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.
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:
Online-purchased home deliveries grew over 2017-2022, from 2.5 to 5.4 per person per month.
In 2022, 53% of K-12 students were dropped off at school in a private vehicle or drove themselves.
Nearly 9 in 10 US households had a vehicle available to drive in 2022.
Two Methodology Documents:
One Haiku:
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:
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' )
)
)
Analysis Examples with the survey
library
Unweighted Counts
Count the unweighted number of records in the survey sample, overall and by groups:
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:
Subsetting
Restrict the survey design to individuals who have used a bicycle in last 30 days:
Calculate the mean (average) of this subset:
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:
Calculate the complex sample survey-adjusted variance of any statistic:
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:
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 )
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:
Calculate the mean (average) of a linear variable, overall and by groups: