Skip to contents

Summarize trip and VMT rates

summarize-trip-and-vmt-rates.Rmd

Why rates? A convenient way to examine and compare

In addition to aggregate counts and shares, psrc.travelsurvey includes functions to calculate average daily trip- and VMT (vehicle miles traveled) rates. These metrics are often used in travel analysis and modeling. As a ratio (mean amount per person per day), these rates are normalized in respect to both population and days reported, while maintaining a unit scale so they can be interpreted or compared in both absolute and relative terms. (In contrast, shares have no unit and can only be compared in relative terms; counts aren’t normalized and by themselves can’t be meaningfully compared across subgroups or surveys.)

psrc.travelsurvey functions

Trip and VMT rates involve summarization at the day level prior to aggregate summarization across respondents, and have their own functions:

Each function is simplified from psrc_hts_stat(); since analysis_unit and stat_var for each are fixed/inherent, there are only two arguments:

  • hts_data - the hts_data object
  • group_vars - any grouping variables, in nesting order (share summaries are calculated per categories of the variable listed last).

The incl_na=FALSE option is also available in both functions.

Requirements

These functions rely on additional functionality submitted to the travelSurveyTools package that has not yet been merged, so you’ll need to install a branch of that package:

remotes::install_github("RSGInc/travelSurveyTools", ref="add_vmt_rate").

Because VMT is calculated from distance, is limited to vehicular travel, and accounts for co-travelers, the preceding get_psrc_hts() call must include the following among the survey_vars argument:

  • mode_class
  • trip_distance
  • travelers_total.

If the hts_data object is missing one or more of these, psrc_hts_vmtrate() will report that to the user and return NULL.

Because count is an implicit trip attribute, psrc_hts_triprate() requires no additional variables.

Examples 

library(psrc.travelsurvey)
library(data.table)

# Specify which variables to retrieve
vars <- c("telecommute_freq", "workplace",
          "mode_class", "distance_miles", "travelers_total")

# Retrieve the data
hts_data <- get_psrc_hts(survey_vars = vars)  # default includes all survey_years
hts_data <- hts_bin_telecommute_trichotomy(hts_data)

# Calculate a trip rate
rs1 <- psrc_hts_triprate(hts_data, group_vars="telecommute_trichotomy")

head(dplyr::select(rs1[survey_year==2023], -c(survey_year, count, min, max, median)))
#>    telecommute_trichotomy     mean  mean_moe
#>                    <fctr>    <num>     <num>
#> 1:          Fully At Home 3.429360 0.5138521
#> 2:                 Hybrid 4.003980 0.3685396
#> 3:        Fully In Person 4.334025 0.2826659
#> 4:                   <NA> 3.505400 0.1940856

# Calculate a VMT rate
rs2 <- psrc_hts_vmtrate(hts_data, "telecommute_trichotomy", incl_na=FALSE)

head(dplyr::select(rs2[survey_year==2023], -c(survey_year, count, min, max, median)))
#>    telecommute_trichotomy     mean mean_moe
#>                    <fctr>    <num>    <num>
#> 1:          Fully At Home 15.78928 2.359958
#> 2:                 Hybrid 24.21145 2.599772
#> 3:        Fully In Person 31.97204 3.300489