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, with a 2022 release expected in late 2023.
Funded by the Federal Highway Administration, with data collected by Westat.

Please skim before you begin:

2017 NHTS Data User Guide
2017 NHTS Weighting Report
This human-composed haiku or a bouquet of artificial intelligence-generated limericks

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

Download, Import, Preparation

Download and unzip each of the main 2017 files:

library(haven)

nhts_dl_uz <-
    function( this_url ){
    
        tf <- tempfile()

        download.file( this_url , tf , mode = 'wb' )

        unzip( tf , exdir = tempdir() )
    }
    
unzipped_survey_data <-
    nhts_dl_uz( "https://nhts.ornl.gov/assets/2016/download/sas.zip" )

unzipped_replicate_weights <-
    nhts_dl_uz( "https://nhts.ornl.gov/assets/2016/download/Replicates.zip" )

unzipped_trip_chains <-
    nhts_dl_uz( "https://nhts.ornl.gov/assets/2016/download/TripChain/TripChain17.zip" )

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( "hhpub" , unzipped_survey_data )
perpub_df <- nhts_import( "perpub" , unzipped_survey_data )
trippub_df <- nhts_import( "trippub" , unzipped_survey_data )
vehpub_df <- nhts_import( "vehpub" , unzipped_survey_data )

hhwgt_df <- nhts_import( "hhwgt" , unzipped_replicate_weights )
perwgt_df <- nhts_import( "perwgt" , unzipped_replicate_weights )

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 ) , 'tripmiles_no_nines' ] <-
    trippub_df[ !( trippub_df[ , 'trpmiles' ] %in% -9 ) , 'trpmiles' ]

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( one , tripmiles_no_nines ) , 
            list( houseid , personid ) , 
            sum , 
            na.rm = TRUE 
        ) 
    )

names( trips_per_person ) <-
    c( 'houseid' , 'personid' , 'trips_per_person' , 'miles_per_person' )

walks_per_person <- 
    with( 
        subset( trippub_df , trptrans == '01' ) , 
        aggregate( 
            cbind( one , tripmiles_no_nines ) , 
            list( houseid , personid ) , 
            sum , 
            na.rm = TRUE 
        ) 
    )

names( walks_per_person ) <-
    c( 'houseid' , 'personid' , 'walks_per_person' , 'walk_miles_per_person' )

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[ is.na( nhts_df[ , 'trips_per_person' ] ) , 'trips_per_person' ] <- 0
nhts_df[ is.na( nhts_df[ , 'miles_per_person' ] ) , 'miles_per_person' ] <- 0

nhts_df <- merge( nhts_df , walks_per_person , all.x = TRUE )
nhts_df[ is.na( nhts_df[ , 'walks_per_person' ] ) , 'walks_per_person' ] <- 0
nhts_df[ is.na( nhts_df[ , 'walk_miles_per_person' ] ) , 'walk_miles_per_person' ] <- 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:

Sort both the one record per household and household replicate weights tables, then define the design:

library(survey)

hhpub_df <- hhpub_df[ order( hhpub_df[ , 'houseid' ] ) , ]
hhwgt_df <- hhwgt_df[ order( hhwgt_df[ , 'houseid' ] ) , ]

hh_design <-
    svrepdesign(
        weight = ~ wthhfin ,
        repweights =
            hhwgt_df[ grep( 'wthhfin[0-9]' , names( hhwgt_df ) , value = TRUE ) ] ,
        scale = 6 / 7 ,
        rscales = 1 ,
        type = 'JK1' ,
        mse = TRUE ,
        data = hhpub_df
    )

Sort both the one record per person and person replicate weights tables, then define the design:

nhts_df <- nhts_df[ do.call( order , nhts_df[ , c( 'houseid' , 'personid' ) ] ) , ]
perwgt_df <- perwgt_df[ do.call( order , perwgt_df[ , c( 'houseid' , 'personid' ) ] ) , ]

nhts_design <-
    svrepdesign(
        weight = ~ wtperfin ,
        repweights =
            perwgt_df[ grep( 'wtperfin[0-9]' , names( perwgt_df ) , value = TRUE ) ] ,
        scale = 6 / 7 ,
        rscales = rep( 1 , 98 ) ,
        type = 'JK1' ,
        mse = TRUE ,
        data = nhts_df
    )

Sort both the one record per trip and person replicate weights tables, then define the design:

trippub_df <- trippub_df[ do.call( order , trippub_df[ , c( 'houseid' , 'personid' ) ] ) , ]
perwgt_df <- perwgt_df[ do.call( order , perwgt_df[ , c( 'houseid' , 'personid' ) ] ) , ]

trip_design <-
    svrepdesign(
        weight = ~ wttrdfin ,
        repweights =
            perwgt_df[ grep( 'wttrdfin[0-9]' , names( perwgt_df ) , value = TRUE ) ] ,
        scale = 6 / 7 ,
        rscales = 1 ,
        type = 'JK1' ,
        mse = TRUE ,
        data = trippub_df
    )

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' )
        
    )

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( ~ miles_per_person , nhts_design )

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

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

svymean( ~ hhstate , nhts_design )

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

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

svytotal( ~ miles_per_person , nhts_design )

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

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

svytotal( ~ hhstate , nhts_design )

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

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

svyquantile( ~ miles_per_person , nhts_design , 0.5 )

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

Estimate a ratio:

svyratio( 
    numerator = ~ walk_miles_per_person , 
    denominator = ~ miles_per_person , 
    nhts_design 
)

Subsetting

Restrict the survey design to ever cyclists:

sub_nhts_design <- subset( nhts_design , nbiketrp > 0 )

Calculate the mean (average) of this subset:

svymean( ~ miles_per_person , 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( ~ miles_per_person , nhts_design )

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

grouped_result <-
    svyby( 
        ~ miles_per_person , 
        ~ 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( ~ miles_per_person , nhts_design )

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

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

# SRS with replacement
svymean( ~ miles_per_person , 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( miles_per_person ~ urban_area , nhts_design )

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

svychisq( 
    ~ urban_area + hhstate , 
    nhts_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        miles_per_person ~ urban_area + hhstate , 
        nhts_design 
    )

summary( glm_result )

Replication Example

This example matches the 2017 rows from Summary of Travel Trends Table 1a:

hhsize_counts <- svytotal( ~ hhsize_categories , hh_design )

stopifnot(
    all( round( coef( hhsize_counts ) / 1000 , 0 ) == c( 32952 , 40056 , 18521 , 26679 ) )
)

hhsize_ci <- confint( hhsize_counts , df = ncol( hh_design$repweights ) )

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

stopifnot( all( round( hhsize_moe / 1000 , 0 ) == c( 0 , 0 , 97 , 97 ) ) )

This example matches 2017 NHTS Westat project co-author’s workshop slide 38:

unwtd_n <- with( nhts_df , tapply( trips_per_person , worker , sum ) )
stopifnot( all( unwtd_n == c( 79295 , 28 , 497944 , 346305 ) ) )

surveyed_n <- with( nhts_df , tapply( trips_per_person , worker , mean ) )
stopifnot( all( round( surveyed_n , 2 ) == c( 2.84 , 1.65 , 3.88 , 3.21 ) ) )

this_mean <- svyby( ~ trips_per_person , ~ worker , nhts_design , svymean )
stopifnot( round( coef( this_mean ) , 2 ) == c( 2.78 , 1.28 , 3.77 , 3.01 ) )

this_ci <- confint( this_mean , df = ncol( nhts_design$repweights ) )

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

stopifnot( all( round( this_moe , 2 ) == c( 0.06 , 2.21 , 0.03 , 0.06 ) ) )

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 )