Travel patterns of older adults in the United States: evidence from the 2022 National Household Travel Survey

INTRODUCTION

The United States is ageing rapidly. The youngest members of the baby boom cohort reached age 65 during the 2010s, and that large generation now dominates the older adult population.( 1,2)

The ageing of the population changes what a national transport system must deliver, because mobility in later life conditions access to health care, food, social contact and, in turn, physical and mental well-being.( 3,4)

Travel-related barriers already limit access to medical care for millions of adults, and the burden falls unevenly across the population.( 5)

When driving stops, out-of-home activity contracts and depressive symptoms often increase.( 6,7) For transport planners and ageing-services agencies, the practical question is therefore not whether older adults will need alternatives to the private car, but which subgroups need them first and in what form.

The National Household Travel Survey (NHTS) has documented these patterns across successive cycles. Adults aged 65 years and older make fewer daily trips than working-age adults, travel shorter distances and depend on the private car for most journeys, and their trips concentrate in shopping, errands and social purposes rather than in commuting.( 8,9)

Trip generation declines further with a self-reported medical condition,( 10) is lower among women than among men,( 11) and differs between rural and urban residence.( 12)

Residential context shapes destination access and the number of trips actually made.( 13) The geography of the built environment matters as much as demography: neighborhood walkability, income and green cover are all associated with whether older adults walk or stay home.( 14,15)

Driving reduction and cessation, the most consequential change in later-life mobility, is gradual and self-regulated long before it becomes complete.( 16,17)

The drivers who remain in later life keep a strong symbolic attachment to the car and to the independence it represents,( 18) which is why the transition away from driving is so often delayed and then abrupt.( 19)

Most of this evidence predates the COVID-19 pandemic and rests on the 2001 and 2017 survey cycles. The pandemic temporarily reduced travel for all age groups and changed the activity patterns of older adults in particular.( 20)

The 2022 NextGen NHTS is the first post-pandemic national travel diary, yet few analyses have examined it jointly across age group, health limitation and urban or rural residence within a single framework that covers trip frequency, mode use and driving cessation.

This joint view matters in practice: a planner deciding where to concentrate demand-responsive transit needs to know whether the binding constraint is age, health or geography, and how the three interact. The question also matters for the design of future cohorts, because the baby boom generation travels differently from the cohorts that preceded it.( 1)

Driving cessation in particular deserves fresh national estimates, because most quantitative syntheses of the phenomenon draw on studies conducted before 2020,( 21,22) and the post-pandemic balance between in-person activities and alternatives such as telemedicine and home delivery may have changed the consequences of not driving.

The objective of this study was to quantify trip frequency, mode use and driving cessation among adults aged 65 years and older in the 2022 National Household Travel Survey and to compare them by age group, health limitation and urban or rural residence. The analysis treats these three outcomes as points along a single continuum of later-life mobility, from reduced trip making to the loss of the ability to drive.

METHODS

This was a cross-sectional secondary analysis of the 2022 NextGen National Household Travel Survey, a nationally representative 24-hour travel diary of the civilian noninstitutionalized population of the United States. The survey used a one-stage address-based sampling design and was fielded from January 2022 to January 2023, with travel days assigned across all seven days of the week over the thirteen-month field period.( 23)

Household members aged 5 years and older completed a diary for an assigned travel day that ran from 4:00 a.m. to 4:00 a.m., recording every trip, its mode, purpose and duration. Public-use household, person and trip files in CSV format were downloaded from the survey portal.( 24) Reporting followed the STROBE statement for observational studies.

The public-use person file records 16,997 persons from 7,893 households, weighted to national control totals derived from the American Community Survey. The trip file records 31,074 person trips. Because a person appears in the trip file only when at least one trip was made, the person file captures the full sample including persons with zero trips on the travel day.

The analysis sample comprised all persons aged 65 years and older in the person file with a usable person weight, which yielded 4,329 persons. The three files were merged on the household and person identifiers before analysis.

Trip-level records were linked to the person file to assign age, sex, health limitation and residential area to each trip, and the household-level urban or rural classification was carried on every record. Values coded as refused, do not know or not ascertained were retained as distinct categories for descriptive reporting and excluded only from the models that required the affected variable. The analysis script reads the raw public-use CSV files and reproduces every estimate from them, so the results can be regenerated by rerunning the script.

Trip frequency was the count of person trips on the travel day recorded in the person file, which includes persons with no trips. The measure is interpreted as trips per person per travel day rather than as a multi-day average. Mode use was derived from the trip-level mode variable and grouped into five mutually exclusive categories: private vehicle driver, private vehicle passenger, walk, transit, and other modes.

The driver and passenger categories were assigned from the household-vehicle driver and passenger flags for car, van, sport utility vehicle and pickup truck trips; transit combined public and commuter bus, streetcar, subway, commuter rail, Amtrak and paratransit; and other modes combined bicycle, motorcycle, recreational vehicle, airplane, taxi, ride-hailing, e-scooter and unspecified modes.

Driving cessation was measured in two complementary ways. Non-driver status was defined as reporting not being a driver, and health-related cessation was defined as selecting the reason "given up driving due to condition or disability" among persons who reported a condition or disability that makes travel difficult. Age group was classified as 65 to 74, 75 to 84, or 85 years and older.

The three-category split separates the young old, who largely resemble working-age adults in their travel, from the old old, among whom driving cessation and health limitation become common, and allows a direct comparison with the age bands used in previous NHTS analyses. Health limitation was a self-reported condition or disability that makes travel difficult, treated as a binary indicator; the small number of persons who refused, did not know or had no ascertained response was excluded from analyses that required the variable and is reported separately. Residential area was the household urban or rural classification based on 2020 census geography. Covariates were sex, recorded as reported by the respondent, and worker status during the reference period.

Person-level estimates used the national person weight, and trip-level mode shares used the annualized national trip weight. The person weight sums to the civilian noninstitutionalized population aged 5 years and older, and the trip weight sums to the national annualized person-trip total. Mode shares were computed as the ratio of weighted trips in each mode group to the weighted total of trips made by persons aged 65 years and older.

Because the survey clusters persons within households and stratification reflects the sampling design, all confidence intervals were estimated with cluster-robust standard errors on the household identifier; this yields design-based inference without requiring the full replicate-weight structure. Weighted means and weighted proportions were reported with 95% confidence intervals.

Daily trip frequency, a non-negative count with overdispersion relative to the Poisson distribution, was compared across age group, sex, health limitation, worker status and residential area with negative binomial regression, reported as incidence rate ratios. Non-driver status was modelled with logistic regression, reported as odds ratios. Both models included age group, sex, health limitation, worker status and residential area simultaneously, so each estimate is adjusted for the others.

Estimates for health limitation were computed among persons with a valid response, and the remaining covariates entered the models as indicator variables. Analyses were two-sided with alpha of 0.05.

RESULTS

Driving cessation

Non-driver status rose steeply with age (Figure 1). Overall, 11.8% of adults aged 65 years and older were non-drivers (95% CI 10.2% to 13.4%). The figure displays the urban and rural gradient of non-driver prevalence across the three age groups. The prevalence was 7.0% at ages 65 to 74, 14.2% at ages 75 to 84 and 40.6% at ages 85 and older. In the adjusted model, the odds of being a non-driver were 1.59 times higher at ages 75 to 84 (95% CI 1.00 to 2.51; p = 0.050) and 5.51 times higher at ages 85 and older (95% CI 3.34 to 9.08; p < 0.001) than at ages 65 to 74.

Health limitation was associated with the highest odds: 7.65 times higher among adults with a travel-limiting condition (95% CI 5.15 to 11.37; p < 0.001), and non-driver prevalence reached 41.9% in this group versus 6.9% without. Rural residence was associated with lower odds of being a non-driver (odds ratio 0.66; 95% CI 0.44 to 0.99; p = 0.047), and non-driver prevalence was 12.7% in urban areas versus 8.8% in rural areas. The odds of being a non-driver were 2.53 times higher for women than for men (95% CI 1.65 to 3.86; p < 0.001). Among adults with a travel-limiting condition, 32.9% (95% CI 27.3% to 38.6%) reported having given up driving due to the condition, rising from 20.1% at ages 65 to 74 to 53.2% at ages 85 and older.

DISCUSSION

In practice, these results identify two groups whose transport needs outpace the private car: the oldest old, and older adults whose health limits travel. Both groups make fewer daily trips, ride as passengers more often and stop driving at far higher rates than their younger and healthier counterparts. The main finding is that age and health limitation, rather than urban or rural residence, drive the gradient in later-life mobility, and that the two sharpen each other after age 85.

The trip-frequency estimates are consistent with previous NHTS-based work. Analyses of the 2001 to 2017 cycles reported that older adults made fewer daily trips than working-age adults and that trip making declined with age, with the steepest fall among the oldest groups.( 8,9)

The mean of 1.81 daily person trips observed here is lower than the pre-pandemic values reported for comparable groups, which is expected given the recovery of travel after 2020 and the ageing of the sample.( 9)

International comparisons point in the same direction: Swiss GPS data show that trip frequency and daily travel time decline monotonically with age,( 25) and a latent-demand framework applied to Chinese cities found the largest unmet travel demand among the oldest adults.( 26)

The health gradient also replicates earlier NHTS evidence that medical conditions reduce daily travel,( 10) here with a rate ratio of 0.64, which is larger than the age gradient between 65 and 74 and 75 to 84. Worker status operated in the expected direction, with workers making 1.29 times as many trips as non-workers, a reminder that part of the age decline in trip making is an employment effect rather than a purely physiological one.

The sensitivity analysis separates these margins: among adults who left home at least once, the mean rose to 2.85 trips, which shows that the zero-trip day, more than a reduction in trips per active day, drives the age decline. This distinction matters for service design, because an adult who never leaves home needs a different intervention from one who leaves home but makes few trips.

The mode-use findings describe a system that remains organized around the private car. The driver share of 72.6% and the passenger share of 18.8% are consistent with the car dependence documented for older Americans.( 8,27)

The shift from driver to passenger with age, and the much stronger shift associated with health limitation, has been described in car-dependent settings where older men and women compensate for reduced driving by riding with others.( 28,29)

In Japan, driving status separates older adults in their social participation more than residential density does.( 30) The finding that transit accounted for less than one percent of trips overall, and for 3.2% of trips even among adults with a travel-limiting condition, matches qualitative evidence that older adults withdraw from transit when it becomes physically or psychologically difficult.( 31,32)

This stands in contrast with settings where concessionary fares sustain higher transit use among older and disabled riders,( 33) which suggests that the low United States share reflects supply and fare policy rather than preference alone. For practitioners, this implies that expanding fixed-route transit alone will not reach most of the population identified here; the binding constraint is the first and last connection to the car.

The overall picture aligns with the broader literature that links the built and social environments to mobility and well-being in later life.( 34) Where older adults live determines the destinations they can reach and therefore the trips they take,( 13) and mode choice responds to the socioeconomic and built-environment context of the region in which they live.( 27)

Walking and the built environment deserve a separate reading. The walk share of 6.8% overall, higher in urban than in rural areas, is consistent with studies that tie older adults' walking to neighborhood walkability, income and the availability of destinations rather than to age alone.( 14,15)

Public transit stop density has also been associated with walking for exercise in a national sample of older adults,( 35) which suggests that the built environment can partially offset the decline in out-of-home activity documented here.

Ride-hailing adds a further layer: it was adopted by a measurable share of older adults before the pandemic,( 36) and it serves adults with disabilities alongside conventional taxis.( 37) The near-zero transit share reported here does not mean older adults reject shared mobility; it means the services they currently reach are organized around the car. Caregivers absorb much of the resulting gap, a burden concentrated in an ageing America.( 38)

Driving cessation followed the expected age curve and its health-related component was substantial. Non-driver prevalence of 11.8% overall and 40.6% at ages 85 and older is consistent with longitudinal evidence that driving cessation accelerates in late old age.( 17,22)

The odds ratio of 7.65 for health limitation exceeds the age gradient, which agrees with reviews showing that functional and medical decline, more than age itself, precipitates driving reduction and cessation.( 16,21)

The finding that 32.9% of adults with a travel-limiting condition reported having given up driving due to the condition, reaching 53.2% at ages 85 and older, aligns with studies of the transition to driving cessation in which older adults stop before they feel ready and often lack a planned alternative.( 19,39)

Driving cessation carries measurable consequences for mental health and social isolation,( 6,7) which makes the two high-risk groups identified here a priority for support.

State licensing policies also shape when and how much older adults drive: license renewal requirements are associated with daily driving among older Americans,( 40) and older-driver crash involvement has continued to decline even as the older population has grown.( 41)

Concessionary transit fares improve the well-being of older and disabled riders where they exist,(33) and environments that support mobility in later life are consistently linked to better well-being.(34)

These findings together imply that the decision to stop driving is not only a medical event; it is shaped by policy, by the availability of alternatives and by the meaning people attach to the car.

The mechanisms behind these gradients are likely to combine physical, cognitive and social processes. Declining vision, slower reaction times and reduced nighttime confidence narrow the situations in which a person feels able to drive, which is why self-regulation, the voluntary avoidance of difficult driving conditions, precedes cessation and why the transition is often gradual rather than sudden.( 16,17)

Medical events such as stroke, falls and dementia accelerate the process, and older adults with early-stage dementia often stop driving before any formal assessment.( 39) When alternatives are scarce, the same health events convert directly into fewer trips, because a person who cannot drive in a car-dependent area has no low-cost substitute.( 4,12)

The social meaning of driving then interacts with these constraints: older adults who define themselves as drivers delay planning for cessation, which makes the eventual transition more disruptive.( 18,19)

For the purpose of intervention, this sequence suggests three points of entry: support before cessation, alternatives at the moment of cessation, and social reintegration afterward.

Several differences by residence and sex deserve attention. Rural residents were less likely to be non-drivers and walked less than urban residents, a pattern consistent with the longer distances and weaker transit alternatives of rural areas.( 12,27)

The rural trip-frequency difference was small and not significant after adjustment, echoing evidence that rural older adults do not take fewer trips than their urban counterparts when a ride is available.( 12)

Women had higher odds of non-driver status and slightly lower trip frequency, which reproduces the gender gap in later-life mobility documented in earlier NHTS cycles.( 11)

Cohort effects also matter: the baby boom generation travels more than the silent generation did at the same ages,( 1,2) so future cohorts may retain driving longer and postpone the patterns described here.

Taken together, the three outcomes describe a predictable sequence rather than three separate problems. Trip frequency declines first and most gently; mode shift from driver to passenger follows as health and age advance; and driving cessation, the endpoint of the sequence, concentrates in the oldest and least healthy groups.

The practical value of quantifying all three in one national frame is that agencies can position services along this sequence. A paratransit provider planning capacity, for instance, needs the passenger-share gradient by health limitation more than it needs the aggregate trip count. A licensing authority weighing in-person renewal requirements needs the non-driver gradient by age and health more than it needs mode shares. Each gradient points to a different decision, and each decision has a different owner, which is the reason for reporting them side by side.

The study has strengths and limitations that bear on interpretation. The 2022 NextGen NHTS is the only national post-pandemic travel diary, and its person and trip weights allow nationally representative statements about the civilian population aged 65 years and older; the use of cluster-robust inference also respects the household-level design.

Against this, the survey measures a single travel day per person, which captures between-person variation in trip frequency but not within-person variation across the week; analyses that pool several diary days per respondent would reduce this noise and are recommended for future work. Health limitation is self-reported and does not distinguish the type or severity of the condition, so the health gradient should be read as the association of perceived travel difficulty rather than of specific diagnoses; linkage to administrative health records would sharpen this estimate.

The trip diary undercounts short and non-motorized trips relative to instrumented observation, which may bias the walking share downward; wearable or smartphone-based validation is recommended. Driving cessation is measured cross-sectionally, so the sequence between health decline and stopping driving cannot be established from these data alone; longitudinal panels that follow drivers through the cessation transition are recommended.

Finally, the sample of adults aged 85 and older is small in unweighted terms, which widens confidence intervals for the oldest group; oversampling the oldest old in future cycles is recommended. The analysis also treats residence as a two-category urban or rural indicator, which may mask gradients within metropolitan areas, where suburban environments differ from dense cores in ways that matter for walking and transit; finer geographic strata in future work are recommended.

Residual confounding is possible because education, income and household composition were not included in the models, and these characteristics correlate with both driving and trip making; household-level socioeconomic adjustment is recommended.

For practitioners, the implications are concrete. Ageing-services agencies should target driving-cessation support and volunteer ride programs at adults aged 75 years and older who report a travel-limiting condition, because this is the group in which passenger dependence rises most quickly. Transit agencies should design door-through-door and demand-responsive services rather than fixed routes alone, given that transit captures less than one percent of older adults' trips.

Rural mobility managers should prioritize rides for health care and errands over walkability investments, because walking plays a smaller role in rural areas. Health systems should screen for driving cessation in the same way they screen for falls, since the health-related component of cessation is large and consequential for mental health.

Licensing authorities should pair renewal procedures with referrals to mobility counselling, so that the administrative moment of licence renewal becomes a point of entry into alternatives rather than a cliff. Planners monitoring equity should track the passenger-share and non-driver gradients over time, because these two indicators capture the moment at which the private car stops meeting an older adult's needs. This implies that the most efficient use of transport resources is not a single programme for all older adults, but a sequence of services matched to the age and health gradients documented here.

CONCLUSIONS

Trip frequency among adults aged 65 years and older declined with age and with a travel-limiting health condition, while mode use remained organized around the private car: the driver share fell and the passenger share rose with age and with health limitation, and walking and transit stayed as minor shares that differed little by residence. Driving cessation rose steeply with age and was most strongly associated with health limitation, with the largest burden among adults aged 85 years and older and among those whose condition makes travel difficult.

For practitioners, the gradient points to a sequence rather than a single intervention: driving-cessation support and volunteer ride programmes for adults aged 75 years and older with a travel-limiting condition, door-through-door and demand-responsive transit for the connections that fixed routes do not serve, and health-system screening for driving cessation alongside screening for falls.

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