National Household Travel Survey (NHTS)

The authoritative source on travel behavior, recording characteristics of people and vehicles of all modes.

• Four core linkable tables, with one record per household, person, trip, and vehicle, respectively.

• A complex sample survey designed to generalize to the civilian non-institutional U.S. population.

• Released every five to eight years since 1969.

• Funded by the Federal Highway Administration, with data collected by Ipsos Public Affairs.

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Recommended Reading

Four Example Strengths & Limitations:

✔️ Origin-Destination passively collected data complement traditional household survey

✔️ Sample supports analysis of metro areas within census divisions

❌ 2022 redesign uses retrospective recorded travel day (1 day prior) rather than travel log

❌ Long-distance trip questions do not estimate respondent’s annual behavior or volume

Three Example Findings:

1. Online-purchased home deliveries grew over 2017-2022, from 2.5 to 5.4 per person per month.

2. In 2022, 53% of K-12 students were dropped off at school in a private vehicle or drove themselves.

3. Nearly 9 in 10 US households had a vehicle available to drive in 2022.

Two Methodology Documents:

2022 NHTS Data User Guide

2022 NHTS Weighting Memo

One Haiku:

# commuter patterns,
# truckin'. what a long strange trip
# who went when where why

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Download, Import, Preparation

Download and unzip each the 2022 files:

library(haven)

tf <- tempfile()

download.file( "https://nhts.ornl.gov/assets/2022/download/sas.zip" , tf , mode = 'wb' )

unzipped_files <- unzip( tf , exdir = tempdir() )

Import the tables containing one record per household, person, trip, and vehicle:

nhts_import <-
    function( this_prefix , this_unzip ){
        
        this_sas7bdat <-
            grep( 
                paste0( this_prefix , "\\.sas7bdat$" ) , 
                this_unzip , 
                value = TRUE 
            )
        
        this_tbl <- read_sas( this_sas7bdat )
        
        this_df <- data.frame( this_tbl )
        
        names( this_df ) <- tolower( names( this_df ) )
        
        this_df
    }
    
hhpub_df <- nhts_import( "hhv2pub" , unzipped_files )
perpub_df <- nhts_import( "perv2pub" , unzipped_files )
trippub_df <- nhts_import( "tripv2pub" , unzipped_files )
vehpub_df <- nhts_import( "vehv2pub" , unzipped_files )

Add a column of ones to three of those tables, then a column of non-missing mileage to the trips table:

hhpub_df[ , 'one' ] <- 1

perpub_df[ , 'one' ] <- 1

trippub_df[ , 'one' ] <- 1

trippub_df[ !( trippub_df[ , 'trpmiles' ] %in% -9 ) , 'wtd_tripmiles_no_nines' ] <-
    trippub_df[ !( trippub_df[ , 'trpmiles' ] %in% -9 ) , 'trpmiles' ] *
    trippub_df[ !( trippub_df[ , 'trpmiles' ] %in% -9 ) , 'wttrdfin' ]

Sum the total trip count and mileage to the person-level, both overall and restricted to walking only:

trips_per_person <- 
    with( 
        trippub_df , 
        aggregate( 
            cbind( wttrdfin , wtd_tripmiles_no_nines ) , 
            list( houseid , personid ) , 
            sum , 
            na.rm = TRUE 
        ) 
    )

names( trips_per_person ) <-
    c( 'houseid' , 'personid' , 'wtd_trips' , 'wtd_miles' )

walks_per_person <- 
    with( 
        subset( trippub_df , trptrans == '20' ) , 
        aggregate( 
            cbind( wttrdfin , wtd_tripmiles_no_nines ) , 
            list( houseid , personid ) , 
            sum , 
            na.rm = TRUE 
        ) 
    )

names( walks_per_person ) <-
    c( 'houseid' , 'personid' , 'wtd_walks' , 'wtd_walk_miles' )

Merge these trip count and mileage values on to the person-level file, replacing non-matches with zero:

nhts_df <- merge( perpub_df , trips_per_person , all.x = TRUE )

nhts_df <- merge( nhts_df , walks_per_person , all.x = TRUE )

for( this_variable in c( 'wtd_trips' , 'wtd_miles' , 'wtd_walks' , 'wtd_walk_miles' ) ){
    nhts_df[ is.na( nhts_df[ , this_variable ] ) , this_variable ] <- 0
}

stopifnot( nrow( nhts_df ) == nrow( perpub_df ) )

Save Locally  

Save the object at any point:

# nhts_fn <- file.path( path.expand( "~" ) , "NHTS" , "this_file.rds" )
# saveRDS( nhts_df , file = nhts_fn , compress = FALSE )

Load the same object:

# nhts_df <- readRDS( nhts_fn )

Survey Design Definition

Construct a complex sample survey design:

Define household-level, person-level, and trip-level designs:

library(survey)

hh_design <-
    svydesign( 
        id = ~ houseid , 
        strata = ~ stratumid , 
        data = hhpub_df , 
        weights = ~ wthhfin
    )

nhts_design <-
    svydesign(
        id = ~ houseid ,
        strata = ~ stratumid ,
        data = nhts_df ,
        weights = ~ wtperfin
    )

trip_design <-
    svydesign(
        id = ~ houseid ,
        strata = ~ stratumid ,
        data = trippub_df ,
        weights = ~ wttrdfin
    )

Variable Recoding

Add new columns to the data set:

hh_design <-
    update(
        hh_design ,
        hhsize_categories =
            factor(
                findInterval( hhsize , 1:4 ) ,
                levels = 1:4 ,
                labels = c( 1:3 , '4 or more' )
            )
    )
    

nhts_design <- 
    update( 
        nhts_design , 
        
        urban_area = as.numeric( urbrur == '01' ) ,
        
        daily_person_trips = ( wtd_trips / ( 365 * wtperfin ) ) ,
        
        daily_person_miles_of_travel = ( wtd_miles / ( 365 * wtperfin ) ) ,
        
        daily_person_walks = ( wtd_walks / ( 365 * wtperfin ) ) ,
        
        daily_person_walk_miles_of_travel = ( wtd_walk_miles / ( 365 * wtperfin ) ) ,
        
        work_status = 
            factor( 
                as.numeric( worker ) , 
                levels = 2:1 , 
                labels = c( 'non-worker' , 'worker' ) 
            )

    )

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Analysis Examples with the survey library  

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( nhts_design , "sampling" ) != 0 )

svyby( ~ one , ~ r_sex_imp , nhts_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , nhts_design )

svyby( ~ one , ~ r_sex_imp , nhts_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ daily_person_walks , nhts_design )

svyby( ~ daily_person_walks , ~ r_sex_imp , nhts_design , svymean )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ work_status , nhts_design , na.rm = TRUE )

svyby( ~ work_status , ~ r_sex_imp , nhts_design , svymean , na.rm = TRUE )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ daily_person_walks , nhts_design )

svyby( ~ daily_person_walks , ~ r_sex_imp , nhts_design , svytotal )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ work_status , nhts_design , na.rm = TRUE )

svyby( ~ work_status , ~ r_sex_imp , nhts_design , svytotal , na.rm = TRUE )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ daily_person_walks , nhts_design , 0.5 )

svyby( 
    ~ daily_person_walks , 
    ~ r_sex_imp , 
    nhts_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ daily_person_walk_miles_of_travel , 
    denominator = ~ daily_person_miles_of_travel , 
    nhts_design 
)

Subsetting

Restrict the survey design to individuals who have used a bicycle in last 30 days:

sub_nhts_design <- subset( nhts_design , last30_bike == '01' )

Calculate the mean (average) of this subset:

svymean( ~ daily_person_walks , sub_nhts_design )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ daily_person_walks , nhts_design )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ daily_person_walks , 
        ~ r_sex_imp , 
        nhts_design , 
        svymean 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( nhts_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ daily_person_walks , nhts_design )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ daily_person_walks , nhts_design , deff = TRUE )

# SRS with replacement
svymean( ~ daily_person_walks , nhts_design , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ urban_area , nhts_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( daily_person_walks ~ urban_area , nhts_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ urban_area + work_status , 
    nhts_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        daily_person_walks ~ urban_area + work_status , 
        nhts_design 
    )

summary( glm_result )

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Replication Example

This example matches the 2022 Household Size counts from Table 2-1:

hhsize_counts <- svytotal( ~ hhsize_categories , hh_design )

stopifnot(
    all( round( coef( hhsize_counts ) / 1000 , 0 ) == c( 36409 , 44751 , 19001 , 27384 ) )
)

hhsize_ci <- confint( hhsize_counts )

hhsize_moe <- hhsize_ci[ , 2 ] - coef( hhsize_counts )

stopifnot( all( round( hhsize_moe / 1000 , 0 ) == c( 1807 , 1760 , 1448 , 1742 ) ) )    

This example matches the 2022 Average Daily Person Trips per Person from Table 2-9:

this_mean <- svymean( ~ daily_person_trips , nhts_design )

stopifnot( round( coef( this_mean ) , 2 ) == 2.28 )

this_ci <- confint( this_mean )

this_moe <- this_ci[ , 2 ] - coef( this_mean )

stopifnot( round( this_moe , 2 ) == 0.06 )

This example matches the 2022 Average Daily PMT per Person from Table 2-9:

this_mean <- svymean( ~ daily_person_miles_of_travel , nhts_design )

stopifnot( round( coef( this_mean ) , 2 ) == 28.55 )

this_ci <- confint( this_mean )

this_moe <- this_ci[ , 2 ] - coef( this_mean )

stopifnot( round( this_moe , 2 ) == 2.39 )

This example matches the 2022 Average Person Trip Length (Miles) from Table 2-9:

this_mean <- svymean( ~ trpmiles , subset( trip_design , trpmiles >= 0 ) )

stopifnot( round( coef( this_mean ) , 2 ) == 12.56 )

this_ci <- confint( this_mean )

this_moe <- this_ci[ , 2 ] - coef( this_mean )

stopifnot( round( this_moe , 2 ) == 1.04 )

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Analysis Examples with srvyr  

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for NHTS users, this code replicates previously-presented examples:

library(srvyr)
nhts_srvyr_design <- as_survey( nhts_design )

Calculate the mean (average) of a linear variable, overall and by groups:

nhts_srvyr_design %>%
    summarize( mean = survey_mean( daily_person_walks ) )

nhts_srvyr_design %>%
    group_by( r_sex_imp ) %>%
    summarize( mean = survey_mean( daily_person_walks ) )