Traffic Volume Distribution Analysis Across California Counties

Empirical Cumulative Distribution Function (ECDF) of Annual Average Daily Traffic

30DayChartChallenge

2025

Analyzing traffic volume distributions across California counties using Empirical Cumulative Distribution Functions (ECDF). This visualization reveals striking differences between urban and rural counties, providing insights into traffic patterns throughout the state highway network.

Author

Steven Ponce

Published

April 12, 2025

Figure 1: A line graph showing Empirical Cumulative Distribution Functions (ECDF) of Annual Average Daily Traffic across ten California counties. The x-axis shows traffic volume from 100 to over 100,000 vehicles on a logarithmic scale. Horizontal dashed lines mark the 25th, 50th, and 75th percentiles. Urban counties like Los Angeles (LA) and Orange (ORA) show curves shifted right, indicating higher traffic volumes. In contrast, rural counties like Tulare (TUL) show curves shifted left, indicating lower traffic volumes.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  paletteer,      # Comprehensive Collection of Color Palettes
  lubridate,      # Make Dealing with Dates a Little Easier
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

traffic_volumnes_raw <- read_csv(here::here(
  'data/30DayChartChallenge/2025/Traffic_Volumes_AADT.csv')
  ) |>
    clean_names()

3. Examine the Data

Show code

glimpse(traffic_volumnes_raw)
skim(traffic_volumnes_raw)

4. Tidy Data

Show code

### |- Tidy ----
traffic_volumes_tidy <- traffic_volumnes_raw |>        
  select(objectid, district, route, county, location_description, 
         back_aadt, ahead_aadt) |>
  pivot_longer(
    cols = c(back_aadt, ahead_aadt),
    names_to = "direction",
    values_to = "aadt"
  ) |>
  mutate(
    direction = case_when(
      direction == "back_aadt" ~ "Back",
      direction == "ahead_aadt" ~ "Ahead",
      TRUE ~ direction
    )
  ) |>
  filter(!is.na(aadt))

# County-level summaries
county_traffic <- traffic_volumes_tidy |>
  group_by(county) |>
  summarize(
    count = n(),
    median_aadt = median(aadt, na.rm = TRUE)
  ) |>
  arrange(desc(count)) |>
  slice_head(n = 10)  # Top 10 counties 

# ECDF for top counties
top_counties <- county_traffic$county

# Data plot
ecdf_data <- traffic_volumes_tidy |>
  filter(county %in% top_counties)

5. Visualization Parameters

Show code

### |- plot aesthetics ---- 
colors <- get_theme_colors(
  palette = paletteer::paletteer_d(
    "ggprism::prism_dark2"
    )
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("Traffic Volume Distribution Analysis Across California Counties",
                          width = 50) 
subtitle_text <- str_wrap("Empirical Cumulative Distribution Function (ECDF) of Annual Average Daily Traffic", 
                          width = 90)

# Create caption
caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 12,
  source_text =  "California AADT via data.gov" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.text.y = element_text(color = colors$text, size = rel(0.68)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),

    # Grid elements
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
 
    # Legend elements
    legend.position = "right",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),

    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ecdf_data |>
  ggplot(aes(x = aadt, color = county)) +
  # Geoms
  stat_ecdf(geom = "step", linewidth = 1) +
  geom_hline(
    yintercept = c(0.25, 0.5, 0.75), linetype = "dashed", 
    color = "gray50", alpha = 0.7
    ) +
  # Annotate
  annotate(
    "text", x = min(traffic_volumes_tidy$aadt, na.rm = TRUE), 
    y = c(0.26, 0.51, 0.76), 
    label = c("25th", "50th", "75th"), 
    hjust = 0, size = 3, color = "gray30"
    ) +
  # Scales
  scale_x_log10(labels = scales::comma) +
  scale_color_manual(values = colors$palette) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Annual Average Daily Traffic (log scale)",
    y = "Cumulative Probability",
    color = "County",
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.9),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 10)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 5, b = 15)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 12, 
  width = 8, 
  height = 8
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 paletteer_1.6.0 scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rematch2_2.1.2    labeling_0.4.3   
[33] rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[37] colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3    base64enc_0.1-3  
[41] utf8_1.2.4        withr_3.0.2       bit64_4.5.2       timechange_0.3.0 
[45] rmarkdown_2.29    bit_4.5.0         ragg_1.3.3        hms_1.1.3        
[49] evaluate_1.0.1    knitr_1.49        markdown_1.13     rlang_1.1.4      
[53] gridtext_0.1.5    Rcpp_1.0.13-1     glue_1.8.0        xml2_1.3.6       
[57] renv_1.0.3        vroom_1.6.5       svglite_2.1.3     rstudioapi_0.17.1
[61] jsonlite_1.8.9    R6_2.5.1          prismatic_1.1.2   systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_12.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- California Annual Average Daily Traffic Volumes, Metadata Updated: November 27, 2024 data.gov

--- title: "Traffic Volume Distribution Analysis Across California Counties" subtitle: "Empirical Cumulative Distribution Function (ECDF) of Annual Average Daily Traffic" description: "Analyzing traffic volume distributions across California counties using Empirical Cumulative Distribution Functions (ECDF). This visualization reveals striking differences between urban and rural counties, providing insights into traffic patterns throughout the state highway network." author: "Steven Ponce" date: "2025-04-12" categories: ["30DayChartChallenge", "2025"] tags: [ "ECDF", "ggplot2", "tidyverse", "distribution", "traffic analysis", "California", "log scale", "cumulative distribution", "highway data", "data.gov" ] image: "thumbnails/30dcc_2025_12.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_12.html" # description: "Day 12 of #30DayChartChallenge: Visualizing traffic volume distributions across California counties using ECDF plots, revealing the stark contrast between urban and rural highway traffic patterns." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A line graph showing Empirical Cumulative Distribution Functions (ECDF) of Annual Average Daily Traffic across ten California counties. The x-axis shows traffic volume from 100 to over 100,000 vehicles on a logarithmic scale. Horizontal dashed lines mark the 25th, 50th, and 75th percentiles. Urban counties like Los Angeles (LA) and Orange (ORA) show curves shifted right, indicating higher traffic volumes. In contrast, rural counties like Tulare (TUL) show curves shifted left, indicating lower traffic volumes.](30dcc_2025_12.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization paletteer, # Comprehensive Collection of Color Palettes lubridate, # Make Dealing with Dates a Little Easier camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 8, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false traffic_volumnes_raw <- read_csv(here::here( 'data/30DayChartChallenge/2025/Traffic_Volumes_AADT.csv') ) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(traffic_volumnes_raw) skim(traffic_volumnes_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- traffic_volumes_tidy <- traffic_volumnes_raw |> select(objectid, district, route, county, location_description, back_aadt, ahead_aadt) |> pivot_longer( cols = c(back_aadt, ahead_aadt), names_to = "direction", values_to = "aadt" ) |> mutate( direction = case_when( direction == "back_aadt" ~ "Back", direction == "ahead_aadt" ~ "Ahead", TRUE ~ direction ) ) |> filter(!is.na(aadt)) # County-level summaries county_traffic <- traffic_volumes_tidy |> group_by(county) |> summarize( count = n(), median_aadt = median(aadt, na.rm = TRUE) ) |> arrange(desc(count)) |> slice_head(n = 10) # Top 10 counties # ECDF for top counties top_counties <- county_traffic$county # Data plot ecdf_data <- traffic_volumes_tidy |> filter(county %in% top_counties) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = paletteer::paletteer_d( "ggprism::prism_dark2" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("Traffic Volume Distribution Analysis Across California Counties", width = 50) subtitle_text <- str_wrap("Empirical Cumulative Distribution Function (ECDF) of Annual Average Daily Traffic", width = 90) # Create caption caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 12, source_text = "California AADT via data.gov" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), axis.text.y = element_text(color = colors$text, size = rel(0.68)), axis.line.x = element_line(color = "gray50", linewidth = .2), # Grid elements panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), # Legend elements legend.position = "right", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ecdf_data |> ggplot(aes(x = aadt, color = county)) + # Geoms stat_ecdf(geom = "step", linewidth = 1) + geom_hline( yintercept = c(0.25, 0.5, 0.75), linetype = "dashed", color = "gray50", alpha = 0.7 ) + # Annotate annotate( "text", x = min(traffic_volumes_tidy$aadt, na.rm = TRUE), y = c(0.26, 0.51, 0.76), label = c("25th", "50th", "75th"), hjust = 0, size = 3, color = "gray30" ) + # Scales scale_x_log10(labels = scales::comma) + scale_color_manual(values = colors$palette) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Annual Average Daily Traffic (log scale)", y = "Cumulative Probability", color = "County", ) + # Theme theme( plot.title = element_text( size = rel(1.9), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(.85), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 5, b = 15) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 12, width = 8, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`30dcc_2025_12.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_12.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - California Annual Average Daily Traffic Volumes, Metadata Updated: November 27, 2024 [data.gov](https://catalog.data.gov/dataset/traffic-volumes-aadt-ee8d6) :::