Commuting and car dependence across the rural-urban continuum of United States counties

INTRODUCTION

The private car dominates the journey to work in the United States, where most workers commute alone in a car, truck or van and only a small minority travels by public transport.( 1)

Car dependence is most acute in rural areas, in which low densities, long distances to services and scarce public transport leave residents with few alternatives to the automobile.( 2,3)

The result is a transport system in which the car functions not as a convenience but as a condition of participation in work, education and health care.( 4,5)

In nonmetropolitan counties, public transport reaches few corridors, and the automobile is, for most working households, the only realistic means of reaching a job.( 2,6)

Commuting carries a double meaning in rural America: it is the daily expression of car dependence and, at the same time, a mechanism that links dispersed rural populations to urban labour markets and regional service centres.( 7)

Whether rural residents actually travel longer than urban residents to reach work is less settled than the image of the rural long-distance commuter suggests. Rural workers face long distances to employment and are sometimes portrayed as extreme commuters.( 8)

Yet the longest average commutes concentrate in large metropolitan areas, where congestion, suburban sprawl and housing costs push workers outward from job-rich cores.( 9,10)

Commuting geography has long treated rural areas as functional extensions of urban centres rather than as uniformly distant places.( 7)

Average commute times in the United States have risen over recent decades, but the rise has been uneven across space and concentrated among low-income metropolitan workers.( 10)

Individual travel surveys, whose rural samples are thin, answer the rural-urban question inconsistently, and the evidence from countries as different as India and the United States points in divergent directions.( 11)

Commuting is therefore not only a question of engineering but of equity: the time, money and vehicles required to reach work determine who can participate in the labour market and at what cost.( 5,10)

Most studies quantify rural mobility with household surveys or with case studies of single regions, which limits comparability across the whole settlement hierarchy. A county-level analysis of the complete United States using the Rural-Urban Continuum Codes offers two advantages. First, it covers every county without the sampling variability that weakens rural survey estimates. Second, it resolves the rural-urban distinction into nine ordered categories rather than a binary contrast, and therefore reveals gradients that a metropolitan-nonmetropolitan split conceals.( 12)

The approach is deliberately descriptive and comparative rather than causal: the aim is to establish the size and direction of rural-urban differences with a national denominator, and to subject each difference to explicit sensitivity checks. The objective of this study is to compare commute time, car dependence and zero-vehicle household rates between metropolitan and nonmetropolitan counties of the United States, to quantify how these measures change across the nine rural-urban continuum categories, and to test whether the differences survive population weighting and the exclusion of small counties.

METHODS

RESULTS

6. Sensitivity analyses

The population-weighted estimates confirmed the direction of every primary result except the zero-vehicle share (Figure 1). The population-weighted and unweighted differences overlapped for the median commute, the drive-alone share and the share commuting 30 minutes or more. The population-weighted share commuting 30 minutes or more differed by −8.1 percentage points (95% CI, −9.8 to −6.3).

Within nonmetropolitan counties, those adjacent to a metropolitan area had longer commutes than those not adjacent (20.6 versus 16.2 minutes; p < 0.001) and higher drive-alone shares (78.0% versus 75.4%; p < 0.001). The zero-vehicle share did not differ between the two groups (difference 0.2 percentage points; p = 0.412).

DISCUSSION

In a complete county-level assessment of the United States, nonmetropolitan counties commuted shorter distances than metropolitan counties while depending more on private cars, and the county-level zero-vehicle household rate did not distinguish rural from urban counties when each county was counted equally. These results place the rural transport problem in a specific light: the burden of rural America is not primarily long commutes but automobile dependence without alternatives.( 2)

The shorter rural commute runs against the image of the rural long-distance commuter but is consistent with earlier commuting geography. Studies of the rural-urban hierarchy showed that commuting ties rural workers to nearby urban centres rather than extending their trips uniformly, and that the highest commuting intensities occur around metropolitan cores.( 7,8)

The longest average commutes in this study appeared in the largest metropolitan counties, where job-housing imbalance and congestion lengthen travel.( 9)

Survey-based work on low-income workers in the United States likewise locates the heaviest commuting burden in metropolitan areas.( 10)

The absence of a rural-urban gap in very long commutes of 60 minutes or more is informative: extreme commuting exists in both settings, driven by congestion in metropolitan areas and by long distances to regional centres in remote rural counties. Longer commutes carry documented welfare costs, including lower satisfaction and diminished well-being, which this pattern attaches mainly to large metropolitan counties.( 14,15)

The mechanisms behind this pattern operate on both sides of the continuum. Rural labour markets are local: residents of small towns and the surrounding countryside often work within the same county, in agriculture, resource extraction, manufacturing or public services, so their commutes are short even when the destination is reached exclusively by car.( 8)

Metropolitan labour markets, by contrast, integrate employment across a wide region, and the separation of affordable housing from job-rich cores lengthens travel even for workers who never enter the congested centre.( 9)

The flat rural-urban gap in commutes of 60 minutes or more is the signature of these two mechanisms operating simultaneously: congestion stretches trips in large metros, while distance to a regional centre stretches the trips of the minority of rural workers who commute out of their county.( 7)

These cross-sectional averages also embed a specific temporal window. The 2020 to 2024 period straddles the COVID-19 pandemic, which temporarily collapsed commuting, shifted a large share of metropolitan knowledge work to the home and only partially recovered by 2024.( 16,17)

Because the rural-urban gap in work from home widened in this window, part of the observed difference reflects the uneven geography of pandemic-era remote work rather than a stable feature of rural labour markets.( 18,19)

The stability of the commute-time and car-dependence results across sensitivity analyses does not remove this temporal caveat, and repeated measurement with the next five-year release will show whether the work-from-home gradient persists.

The higher drive-alone and private-vehicle shares in nonmetropolitan counties agree with a broad literature on rural car dependency in Ireland, the United Kingdom, China and the United States.( 2,3,5,20)

Automobile dependence is a structural feature of rural settlement: low density makes public transport economically fragile and car ownership a near precondition of participation in work and services.( 4,6,21)

Within rural areas, the gradient between adjacent and non-adjacent counties reinforces this reading; counties that function as residential extensions of metropolitan labour markets showed both longer commutes and higher drive-alone shares than the most remote counties, where shorter local trips predominate.( 7,22)

Comparative work on car ownership in rural towns and on the spatial limits of car-dependence models reaches the same conclusion, namely that rurality itself, rather than income or preference alone, sustains automobile reliance.( 23-25)

Non-car modes cannot close the rural gap in the short term. Walking and cycling serve very short trips, and in low-density settlements the distances to work, school and services exceed comfortable active-travel ranges for most residents.( 26)

Demand-responsive and shared services have repeatedly been proposed as rural solutions, but their economics remain fragile at rural densities, and their users still depend on the car for the last mile or for trips the services do not cover.( 27,28)

These findings therefore argue for realism: policy should treat the car as part of the rural mobility system and direct effort toward making it affordable, shared and gradually less dominant, rather than assuming that mode shift can proceed as it does in cities.( 4)

The lower work-from-home share in nonmetropolitan counties is consistent with the occupational structure of rural labour markets and with post-pandemic telework studies that find remote work concentrated in metropolitan knowledge sectors.( 16,17,29)

Telework intensity, and not only its prevalence, follows the same urban gradient, and its uneven geography persisted after the acute phase of the pandemic.( 18,19,30) For rural residents, the smaller remote-work share means fewer opportunities to substitute travel with connectivity, which compounds their dependence on the automobile for daily participation.( 31,32)

Expanding remote work is unlikely, by itself, to reduce rural car dependence, because the occupations that can be performed remotely are underrepresented in rural economies and because remote workers still rely on the car for non-work travel.( 29,32)

The null unweighted difference in zero-vehicle households, together with its reversal under household weighting, is the most informative result. Zero-vehicle households cluster in large central cities with dense transit networks, so a household-weighted national mean is dominated by metropolitan counties.( 33,34)

In rural counties, carlessness is less common on average but more likely to be involuntary and linked to poverty than to the availability of alternatives.( 3,35,36)

The two settings therefore present different forms of transport disadvantage that a binary rural-urban contrast conceals. This distinction bears directly on measurement: studies of car ownership burden that weight households, and studies that weight counties, can report opposite signs for the same construct, as occurred here.( 5,37)

Taken together, the findings reframe the prevailing account of rural transport disadvantage. The disadvantage of rural counties is not that residents travel too far but that they have no practical alternative to the private car, so households without a vehicle, older adults who no longer drive and people with disabilities are exposed to acute mobility poverty.( 3,31)

The disadvantage of large metropolitan counties is the opposite: alternatives exist but congestion and housing constraints inflate travel time, and the longest commutes fall on the workers least able to pay for them.( 9,10)

A rural-urban contrast expressed only through average commute time would miss both problems, which is why the zero-vehicle and car-dependence measures are indispensable companions to the commute-time result.

The gradient analysis sharpens the interpretation further. Commute time declined along the continuum from the largest metropolitan counties to the most remote rural counties, while drive-alone shares remained above three quarters in every nonmetropolitan category and work from home fell by roughly half between the largest metropolitan counties and the smallest nonmetropolitan counties.

The zero-vehicle share, by contrast, was flat across the nine categories, which confirms that vehicle ownership is distributed differently from the commute and mode variables that distinguish rural from urban counties. The estimates were stable across population weighting and the exclusion of small counties, and every difference kept its sign in those specifications except the zero-vehicle share, as described above.

The comparison with studies that use different designs reinforces confidence in the findings. Household-level surveys capture individual commute decisions but thin out in rural areas, whereas the present county-level design trades individual detail for complete coverage and therefore avoids the rural under-sampling that weakens survey-based contrasts.( 12)

Models of car dependence built from individual or household data reach similar conclusions about the structural role of rurality.(24,25) The direction of the results held under population weighting and county-size exclusions, which indicates that the findings are not an artefact of the many small rural counties that dominate the unweighted sample.

The analysis also offers a methodological lesson for county-level transport equity research. Reporting a single rural-urban difference for a construct such as vehicle ownership can hide the dependence of the sign on the weighting scheme, as the reversal of the zero-vehicle result shows.

Researchers comparing counties should therefore report both unweighted and population-weighted estimates, state the analytic denominator for each outcome, and accompany binary comparisons with the ordered continuum when the classification allows it.( 12) These conventions cost little and prevent the sign of a headline difference from becoming an artefact of the weighting choice.

The choice of the Rural-Urban Continuum Codes also warrants scrutiny alongside alternative classifications. The Rural-Urban Commuting Area codes measure rurality through commuting flows themselves, whereas the continuum codes rely on population size and adjacency, and the two can disagree for counties with strong commuting ties to a core.( 12)

Because commuting is one of the outcomes studied here, a classification built from population size and adjacency avoids circularity: it does not define rurality by the very travel behaviour being measured. This independence supports the present choice, and the contrast between adjacent and non-adjacent nonmetropolitan counties reproduced the same qualitative conclusions.

The main strengths of this study are the complete coverage of United States counties, the use of a standardized nine-category rural-urban classification, the reproducibility of the estimates from public data and the explicit sensitivity to weighting and county size. The main limitations follow from the design. The ecological unit is the county, so the associations describe places rather than individuals and cannot be transferred to residents without committing the ecological fallacy, and the cross-sectional design cannot establish the direction of any causal relation between rurality and travel behaviour.( 12)

The cross-sectional period 2020 to 2024 obscures the pandemic-era disruption of commuting and its partial recovery.( 16)

The Rural-Urban Continuum Codes are based on the 2020 census while the travel estimates span 2020 to 2024, a small temporal mismatch. Margins of error from the American Community Survey were not propagated into the county-level means, although five-year county estimates are large enough that suppression was absent.

The grouped travel-time distribution required midpoint assumptions for the mean commute, and the median was used as the primary outcome to limit that assumption. County-level income, age structure and industry mix were not adjusted for, so part of the rural-urban difference may reflect these compositional factors rather than rurality itself.

Each limitation suggests a concrete extension. Multilevel individual data would address the ecological gap; annual series would track post-pandemic recovery; and a margin-of-error-weighted mean would refine the county estimates. For policy, these results imply that rural transport policy should prioritize access alternatives and vehicle affordability over commute-time relief, whereas metropolitan policy continues to confront long commutes. In rural areas, subsidized demand-responsive services, intercity connections and support for vehicle access among low-income households address the documented dependence, and public acceptability of the measures that reduce car reliance depends on how the dependence itself is framed.( 2,38,39)

Rural transport interventions, from autonomous shuttles to shared mobility, remain plausible but unproven complements to the private car, and their evaluation should be framed against the baseline of dependence documented here.( 26-28)

Measuring rurality along the full continuum, rather than as a binary, should become standard in county-level transport equity analysis, and repeating this assessment with each new five-year release of the American Community Survey would turn a cross-sectional snapshot into a monitor of rural mobility disadvantage over time.(12) The county is also the administrative scale at which many United States transport and housing programs operate, so these results translate directly into the geography of funding allocation and planning.

CONCLUSIONS

Commute time, car dependence and zero-vehicle household rates were compared between rural and urban counties of the United States, and their variation across the rural-urban continuum was quantified. Nonmetropolitan counties showed shorter commutes and greater reliance on private cars, while county-level zero-vehicle household rates did not differ between the two groups.

The differences in commuting and car dependence remained stable under population weighting and the exclusion of small counties, whereas the zero-vehicle result reversed once households were weighted. Across the continuum, commute time fell from the largest metropolitan counties to the most remote rural counties while drive-alone shares remained high in every nonmetropolitan category, and work from home declined from metropolitan cores toward the rural periphery. These contrasts imply that the two policy agendas are complementary rather than competing: metropolitan areas need shorter and faster commutes, while rural areas need alternatives to the private car and support for the households that cannot afford one.

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