Working from home and the decline of transit commuting in US metropolitan areas, 2019-2023

INTRODUCTION

Public transportation carries a small but strategically important share of the journey to work in the United States. In the years before the pandemic, roughly one in twenty workers commuted by public transit, and the share was far higher in the largest metropolitan areas and among lower-income, immigrant and minority workers who depend on transit for access to employment. (1,2)

Because transit commuting is concentrated in a limited number of dense labor markets, changes in those markets drive the national aggregate, and losses in those markets carry direct consequences for household mobility, congestion and municipal finance. Transit commuting therefore functions as a marker of both urban mobility and transport equity, which makes its recent trajectory a matter of practical as well as academic concern.

The coronavirus pandemic interrupted this pattern abruptly. Transit ridership collapsed in the first months of 2020 and recovered only partially in the years that followed, a loss that has been documented in national ridership series and in studies of individual systems.( 3-5)

The decline was not uniform: it varied by mode, by region and by the socioeconomic composition of riders, and a substantial share of former riders had not returned even after offices reopened.( 6-8)

Explaining that incomplete recovery requires separating the demand that disappeared with the pandemic from the demand that was displaced by more durable changes in how and where people work.

The same period brought a large and persistent expansion of working from home. Before 2020 working from home was a stable and modest share of the workforce, but the pandemic moved a large fraction of office work into the home, and hybrid arrangements persisted after public health restrictions were lifted.( 9-11)

By 2023, working from home had settled well above its pre-pandemic level even as some workers returned to the office part-time, and its prevalence varied considerably across metropolitan areas in ways that reflected industry composition, occupational structure and local housing costs. Working from home removes the commute trip altogether, so it has an obvious mechanical relationship with transit commuting: a worker who does not travel to a workplace cannot board a bus or train. The scale and persistence of the shift suggest that remote work could explain a substantial portion of the observed decline in transit commuting, rather than a general erosion of transit mode share among workers who still commute.

The available evidence supports this mechanism but leaves the national metropolitan picture incomplete. Panel and survey studies in Europe, Australia and the United States have found that teleworking reduces commuting trips, commute distance and travel time.( 12-15)

Studies focused on transit have linked remote work to lower ridership and to a changing composition of riders.( 5,16,17)

What is missing is a direct, nationwide quantification across US metropolitan areas of the association between the growth of working from home and the decline of transit commuting using a single official source that applies the same definitions to every area. This gap matters because metropolitan areas differ widely in baseline transit dependence, economic structure and the intensity with which they adopted remote work, and because the policy response depends on knowing whether the loss reflects fewer commuters or lower transit use among commuters.

A metropolitan-level analysis is well suited to isolate this channel. The American Community Survey applies identical question wording and classification to every metropolitan area in every year, so the change in working from home and the change in transit commuting can be computed without the harmonization problems that affect pooled surveys.

Moreover, metropolitan areas vary widely in how much office employment they contain, how much of it could be performed remotely and how dependent their workers are on transit, which provides the cross-sectional variation needed to detect an association. If working from home drives the transit decline, areas that adopted it most should show the largest transit losses, and the relationship should remain visible among workers who still commuted rather than disappearing once the mechanical loss of commuters is removed.

The direction and strength of that relationship carry direct implications for transit agencies, because a loss concentrated in the commuting population has different remedies from a loss of transit mode share among people who continue to travel to work.

The objective of this study was to estimate the ecological association between the change in working-from-home share and the change in public transportation commute share across US metropolitan areas between 2019 and 2023. The primary outcome was the change in transit commuting among all workers, and the secondary outcome was the change among workers who still commuted, which isolates mode substitution from the mechanical loss of commuters. It was hypothesized that metropolitan areas with larger increases in working from home experienced larger declines in public transportation commuting.

METHODS

RESULTS

Included metropolitan areas

A total of 233 metropolitan areas had published table B08301 estimates in 2019 and 246 had them in 2023, and 204 metropolitan areas had estimates in both years and entered the change analysis. The areas excluded from the change analysis were predominantly smaller metropolitan areas whose one-year commuting estimates were suppressed by the Census Bureau, so the matched sample concentrates on the larger labor markets where almost all transit commuting in the United States takes place.

These 204 areas represented 123 million workers in 2019. At the national level, working from home rose from 5.72% of workers in 2019 to 13.84% in 2023, after a peak of 17.86% in 2021 and a partial return to 15.18% in 2022. Public transportation among all workers fell from 4.96% in 2019 to 3.53% in 2023, with a trough of 2.46% in 2021 and a partial recovery to 3.12% in 2022. The absolute number of transit commuters declined from 7.78 million to 5.74 million, while the number working from home grew from 8.97 million to 22.49 million (Table 1) (Figure 1).

Indicator	2019	2023
Workers aged 16 years and over, national (millions)	156.94	162.43
Working from home (% of workers)	5.72%	13.84%
Public transportation (% of workers)	4.96%	3.53%
Public transportation (% of commuters)	5.26%	4.10%
Transit commuters, national (millions)	7.78	5.74
Metropolitan areas with published B08301 estimates	233	246

Note: Commuters are workers who did not work from home. Values are percentages unless otherwise indicated. Source: U.S. Census Bureau, American Community Survey 1-year estimates, table B08301.

Primary outcome

Across the 204 metropolitan areas, the working-from-home share increased by a mean of 7.27 percentage points (median 6.98, interquartile range 5.51 to 9.06), while the public transportation share among all workers decreased by a mean of −0.65 percentage points (median −0.34, interquartile range −0.85 to −0.09). The transit change was negative in at least three quarters of metropolitan areas, as indicated by an upper quartile of −0.09 percentage points. In the weighted regression, each one percentage-point increase in working from home was associated with a −0.354 percentage-point decline in the transit share among all workers (95% CI −0.519 to −0.189, p <0.001), and the model explained 0.25 of the variance in the change. The unweighted Pearson correlation between the two changes was −0.47 (p <0.001) (Table 2) (Figure 2).

Model	Metropolitan areas	Outcome	Beta (95% CI)	p	R2
Weighted OLS (2019 workers)	204	Transit share, all workers	−0.354 (−0.519 to −0.189)	<0.001	0.250
Weighted OLS (2019 workers)	204	Transit share, commuters	−0.294 (−0.432 to −0.155)	<0.001	0.305
Unweighted OLS	204	Transit share, all workers	−0.186 (−0.262 to −0.111)	<0.001	0.223
Unweighted OLS	204	Transit share, commuters	−0.150 (−0.217 to −0.084)	<0.001	0.189
Weighted OLS, at least 500,000 workers in 2019	51	Transit share, all workers	−0.379 (−0.698 to −0.059)	0.020	0.178
Huber robust regression	204	Transit share, all workers	−0.112 (−0.143 to −0.081)	<0.001

Note: Beta is the percentage-point change in transit share per 1 percentage-point increase in working-from-home share. CI = confidence interval; OLS = ordinary least squares; R2 = coefficient of determination. Weighted models use 2019 workers as weights with HC3 standard errors. Source: U.S. Census Bureau, American Community Survey 1-year estimates, table B08301.

DISCUSSION

The main finding of this study was that the growth of working from home was associated with the decline of public transportation commuting across US metropolitan areas between 2019 and 2023, and that the association held both for the share of all workers and for the share of workers who still commuted.

The relationship was not fully explained by the mechanical loss of commuters, because the national decomposition attributed most of the decline to lower transit use among remaining commuters rather than to the reduction in the commuting population. This result indicates that remote work was a structural driver of the post-pandemic transit contraction rather than a temporary demand shock.

The finding is consistent with evidence that working from home suppresses the commute trip itself. Panel studies have reported that workers who shifted to telework made fewer work trips and spent less time traveling,( 12,14) and analyses of the United States and Germany found that telecommuting reduced trip-making and commuter travel over the long term.( 13,15)

The urban scale of the effect has been documented by work showing that working from home reshaped commuting and the spatial structure of cities.(10) Those studies measured travel in general; the present analysis extends the conclusion specifically to public transportation, the mode most exposed to the loss of the daily office commute.

The transit-specific literature points in the same direction but has emphasized the pandemic shock rather than the remote-work channel. Studies of bus ridership across the United States and of transit systems in individual cities attributed the decline to service reductions, infection risk and socioeconomic change.( 3,4,6,19)

Segmentations of riders have shown that the loss was uneven across user groups and that part of it may be permanent.( 5,17) Estimates of the elasticity of transit ridership to service changes after the pandemic suggest that supply adjustments alone cannot account for the observed contraction,( 20) even though new transit lines continued to expand commuting in some corridors.( 21)

The contribution of the present study is to show that, net of those factors, metropolitan areas that adopted working from home more intensely also lost more transit commuting, with a graded relationship across quartiles of working-from-home change that is more consistent with a behavioral substitution process than with an undifferentiated shock.

The association was stronger in the weighted model than in the unweighted model, which indicates that large metropolitan areas drove a disproportionate share of the joint change in working from home and transit commuting.

Large metros concentrate both the office employment that can be performed remotely and the transit systems that serve it, and they experienced the largest absolute losses of transit commuting. The result is compatible with findings that transit ridership is concentrated in a relatively small number of systems, that the recovery was uneven across agencies, and that the pandemic widened existing socioeconomic disparities in travel behavior and transit use.( 6,22-24)

Several mechanisms can explain why working from home and transit commuting moved together beyond the mechanical removal of trips. Remote work is most feasible for office and knowledge workers, who are overrepresented among transit commuters in central business districts, so the workers most able to stay home were also the workers most likely to ride transit.( 12)

Working from home may also have loosened the residential constraint that kept transit riders near stations, enabling relocation away from transit-rich corridors, a mechanism documented for telecommuting and residential location choice.( 25,26)

The persistence of hybrid work reduces the frequency with which even occasional transit users purchase passes, which weakens ridership habits and agency finances.( 5,27)

A related channel operates through commuting frequency: hybrid workers who travel two or three days a week may abandon monthly passes in favor of single tickets or other modes, which lowers the recorded transit commuting share even when neither the workplace nor the residence has changed. The substitution of work trips by teleworking documented during the pandemic is consistent with this interpretation, as is the finding that teleworkers reduced transit use and that mode choice shifted in the later stages of the pandemic.( 28-30)

The evidence is not confined to the largest systems: in small and medium-sized Massachusetts cities, working from home was associated with lower retention of public transportation commuting over the 2011 to 2021 period, which suggests that the same substitution operates outside the major transit markets.( 31)

The modeling literature reaches a comparable conclusion, forecasting that telecommuting produces lasting reductions in commuting and transit demand rather than a one-off shock.( 32,33)

The relationship should not be treated as homogeneous, however. Telecommuting can encourage longer commutes for those who still travel because it relaxes the penalty of distance, and some surveys of teleworkers in Adelaide and Montreal found that remote work redistributed travel rather than eliminating it altogether.( 26,34,35)

Canadian national data likewise attributed a large part of the early pandemic change in commute mode to perceived infection risk rather than to remote work per se.( 36) These qualifications do not contradict the present findings, but they indicate that the association measured here is a net effect that combines a direct trip-removal channel with residential and mode adjustments that can partially offset it.

The decomposition deserves emphasis because it separates two policy-relevant channels. The smaller contribution of fewer commuters implies that waiting for a full return to the workplace would not restore transit commuting to its 2019 level. The larger contribution of lower transit use among remaining commuters implies a genuine erosion of transit mode share among people who still travel to work. This distinction is relevant for agencies that must decide between restoring peak service for office workers and redesigning networks around the trips that persist under hybrid work, a tension already documented in the recovery period.( 7,17,22)

The decomposition therefore separates the two questions that policy discussions often conflate: whether transit lost riders because fewer people commuted, and whether it lost mode share among people who still commuted. The data show that both operated, and that the second was the larger. Comparisons with other countries support the generality of the pattern. In Australia, remote work and attitudinal change reduced commuting in post-pandemic Melbourne, and telecommuting among Adelaide employees was associated with fewer work trips.( 34,37)

In Paris, teleworking had direct and indirect effects on mobility that persisted beyond the lockdown.( 38)

Transit ridership studies outside the United States, such as the Beijing subway, likewise found that the pandemic-era demand loss did not fully recover.(39) The mechanism therefore appears to travel across contexts, although the magnitude will depend on each country's baseline transit dependence and the intensity of remote work.

The equity dimension of this finding is substantial. Transit commuting is disproportionately undertaken by lower-income workers, immigrants and workers of color, and the post-pandemic decline in transit use was itself uneven across socioeconomic groups.( 1,2,6)

If remote work removes a large share of the most reliable transit demand, the riders who remain may face thinner service, and the fiscal pressure that follows can fall most heavily on the groups that depend on transit most. The association documented here therefore connects a labor-market change in the upper part of the wage distribution to a mobility consequence in the lower part, a link that transport equity analyses of the recovery have emphasized.( 22,24)

Because the largest absolute transit losses occurred in the largest metropolitan areas, where fare revenue and service frequency are most interdependent, the fiscal strain may be concentrated precisely where transit provides the greatest social value.

CONCLUSIONS

Growth in working from home was associated with the decline of public transportation commuting across US metropolitan areas between 2019 and 2023, and the association persisted after excluding workers who stopped commuting altogether. The national decline of transit commuting was attributable more to lower transit use among remaining commuters than to the mechanical reduction in the number of commuters.

The results indicate that remote work is a structural influence on transit demand rather than a temporary pandemic effect, with durable consequences. Metropolitan areas and transit agencies that anticipate continued hybrid work should plan service and revenue accordingly, and should monitor whether the association persists, weakens or reverses as hybrid arrangements mature over the coming years.

References

1. 1 Zhang Y, Xu D. Public transit commuting among U.S. immigrants: The role of English skills. Travel Behaviour and Society. 2024;37:100840. doi: 10.1016/j.tbs.2024.100840
2. 2 bunten DM, Fu E, Rolheiser L, Severen C. The Problem Has Existed over Endless Years: Racialized Difference in Commuting, 1980–2019. Journal of Urban Economics. 2024;141:103542. doi: 10.1016/j.jue.2023.103542
3. 3 Wilbur M, Ayman A, Sivagnanam A, Ouyang A, Poon V, Kabir R, et al. Impact of COVID-19 on Public Transit Accessibility and Ridership. Transportation Research Record: Journal of the Transportation Research Board. 2023;2677(4):531-546. doi: 10.1177/03611981231160531
4. 4 Ziedan A, Lima L, Brakewood C. A multiple mediation analysis to untangle the impacts of COVID-19 on nationwide bus ridership in the United States. Transportation Research Part A: Policy and Practice. 2023;173:103718. doi: 10.1016/j.tra.2023.103718
5. 5 Soria J, Edward D, Stathopoulos A. Requiem for transit ridership? An examination of who abandoned, who will return, and who will ride more with mobility as a service. Transport Policy. 2023;134:139-154. doi: 10.1016/j.tranpol.2023.02.016
6. 6 Jiao J, Hansen K, Azimian A. Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A. Transportation Research Record: Journal of the Transportation Research Board. 2023;2677(4):287-297. doi: 10.1177/03611981231159906
7. 7 Deka D, Liu Z. Who stopped riding buses and what would motivate them to return? A New Jersey case study. Case Studies on Transport Policy. 2024;15:101159. doi: 10.1016/j.cstp.2024.101159
8. 8 Paul J. Temporary versus Permanent Pandemic Transit Leavers: Findings from the 2022 US National Household Travel Survey. Findings. 2024;2024. doi: 10.32866/001c.92046
9. 9 Iogansen X, Malik JK, Lee Y, Circella G. Change in work arrangement during the COVID-19 pandemic: A large shift to remote and hybrid work. Transportation Research Interdisciplinary Perspectives. 2024;25:100969. doi: 10.1016/j.trip.2023.100969
10. 10 Ramani A, Alcedo J, Bloom N. How working from home reshapes cities. Proc Natl Acad Sci USA. 2024;121(45):e2408930121. doi: 10.1073/pnas.2408930121
11. 11 Conway MW, Salon D, da Silva DC, Mirtich L. How Will the COVID-19 Pandemic Affect the Future of Urban Life? Early Evidence from Highly-Educated Respondents in the United States. Urban Science. 2020;4(4):50. doi: 10.3390/urbansci4040050
12. 12 Faber RM, Hamersma M, Brimaire J, Kroesen M, Molin EJE. The relations between working from home and travel behaviour: a panel analysis. Transportation. 2024;51(6):2173-2197. doi: 10.1007/s11116-023-10401-4
13. 13 Böhnen C, Kuhnimhof T. Working from home and commuter travel in germany – panel data analysis of long-term effects. Transportation Research Part A: Policy and Practice. 2024;190:104257. doi: 10.1016/j.tra.2024.104257
14. 14 Rüger H, Laß I, Stawarz N, Mergener A. To what extent does working from home lead to savings in commuting time? A panel analysis using the Australian HILDA Survey. Travel Behaviour and Society. 2024;37:100839. doi: 10.1016/j.tbs.2024.100839
15. 15 Obeid H, Anderson ML, Bouzaghrane MA, Walker J. Does telecommuting reduce trip-making? Evidence from a U.S. panel during the COVID-19 pandemic. Transportation Research Part A: Policy and Practice. 2024;180:103972. doi: 10.1016/j.tra.2024.103972
16. 16 Edward D, Soria J, Stathopoulos A. Multi-modal Machine Learning Investigation of Telework and Transit Connections. Data Sci Transp. 2024;6(2):14. doi: 10.1007/s42421-024-00100-6
17. 17 Tiznado-Aitken I, Palm M, Farber S. Segmenting transit ridership: From crisis to opportunity. Transportation Research Part A: Policy and Practice. 2024;190:104239. doi: 10.1016/j.tra.2024.104239
18. 18 U.S. Census Bureau. American Community Survey 1-Year Estimates: Means of Transportation to Work (table B08301), 2019-2023 [Internet]. 2024. Available from: https://www.census.gov/programs-surveys/acs
19. 19 Meredith-Karam P, Kong H, Stewart A, Zhao J. Understanding and comparing the public transit and ride-hailing ridership change in Chicago during COVID-19 via statistical and survey approaches. Travel Behaviour and Society. 2024;37:100838. doi: 10.1016/j.tbs.2024.100838
20. 20 Redelmeier P, El-Geneidy A. If You Cut It Will They Ride? Longitudinal Examination of the Elasticity of Public Transport Ridership in the Post-Pandemic Era. Transportation Research Record: Journal of the Transportation Research Board. 2024;2678(11):241-252. doi: 10.1177/03611981241240754
21. 21 Li W, Zhong H, Boarnet MG. Effects of new transit lines on commuting: Evidence from restricted-use Census Bureau microdata. Applied Geography. 2024;164:103202. doi: 10.1016/j.apgeog.2024.103202
22. 22 Karner A, LaRue S, Klumpenhouwer W, Rowangould D. Evaluating public transit agency responses to the Covid-19 pandemic in seven U.S. regions. Case Studies on Transport Policy. 2023;12:100989. doi: 10.1016/j.cstp.2023.100989
23. 23 Rafiq R, McNally MG, Uddin MYS. Telecommuting and Travel during COVID-19: An Exploratory Analysis across Different Population Geographies in the U.S.A. Transportation Research Record: Journal of the Transportation Research Board. 2023;2677(4):562-582. doi: 10.1177/03611981221109182
24. 24 Brough R, Freedman M, Phillips DC. Understanding socioeconomic disparities in travel behavior during the COVID‐19 pandemic. Journal of Regional Science. 2021;61(4):753-774. doi: 10.1111/jors.12527
25. 25 Wang K, Ozbilen B. Synergistic and threshold effects of telework and residential location choice on travel time allocation. Sustainable Cities and Society. 2020;63:102468. doi: 10.1016/j.scs.2020.102468
26. 26 Caldarola B, Sorrell S. Does teleworking encourage longer commutes? Pre-pandemic evidence from the English National Travel Survey. Travel Behaviour and Society. 2024;37:100822. doi: 10.1016/j.tbs.2024.100822
27. 27 Anik MAH, Habib MA. COVID-19 and Teleworking: Lessons, Current Issues and Future Directions for Transport and Land-Use Planning. Transportation Research Record: Journal of the Transportation Research Board. 2024;2678(12):554-566. doi: 10.1177/03611981231166384
28. 28 Pedreira Junior JU, Pitombo CS. Unveiling substitution patterns of work trips by teleworking and their associations with physical and virtual accessibility in the Brazilian COVID-19 crisis. Travel Behaviour and Society. 2024;34:100657. doi: 10.1016/j.tbs.2023.100657
29. 29 Hensher DA, Balbontin C, Beck MJ, Wei E. Commuting mode choice and work from home in the later stages of COVID-19: Consolidating a future focussed prediction tool to inform transport and land use planning. Transportation Research Part A: Policy and Practice. 2024;187:104194. doi: 10.1016/j.tra.2024.104194
30. 30 Tokey AI, Alam MS. The travel behavior, attitude, and sociodemographic characteristics of the teleworkers in post-pandemic era. World Development Sustainability. 2023;2:100066. doi: 10.1016/j.wds.2023.100066
31. 31 Shakespeare RM, Srinivasan S. Demographic and Built Environment Predictors of Public Transportation Retention and Work-from-Home Changes in Small- to Medium-Sized Massachusetts Cities, 2011–2021. Sustainability. 2024;16(19):8620. doi: 10.3390/su16198620
32. 32 Kogus A, Gal-Tzur A, Shiftan Y. Modelling the Long-Term Expected Impact of the Covid-19 Crisis on Commute and Telecommute. Transportation Research Record: Journal of the Transportation Research Board. 2024;2678(12):466-479. doi: 10.1177/03611981231160170
33. 33 Asif Hasan Anik M, Ahsanul Habib M. Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior. Case Studies on Transport Policy. 2024;17:101244. doi: 10.1016/j.cstp.2024.101244
34. 34 Chalabi G, Dia H. Telecommuting and Travel Behaviour: A Survey of White-Collar Employees in Adelaide, Australia. Sustainability. 2024;16(7):2871. doi: 10.3390/su16072871
35. 35 Victoriano-Habit R, El-Geneidy A. Studying the Interrelationship between Telecommuting during COVID-19, residential local accessibility, and active travel: a panel study in Montréal, Canada. Transportation. 2024;51(3):1149-1166. doi: 10.1007/s11116-022-10369-7
36. 36 Harris MA, Branion-Calles M. Changes in Commute Mode Attributed to COVID-19 Risk in Canadian National Survey Data. Findings. 2021;2021. doi: 10.32866/001c.19088
37. 37 Chalabi G, Dia H. The Impacts of Remote Work and Attitudinal Shifts on Commuting Reductions in Post-COVID Melbourne, Australia. Sustainability. 2024;16(17):7289. doi: 10.3390/su16177289
38. 38 Kiko M, Coulombel N, Poulhès A, Seregina T, Tremblin G. Evaluation of Direct and Indirect Effects of Teleworking on Mobility: The Case of Paris. Transportation Research Record: Journal of the Transportation Research Board. 2024;2678(3):865-878. doi: 10.1177/03611981231182973
39. 39 Wang Z, Guo R, Zou L, Li T, Yao X. Impact Evaluation of COVID-19 on Transit Ridership: A Case Study of the Beijing Subway. Urban Rail Transit. 2024;10(4):297-316. doi: 10.1007/s40864-024-00224-6
40. 40 Credit K, Arnao Z. A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data. Environment and Planning B: Urban Analytics and City Science. 2023;50(3):709-722. doi: 10.1177/23998083221129614