Traffic on A64 Highway

library(tidyverse) # ggplot, lubridate, dplyr, stringr, readr...
library(praise)

The Data

This week we’re exploring National Highways Traffic Flow data! National Highways operates and maintains motorways and major A roads in England. They directly monitor the speed and flow of roads using on road sensors, and the data can be accessed via the National Highways API.

This week’s data has vehicle size and speed information for May 2021 from four different road sensors on the A64 road.

• Do vehicles travel faster on certain days or at certain times?
• What time of day do large vehicles use this road?
• Do smaller vehicles travel faster?

A64_traffic <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-12-03/A64_traffic.csv') |> 
  mutate(dow = wday(`Report Date`, label = TRUE)) |> 
  mutate(holiday = ifelse(`Report Date` == ymd("2021-05-03") | `Report Date` == ymd("2021-05-31"), TRUE, FALSE)) |> 
  mutate(status = ifelse(`Report Date` < "2021-05-17", "covid_strict", "covid_free")) |> 
  mutate(label = case_when(
    SiteId == 6867 ~ "Norton-on-Derwent",
    SiteId == 7035 ~ "Between B1249 and A1039",
    SiteId == 7042 ~ "York (north)",
    SiteId == 7058 ~ "Filey"
  ))

A64_traffic |> 
  filter(dow == "Mon") |> 
  filter(SiteId == 6867) |> 
  ggplot(aes(x = `Time Period Ending`, y = `Total Volume`, color = holiday, group = `Report Date`, linetype = status)) + 
  geom_line() + 
  theme_bw()

A64_traffic |> 
  #filter(SiteId == 6867) |> 
  ggplot(aes(x = hour(`Time Period Ending`), y = `0 - 520 cm`, group = `Report Date`, linetype = status)) + 
  geom_smooth(se = FALSE, color = "orange") + 
  geom_smooth(aes(y = `1160+ cm`*10), color = "blue", se = FALSE) + 
  geom_vline(xintercept = 8, color = "grey") + 
  geom_vline(xintercept = 12, color = "grey") + 
  geom_vline(xintercept = 17, color = "grey") + 
  facet_grid(label~dow) + 
  scale_y_continuous(
    
    # Features of the first axis
    name = "Number of passenger vehicles",
    
    # Add a second axis and specify its features
    sec.axis = sec_axis(~./10, name="Number of big trucks")
  )  + 
  theme_bw() +
  theme(
    axis.title.y = element_text(color = "orange", size=13),
    axis.title.y.right = element_text(color = "blue", size=13)
  ) +
  labs(title = "More driving after COVID restrictions lifted",
       x = "hour of the day",
       color = "type of vehicle",
       linetype = "COVID restriction") 

The dashed lines represent days in May of 2021 where a COVID lockdown was in place. The left axis (in orange) represents the number of passenger vehicles at different places on the road. The right axis (in blue) represents the number of big trucks at different places on the road. On May 17, 2021, the lockdown was lifted. We see that the orange passengers cars started driving more after the lockdown was lifted. The larger vehicles did not change much across the restrictions, presumably because they were already out and about doing their work regardless of the restrictions. The grey vertical lines are at 8am, noon, and 5pm.

A64_traffic |> 
  #filter(SiteId == 6867) |> 
  ggplot(aes(x = `Time Period Ending`, y = `Avg mph`, group = `Report Date`, 
             linetype = status, color = as.factor(label))) + 
  geom_smooth(se = FALSE) + 
  geom_vline(xintercept = hms("08:00:00"), color = "grey") + 
  geom_vline(xintercept = hms("12:00:00"), color = "grey") + 
  geom_vline(xintercept = hms("17:00:00"), color = "grey") + 
  facet_grid(label~dow) + 
  labs(title = "More driving after COVID restrictions lifted",
       color = "location of traffic sensor",
       linetype = "COVID restriction") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust=1)) 

The dashed lines represent days in May of 2021 where a COVID lockdown was in place. For each location and day of the week, the average speed across the day is plotted. The grey vertical lines are at 8am, noon, and 5pm.

praise()

[1] "You are majestic!"