Traffic Flow Analysis: A64 Road, May 2021

Analysis of traffic patterns across multiple dimensions

A comprehensive analysis of traffic patterns on the A64 road during May 2021, examining hourly volumes, daily trends, weekday-weekend differences, and speed variations across multiple sensor locations. The visualization reveals distinct rush hour patterns, daily volume fluctuations with confidence intervals, and systematic differences between weekday and weekend traffic behaviors.

Figure 1: A four-panel visualization of A64 Road Traffic Flow (May 2021). The top left shows hourly traffic volume with highlighted rush hours (7-9 AM, 4-6 PM). The top right displays daily traffic volume trends with a scatter plot and confidence interval. The bottom left compares weekday vs weekend traffic patterns, showing average speed and volume throughout the day. The bottom right visualizes average speed trends by sensor location.

Steps to Create this Graphic

1. Load Packages & Setup

## 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
    glue,              # Interpreted String Literals
    here,              # A Simpler Way to Find Your Files
    patchwork,         # The Composer of Plots
    paletteer,         # Comprehensive Collection of Color Palettes
    gghighlight,       # Highlight Lines and Points in 'ggplot2'
    lubridate          # Working with Dates and Times
)   
})

suppressMessages(source(here::here("_setup.R")))

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  16,
  height =  10,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)

2. Read in the Data

# tt <- tidytuesdayR::tt_load(2024, week = 49) 

# A64_traffic  <- tt$A64_traffic |> clean_names()
 
# tidytuesdayR::readme(tt)
# rm(tt)

# Option 2: Read directly from GitHub
A64_traffic <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-12-03/A64_traffic.csv')
A64_traffic <-  A64_traffic |> clean_names() 

3. Examine the Data

glimpse(A64_traffic)
skim(A64_traffic)

4. Tidy Data

### |- tidy data ----

# Hourly Volume Patterns (plot 1) ----
hourly_volume_clean <- A64_traffic |>
    group_by(report_date, hour = lubridate::hour(time_period_ending)) |>
    summarise(
        hourly_volume = sum(total_volume, na.rm = TRUE),
        .groups = 'drop'
    ) |>
    mutate(
        month_day = format(report_date, "%b %d"),
        hour_label = sprintf("%02d:00", hour),
        hour_f = factor(hour, levels = c(7, 8, 9, 16, 17, 18), ordered = TRUE),
        is_peak = hour %in% c(7:9, 16:18)
    )

# Daily Volume Trends (plot 2) ----
daily_volume_clean <- A64_traffic |>
    group_by(date = as.Date(report_date)) |>
    summarise(
        daily_volume = sum(total_volume, na.rm = TRUE),
        .groups = 'drop'
    ) |>
    mutate(
        week_num = week(date),
        month_day = format(date, "%b %d")
    )

# Weekend Patterns (plot 3) ----
weekend_patterns_clean <- A64_traffic |>
    mutate(
        hour = lubridate::hour(time_period_ending),
        is_weekend = ifelse(lubridate::wday(report_date) %in% c(1, 7), "Weekend", "Weekday"),
        hour_label = sprintf("%02d:00", hour)
    ) |>
    group_by(hour, hour_label, is_weekend) |>
    summarise(
        avg_volume = mean(total_volume, na.rm = TRUE),
        avg_speed = mean(avg_mph, na.rm = TRUE),
        .groups = 'drop'
    ) |>
    pivot_longer(
        cols = c(avg_speed, avg_volume),
        names_to = "metric",
        values_to = "value"
    ) |>
    mutate(
        metric = factor(metric,
                        levels = c("avg_speed", "avg_volume"),
                        labels = c("Average Speed (mph)", "Average Volume (count)"))
    ) |>
    group_by(hour, metric) |>
    summarise(
        weekday = value[is_weekend == "Weekday"],
        weekend = value[is_weekend == "Weekend"],
        .groups = 'drop'
    )

# Sensor Speed Patterns (plot 4) ----
sensor_speed_clean <- A64_traffic |>
    group_by(
        date = as.Date(report_date), 
        site_id,
        site_name
    ) |>
    summarise(
        avg_speed = mean(avg_mph, na.rm = TRUE),
        .groups = 'drop'
    ) |>
    mutate(
        month_day = format(date, "%b %d"),
        site_id = factor(site_id) 
    )

5. Visualization Parameters

### |-  plot aesthetics ----
bkg_col      <- "#f5f5f2"  
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray30"  

viz_colors <- list(
    morning = "#83c5be",    
    evening = "#7B7FD4",     
    orange  = "#EE6100FF",   
    gray_light = "gray90",   
    gray_dark  = "gray30"    
)

# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 49 } &bull; Source: WebTRIS Traffic Flow API<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
bs <- str_glue("<span style='font-family:fa6-brands'>&#xe671; </span>")

# text
title_text    <- str_glue("Traffic Flow Analysis: A64 Road, May 2021")
subtitle_text <-  str_glue("Analysis of traffic patterns across multiple dimensions")
caption_text  <- str_glue("{tt} {li} stevenponce &bull; {bs} sponce1 &bull; {gh} poncest &bull; #rstats #ggplot2")

### |-  fonts ----
setup_fonts()

### |-  plot theme ----
theme_traffic <- function() {
    theme_minimal(base_size = 14, base_family = "text") +
        theme(
            # Background
            plot.background  = element_rect(fill = bkg_col, color = bkg_col),
            panel.background = element_rect(fill = bkg_col, color = bkg_col),
            plot.margin      = margin(t = 10, r = 20, b = 10, l = 20),
            
            # Title, subtitle, caption
            plot.title = element_markdown(
                family = "title",
                size = rel(1.4),
                face = "bold",
                color = title_col,
                hjust = 0.5,
                margin = margin(t = 10, b = 5)
            ),
            plot.subtitle = element_markdown(
                family = "text",
                size = rel(1),
                color = subtitle_col,
                hjust = 0.5,
                margin = margin(b = 10)
            ),
            plot.caption = element_markdown(
                family = "caption",
                size = rel(0.7),
                color = caption_col
            ),
            
            # Axis formatting
            axis.title = element_text(
                family = "text",
                size = rel(0.93),
                face = "bold",
                color = text_col
            ),
            axis.text = element_text(
                family = "text",
                size = rel(0.79),
                color = text_col
            ),
            axis.line.x = element_line(
                color = "#252525",
                linewidth = 0.2
            ),
            
            # Grid lines
            panel.grid.major.y = element_line(
                color = "gray90",
                linewidth = 0.2
            ),
            panel.grid.major.x = element_blank(),
            panel.grid.minor = element_blank(),
            
            # Legend
            legend.position = "top",
            legend.title = element_text(
                size = rel(0.8),
                face = "bold"
            ),
            legend.text = element_text(
                size = rel(0.71)
            ),
            legend.key.width = unit(1.5, "cm"),
            legend.spacing.x = unit(0.2, 'cm')
        )
}

6. Plot

### |-  plot 1 ----
p1 <- hourly_volume_clean |>
    ggplot(aes(x = report_date, 
               y = hourly_volume, 
               group = hour)) +
    # Add non-peak hours with gray color
    geom_line(
        data = hourly_volume_clean |> filter(!hour %in% c(7:9, 16:18)),
        color = viz_colors$gray_dark,
        alpha = 0.2,
        linewidth = 0.5
    ) +
    # Add peak hours with colors
    geom_line(
        data = hourly_volume_clean |> filter(hour %in% c(7:9, 16:18)),
        aes(color = hour_f),
        alpha = 0.9,
        linewidth = 0.7
    ) +
    scale_color_manual(
        values = c(
            "7" = viz_colors$orange,
            "8" = viz_colors$orange,
            "9" = viz_colors$orange,
            "16" = viz_colors$evening,
            "17" = viz_colors$evening,
            "18" = viz_colors$evening
        ),
        name = "Hour of Day"
    ) +
    scale_y_continuous(
        labels = scales::comma_format(),
        breaks = scales::breaks_pretty(n = 6),
        expand = c(0.02, 0.02)
    ) +
    scale_x_datetime(
        date_breaks = "1 week",
        date_labels = "%b %d",
        expand = c(0.02, 0.02)
    ) +
    labs(
        title = "Hourly Traffic Volume Throughout May",
        subtitle = "Highlighting rush hour periods (7-9 AM, 4-6 PM)",
        x = "Date",
        y = "Hourly Volume"
    ) +
    theme_traffic() +
    theme(
        legend.key.width = unit(2, "cm"),
        legend.key.height = unit(0.3, "cm"),
        legend.title = element_text(
            size = rel(0.8),
            face = "bold",
            margin = margin(b = 5)
        ),
        legend.box.spacing = unit(0.5, "cm")
    ) +
    guides(
        color = guide_legend(
            nrow = 2,
            byrow = TRUE
        )
    )

### |-  plot 2 ----
p2 <- daily_volume_clean |>
    ggplot(aes(x = date, y = daily_volume)) +
    # Add confidence band and line
    geom_smooth(
        method = "loess",
        span = 0.4,
        color = viz_colors$evening,        
        fill = alpha(viz_colors$evening, 0.25),
        linewidth = 1.0,
        se = TRUE
    ) +
    # Add values as points
    geom_point(
        color = viz_colors$orange,
        alpha = 0.6,
        size = 1.8
    ) +
    # Scales
    scale_y_continuous(
        labels = scales::comma_format(),
        breaks = seq(0, 50000, by = 10000),
        limits = c(0, 50000),
        expand = c(0, 0)  # Remove padding
    ) +
    scale_x_date(
        date_breaks = "1 week",
        date_labels = "%b %d",
        expand = c(0.02, 0.02)
    ) +
    # Labs
    labs(
        title = "Daily Traffic Volume Patterns Throughout May",
        subtitle = "Showing daily total volume with 95% confidence interval",
        x = "Date",
        y = "Daily Volume"
    ) +
    # Theme 
    theme_traffic() +
    theme(
        panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),
        panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20)
    )

### |-  plot 3 ----
p3 <- ggplot(weekend_patterns_clean, aes(x = hour)) +
    # Add ribbons (weekday vs weekend)
    geom_ribbon(
        aes(
            ymin = pmin(weekday, weekend),
            ymax = weekend,
            fill = "Weekend"
        ),
        alpha = 0.15
    ) +
    geom_ribbon(
        aes(
            ymin = pmin(weekday, weekend),
            ymax = weekday,
            fill = "Weekday"
        ),
        alpha = 0.15
    ) +
    # Add lines 
    geom_line(
        aes(y = weekday, color = "Weekday"), 
        linewidth = 1.0
    ) +
    geom_line(
        aes(y = weekend, color = "Weekend"), 
        linewidth = 1.0
    ) +
    # Scales
    scale_x_continuous(
        breaks = seq(0, 23, by = 4),
        labels = function(x) sprintf("%02d:00", x),
        expand = c(0.02, 0.02)
    ) +
    # Add legend 
    scale_color_manual(
        name = "Day Type",
        values = c(
            "Weekday" = viz_colors$evening,
            "Weekend" = viz_colors$orange
        )
    ) +
    scale_fill_manual(
        name = "Day Type",
        values = c(
            "Weekday" = viz_colors$evening,
            "Weekend" = viz_colors$orange
        )
    ) +
    # Labs
    labs(
        title = "Traffic Patterns: Weekday vs Weekend",
        subtitle = "Comparing average speed and volume throughout the day",
        x = "Hour of Day",
        y = NULL
    ) +
    # Facet
    facet_wrap(~metric, scales = "free_y", nrow = 1) +
    # Theme
    theme_traffic() +
    theme(
        strip.text = element_text(size = rel(1), face = "bold"),
        panel.grid.major.y = element_line(
            color = viz_colors$gray_light, 
            linewidth = 0.3
        ),
        legend.key.width = unit(2, "cm")
    ) 
    

### |-  plot 4 ----
p4 <- sensor_speed_clean |>
    ggplot(aes(x = date, y = avg_speed, color = site_id, group = site_id)) +
    # Add lines
    geom_line(linewidth = 1.0, alpha = 0.9) +

    # Scales 
    scale_y_continuous(
        breaks = seq(30, 50, by = 5),
        limits = c(25, 55),
        expand = c(0, 0)
    ) +
    scale_x_date(
        date_breaks = "1 week",
        date_labels = "%b %d",
        expand = c(0.02, 0)
    ) +
    scale_color_manual(
        values = c(
            "6867" = viz_colors$evening,    
            "7035" = viz_colors$orange,    
            "7042" = viz_colors$evening,   
            "7058" = viz_colors$orange  
        ),
        name = "Sensor ID"
    ) +
    # Customize legend
    guides(
        color = guide_legend(
            nrow = 1,
            byrow = TRUE
        )
    ) +
    # Labs
    labs(
        title = "Average Speed Trends by Sensor Location",
        subtitle = "Comparing speed patterns across different monitoring points",
        x = "Date",
        y = "Average Speed (mph)"
    ) +
    # Theme
    theme_traffic() +
    theme(
        panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),
        legend.key.width = unit(2, "cm"),
        legend.text = element_text(size = rel(0.9))
    ) 
   
### |-  combined plots ----
combined_plot <- (
    p1 + p2 + 
        plot_layout(widths = c(1, 1))
) / (
    p3 + p4
)

combined_plot <- combined_plot +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text  ,
        caption = caption_text,
        theme = theme(
            plot.title = element_text(
                family = "title", 
                size = rel(2.5), 
                face = "bold",
                hjust = 0.5,
                color = title_col,
                margin = margin(b = 10)
            ),
            plot.subtitle = element_text(
                family = "text",
                size = rel(1.3),
                hjust = 0.5,
                color = subtitle_col,
                margin = margin(b = 20)
            ),
            plot.caption = element_markdown(
                family = "caption",
                size = rel(0.75),
                color = caption_col,
                hjust = 0.5,
                margin = margin(t = 20)
            ),
            plot.margin = margin(10, 10, 10, 10),
            plot.background = element_rect(fill = bkg_col, color = bkg_col),
            panel.background = element_rect(fill = bkg_col, color = bkg_col)
        )
    ) &
    theme(
        legend.position = "top",
        legend.box = "vertical",
        legend.margin = margin(t = 10),
        legend.spacing = unit(1, "cm"),
        legend.key.width = unit(2, "cm"),
        legend.key.height = unit(0.3, "cm")
    )

7. Save

### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot_patchwork(combined_plot, type = "tidytuesday", year = 2024, week = 49, height = 10, width = 16)

8. Session Info

TipExpand 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] ggplotify_0.1.2   gghighlight_0.4.1 paletteer_1.6.0   patchwork_1.3.0  
 [5] here_1.0.1        glue_1.8.0        scales_1.3.0      skimr_2.1.5      
 [9] janitor_2.2.0     showtext_0.9-7    showtextdb_3.0    sysfonts_0.8.9   
[13] ggtext_0.1.2      lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1    
[17] dplyr_1.1.4       purrr_1.0.2       readr_2.1.5       tidyr_1.3.1      
[21] tibble_3.2.1      ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      pacman_0.5.1      
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.4        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] camcorder_0.1.0    repr_1.1.7         withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   cli_3.6.3          rmarkdown_2.29    
[29] crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0        
[33] commonmark_1.9.2   splines_4.4.0      parallel_4.4.0     base64enc_0.1-3   
[37] vctrs_0.6.5        yulab.utils_0.1.8  Matrix_1.7-0       jsonlite_1.8.9    
[41] gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0 
[45] magick_2.8.5       gifski_1.32.0-1    rematch2_2.1.2     codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] htmltools_0.5.8.1  R6_2.5.1           rprojroot_2.0.4    vroom_1.6.5       
[57] evaluate_1.0.1     lattice_0.22-6     markdown_1.13      gridtext_0.1.5    
[61] snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1      svglite_2.1.3     
[65] nlme_3.1-164       mgcv_1.9-1         xfun_0.49          fs_1.6.5          
[69] pkgconfig_2.0.3   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2024_49.qmd.

For the full repository, click here.