Public transit carried 9,537.4 million unlinked passenger trips in the United States in 2019, a volume concentrated in motor bus and heavy rail services that connect workers to central business districts and provide mobility to households without a car. Bus and heavy rail together supplied the large majority of those trips, while commuter rail, light rail and a set of smaller modes served narrower travel markets.

Because those two modes dominated the national total, their trajectories largely determined the national trajectory. The sector is also an instrument of social policy, because its riders are disproportionately low-income, people of color and people without access to an automobile, and because transit sustains the dense labor markets in which large cities are most productive. These social functions make the question of how quickly, and for whom, transit demand returns after a disruption a matter of both mobility policy and transport equity.

Ridership had already eroded during the 2010s, before the pandemic, when growth in private vehicle use and ride-hailing services, the suburbanization of jobs and housing, and declining service reliability were associated with sustained losses in several large systems.(1,2)

Longitudinal evidence across twenty-five North American cities attributed ridership growth primarily to service supply and fare policy, both of which tightened before the pandemic,(3) and transportation network companies displaced a measurable share of transit trips during the same period.(4) Scholars warned during those years that the sector faced a structural challenge to its traditional commute-oriented business model,(5) and peer-group comparisons showed that agencies of different sizes faced different ridership conditions.(6)

The COVID-19 pandemic interrupted this trajectory abruptly. Nationwide transit demand in the United States collapsed in the spring of 2020 as stay-at-home orders took effect, with system-level analyses documenting declines of more than seventy percent in the earliest weeks.(7)

Agencies responded with reduced service, rear-door boarding and enhanced cleaning, measures intended to limit contagion rather than to sustain patronage.(8)

Travel surveys confirmed that ridership losses were concentrated among higher-income, white-collar commuters able to telework, while lower-income riders continued to depend on transit for essential trips.(9)

Aggregated studies of the pandemic period estimated that public transit ridership fell more sharply and recovered more slowly than private car travel, a pattern explained by fear of contagion and by the withdrawal of a large share of commute demand.(10)

Recovery after the acute phase has been partial and unequal. Retrospective analyses of United States ridership through 2022 found national volumes still far below pre-pandemic levels, with recovery varying across systems and modes.(11,12)

Historical reviews of disruptive events indicate that transit ridership typically needs years to return to its prior trend after a shock, and the pandemic appears to follow that pattern.(13)

Mode-use evidence indicated that commuter rail and heavy rail lagged bus ridership, a pattern attributed to the persistence of remote work in office-oriented labor markets.(14,15)

Geographic evidence indicated that recovery also varied sharply across United States regions, with some metropolitan areas recovering far faster than others.(16) Shifting attitudes toward car ownership among former riders suggested that part of the loss may become permanent even as fear recedes.(17)

The incomplete recovery carries practical consequences: transit agencies faced a fiscal cliff as federal pandemic relief expired, and the size of the persistent ridership gap determines the scale of service that state and local governments can sustain.

Most of this evidence is drawn from single systems, individual modes or restricted regional samples, and no census-level comparison has quantified recovery simultaneously by transit mode, agency size and region through 2024. These three axes matter because they structure both the pre-pandemic ridership base and the pandemic shock.

Mode separates commute-oriented rail from neighborhood bus and paratransit service, agency size separates large legacy systems from smaller operators with different ridership composition, and region captures differences in employment structure and reopening policy across the country.

The objective of this study was to quantify the uneven recovery of public transit ridership in the United States by transit mode, agency size and region between 2019 and 2024, using the complete monthly ridership records of the Federal Transit Administration. We hypothesized that rail and commuter-oriented modes and the largest agencies would show slower and less complete recovery, whereas demand-responsive services and small agencies would recover more fully.

Contrary to the expectation that transit demand would converge toward its pre-pandemic baseline as economic activity resumed, the census data show a persistent shortfall of roughly one fifth in 2024 and systematic differences across modes, agency sizes and regions.

Recovery was least complete where the 2019 ridership base depended most on downtown commuting, and most complete where service is demand-responsive or serves smaller, less office-oriented markets. The shortfall was not uniform: the spread across modes exceeded fifty percentage points and the spread across regions approached twenty, so a single national recovery figure hides most of the variation.

The mode gradient reproduces, at national scale, the bus-versus-rail split reported in single-system and mode-specific studies. Bus ridership recovered to 83.7% while heavy rail reached 74.9%, consistent with nationwide bus analyses that attributed most of the pandemic loss to mediating factors such as employment, service and infection risk rather than to the mode itself.(20)

The deeper losses in commuter rail and commuter bus match evidence that remote work removed a large share of peak-period, office-bound trips.(14,15) Longitudinal elasticity estimates suggest that ridership became more responsive to service levels after the pandemic, so agencies that cut service during the crisis may have amplified their own losses.(21)

Reviews of the pandemic mobility literature converge on the same gradient, in which rail and commute-oriented services are the most exposed.(22)

Bus rapid transit was the only fixed-route mode to exceed its 2019 baseline: ridership rose from 63.6 million to 69.6 million trips, a result consistent with evidence that service expansion drives ridership gains even when other modes are contracting,(3) and it suggests that bus-priority investment may be more resilient to the telework shock than rail extensions.(14)

The persistence of telework is the most plausible mechanism for the commuter-mode lag. Machine learning analyses of United States travel data identified telework as the strongest single correlate of the transit decline,(15) and stated-preference evidence from commuters in Melbourne documented durable expectations of reduced commuting after the pandemic.(23)

Small and medium cities in Massachusetts showed the same coupling, in which work-from-home intensity predicted whether residents retained transit use.(24)

Panel evidence from Montréal found that the riders who left transit were precisely those whose commute became remote,(25) and focus groups with essential workers described returning to transit out of necessity rather than choice.(26)

Mode-share analyses further warn that the trips shed by transit did not disappear but shifted toward private driving, a substitution that risks entrenching car dependence.(27)

The size gradient ran in the opposite direction to the mode gradient and was quantitatively larger. Small agencies recovered to 91.6%, 13.0 percentage points above large agencies, echoing earlier peer-group work that found ridership determinants differ systematically by agency scale.(6)

Agency surveys in California and nationwide described large operators as more exposed to downtown office demand and to harder service and fare trade-offs during the pandemic, while smaller operators benefited from more stable demand bases.(28,29)

The pattern also mirrors segmentation evidence that the crisis hardened a divide between choice riders and captive riders, the latter concentrated in the agencies that lost the least.(30)

International evidence shows the same uneven shape, which suggests the finding is not an artifact of United States institutions. Bus ridership loss and recovery in Spanish cities displayed the same spatiotemporal unevenness within metropolitan areas,(31) and analyses of Lyon described a slower return in the central business district than in peripheral neighborhoods.(32)

The contrast between agency sizes also has a fiscal dimension. Large agencies depend on fare revenue to fund a larger share of their operating budgets, so a slower ridership recovery translated directly into budget pressure, and surveys of United States transit systems during the pandemic documented the resulting fare and service dilemmas.(28,29)

Smaller agencies, by contrast, often serve captive demand through contracted services whose ridership is less sensitive to the commuter cycle, which insulated them from the sharpest revenue shocks. This fiscal channel means the ridership divide observed here could widen over time if revenue shortfalls force further service cuts at the largest systems, a risk consistent with elasticity estimates showing stronger post-pandemic ridership responses to service reductions.(21)

Regional differences were as wide as the mode differences. The Great Lakes region, at 69.7%, lagged the South Central region, at 88.1%, consistent with regional analyses in California and seven United States regions that tied recovery to employment structure and policy responses.(16,33)

City-level studies in New York, Chicago, Austin and King County found neighborhood-level disparities shaped by income, race and the spatial distribution of essential work.(34-37)

Single-system and cross-city evidence from Lyon, the San Francisco Bay Area and the Bay Area Rapid Transit system showed the same uneven loss and recovery within metropolitan areas.(32,38,39)

Boston's auto-dominated mobility patterns also complicated a return to transit after the pandemic.(40)

The near-complete recovery of demand response, at 97.7%, and the resilience of small agencies point to the role of riders with few alternatives. Essential workers and transit-dependent households sustained ridership through the pandemic and returned more quickly than discretionary riders.(9,41)

This interpretation is reinforced by resilience analyses showing that ridership losses were smallest where dependence on transit was highest,(42,43) and by panel evidence that the riders most likely to abandon transit were those with a car available.(44) Time-series studies caution that recovery speed varies across systems, so national averages conceal substantial local dispersion.(45)

These findings carry implications for transport equity. The modes and agencies that lost the most are also those that serve the most office-oriented, higher-income commute markets, whereas the services that recovered first serve riders with the fewest alternatives. A recovery that restores downtown commuter rail before neighborhood bus and demand response would therefore deepen, rather than narrow, the existing disparities documented in accessibility research.(12)

Policy that prioritizes the return of peak commuters risks leaving behind the riders who never stopped using transit.(41) Equity-oriented recovery therefore requires monitoring the distribution of ridership across modes and neighborhoods alongside the aggregate, because the aggregate can recover while the services used by the most dependent riders continue to lag.

The three axes also interact, and this overlap qualifies a purely additive reading of the results. The largest agencies are disproportionately the operators of heavy and commuter rail, and the regions with the weakest recovery contain several of the largest legacy systems, whereas the strongest recovery regions contain more small and mid-sized operators.

The mode, size and region gradients are therefore not independent, and separating their contributions would require a panel model with agency fixed effects and controls for service supply and local employment. The descriptive census presented here establishes the magnitude of the unevenness; the next analytical step is to decompose it across the three axes.

The practical consequence for planning is that post-pandemic ridership is less predictable than its pre-pandemic trend, and models fitted to pre-2020 data systematically overestimate current demand. Comparative evaluations of time-series methods found that no single specification dominates after the shock, which counsels caution in ridership forecasts used to justify capital investment.(45)

The uneven recovery documented here reinforces that caution: a single national recovery factor would misstate the position of individual modes, agencies and regions.

The study's strength is the census of every active full reporter rather than a sample, which allows the three-way contrast of mode, size and region within a single national frame. The use of the same 2019 fleet measure for all agencies, the same modal taxonomy and the same administrative regions makes the three contrasts directly comparable, and the sensitivity analyses show that the headline result is robust to panel composition and to the inclusion of paratransit modes.

A caution is that part of the 2019 to 2024 change continues a pre-pandemic decline documented through the 2010s,(1,2) so the pandemic recovery gap is best read as a deepening of an existing vulnerability rather than an entirely new trajectory.(3,5)

The uneven recovery is also a preview of how the sector may absorb future shocks. If a remote-work shock that did not damage infrastructure could remove one fifth of ridership for five years, then disruptions that constrain service directly, such as extreme weather or infrastructure failure, may produce longer and more geographically concentrated losses. Resilience planning should therefore account for the structural differences documented here, because the same event will land very differently on a small bus operator than on a large commuter rail system.

Several limitations condition these results. First, the restriction to full reporters excludes small systems, rural and reduced asset reporters, whose monthly data follow different schedules; a sensitivity that includes those reporters would test whether the small-agency advantage extends further down the size distribution.

Second, size classes are terciles of a single fleet measure and do not separate demand-responsive from fixed-route fleets within mixed agencies. Third, the analysis does not adjust for service supply, fares or local employment, so mode and region contrasts may partly reflect service decisions rather than demand alone; future work should estimate panel models that condition on vehicle revenue miles and service hours. Fourth, the monthly series includes adjustments and estimates for incomplete months, which could smooth short-term variation.

Fifth, the ecological design supports description of population-level differences but not causal claims about individual travel behavior. Sixth, the mode grouping treats commuter bus separately from motor bus but does not isolate express bus service, whose riders resemble commuter rail users; finer service typologies could sharpen the mode contrast in future work.

For policy, the uneven pattern suggests that restoring national ridership requires differentiated strategies rather than a single response. Commuter rail and large legacy systems face a structural telework shock and may need to reallocate service toward off-peak and non-commute markets, whereas the resilience of demand response and small agencies indicates that flexible, needs-based service can retain riders through disruptions.

Fare and service levers, which drove ridership before the pandemic,(3) remain the instruments with the strongest empirical support for rebuilding demand.

Regional disparities also counsel against uniform federal performance targets, because a recovery benchmark that is attainable in the South Central region may be unreachable in the Great Lakes under current employment conditions, and equity-oriented planning should weight the needs of transit-dependent riders rather than the return of peak commuters. The findings also bear on the governance question of who should absorb the cost of recovery: funding formulas tied to pre-pandemic ridership would lock in the disparities observed here, whereas mode-sensitive and region-sensitive targets would recognize that a small operator and a large legacy rail system face different demand conditions.

National public transit ridership in the United States remained below its pre-pandemic level through 2024 after an abrupt collapse in 2020. Recovery was least complete among rail and commuter-oriented modes and most complete among demand-responsive and bus rapid transit services. Recovery was also inversely related to agency size, with small agencies recovering more fully than large agencies. Recovery varied widely across Federal Transit Administration regions, with the weakest outcomes in the Great Lakes and the strongest in the South Central region.