economic-displacement-may-be-a-driver-of-transit-ridership-loss/