Do Americans have an obsession with big vehicles?

In recent years, there has been growing concern over the increasing size of vehicles on American roads, referred to as “car bloat.” One key reason for this is a regulatory loophole in U.S. fuel economy standards that allows larger vehicles to meet less stringent efficiency requirements compared to smaller cars. This loophole, established under the Corporate Average Fuel Economy (CAFE) standards, has incentivized automakers to prioritize the production of heavier vehicles to meet fuel efficiency goals more easily. As a result, car bloat has contributed not only to increased emissions but also to concerns about road safety. Few trends have been worse for the environment, people’s health, and public safety.

With SUVs and trucks dominating sales, understanding the U.S. vehicle fleet’s shift toward larger models is crucial for informing public safety regulations, consumer choices, and urban planning.

Research Questions

1. How have the sizes of the vehicles that make up the US fleet changed over time?
   
2. What environmental impacts arise from the growing size of vehicles, considering their footprint?
   

Data sources

The primary source of data for this project is a website called Car and Driver, which provides detailed data on vehicle weight, size, and safety features for a wide variety of models. The website provides comparisons of vehicles– eg. dimensions, weight, and safety ratings– across categories such as sedans, SUVs, and trucks. The information on the website is generally original data compiled from automakers and third-party testing. The data should be reliable for comparing vehicle size, weight, and safety features, but the specifications might be pre-processed to include summaries or aggregated results, which may lack context about real-world performance in crashes.

The data used in this analysis was sourced from the Jhelvy GitHub repository, where it was scraped, cleaned, and made available by Professor Helveston. For the purpose of this project, we did a lot of data cleaning to filter out the vehicle types and particular specifications that we needed.

car_data <- read_csv(here(“data_raw”, “main_data.csv”))

relevant_columns <- c("style", "year", "msrp", "wheelbase_inches_2", "length_inches_2", "width_without_mirrors_inches_2", "height_inches_2")

We also got data about the 25 Bestselling Cars, Trucks, and SUVs of 2024 So Far and the estimated number of each vehicle sold from the same website.

bestsellingcars_2024 <- c("Honda HR-V", "Toyota Tundra", "Kia Sportage", "Nissan Sentra", "Honda Accord", "Subaru Outback", "Toyota Tacoma", "Subaru Forester", "Subaru Crosstrek", "Chevrolet Equinox", "Hyundai Tucson", "Ford Explorer", "Chevrolet Trax", "Jeep Grand Cherokee", "Toyota Corolla", "Honda Civic", "Nissan Rogue", "Toyota Camry", "GMC Sierra 1500", "GMC Sierra 2500", "GMC Sierra 3500", "Ram Pickup", "Honda CR-V", "Tesla Model Y", "Toyota RAV4", "Chevrolet Silverado", "Ford F-Series")

bestSellingCarData <- car_data %>% 
  filter(Style %in% bestsellingcars_2024) %>%
  clean_names() %>% 
  select(relevant_columns) %>% 
# The GMC Sierra is one of the best selling cars of 2024, but it encompasses the light-duty 1500 and the heavy-duty 2500 and 3500 models, so we will rename the style for these vehicles to "GMC Sierra"
 mutate(
   style = ifelse(style %in% c("GMC Sierra 1500", "GMC Sierra 2500", "GMC Sierra 3500"), "GMC Sierra", style),
   msrp = parse_number(msrp)
        ) 
grouped_data <- bestSellingCarData %>% 
  group_by(style, year) %>% 
  summarise(
    msrp = mean(msrp, na.rm = TRUE),
    wheelbase_inches_2 = mean(wheelbase_inches_2, na.rm = TRUE),
    length_inches_2 = mean(length_inches_2, na.rm = TRUE),
    width_without_mirrors_inches_2 = mean(width_without_mirrors_inches_2, na.rm = TRUE),
    height_inches_2 = mean(height_inches_2, na.rm = TRUE))

In addition to this data, by observation and a Google search for confirmation, we created a spreadsheet with the 25 Bestselling Vehicles, number of units sold, and vehicle type (Car/ Truck/ SUV) and added that to the main data.

car_types_data <- read_excel(here("data_raw","style_and_units.sold.xlsx"))

main_df <- left_join(grouped_data, car_types_data, by = "style")
write_csv(main_df, here("data_processed", "main_df.csv"))

Shiny Table of Data

Preliminary Charts

Number of vehicle types

```{r}
# Count the number of each type of vehicle
vehicle_counts <- main_df %>%
  group_by(type) %>%
  summarise(count = n())

ggplot(vehicle_counts, aes(x = type, y = count)) +
  geom_col(show.legend = FALSE) + 
  theme(
    axis.title = element_blank(),
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  ) +
  labs(
    title = 'Number of Vehicles by Type'
  )

This is a basic representation of the number of each vehicle type (Cars, SUVs, and Trucks) in the list of bestselling vehicles in the U.S. Observe that the number of SUVs is more than five times that of regular passenger cars. This suggests that larger vehicles, particularly SUVs, are favored by consumers, possibly due to factors like perceived safety, utility, or marketing influence, which could be contributing to the growing trend of “car bloat” in the U.S. vehicle fleet.

Footprint Over time by Vehicle type

# First, we calculate the mean for each year across all vehicles
footprint_data <- main_df %>%
  group_by(year) %>%
  mutate(
    mean_footprint = mean(footprint, na.rm = TRUE)
  )


ggplot(footprint_data) +
  geom_line(
    aes(x = year, y = footprint, group = style),
    color = 'grey', alpha = 0.3
  ) +
  geom_line(
    aes(x = year, y = mean_footprint),
    size = 0.8, color = 'black'
  ) +
  annotate(
    'text', x = min(footprint_data$year) + 9, y = mean(footprint_data$mean_footprint, na.rm = TRUE) + 100,
    hjust = 0, label = 'US Mean', color = 'black'
  ) +
  theme_minimal() +
   theme(
    axis.title.x = element_text(face = "bold", hjust = 0), 
    axis.text.y = element_text(hjust = 1, size = 9),
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 13, face = "italic")) +
  labs(
      x = NULL,
    y = NULL,
    title = 'Trends in Vehicle Footprint (2007 - 2023)',
    subtitle = 'How is the environmental footprint of vehicles changing over time?'
  ) +
  scale_y_continuous(
    limits = c(12500, 14500)
  )

As expected, the trend shown in the chart indicates an overall increase in the environmental footprint of vehicles over the past several years. This could be due to several factors, including a shift in consumer preference toward larger vehicles like SUVs and trucks, which have a larger physical footprint compared to smaller cars. Automakers are designing vehicles with more space and advanced features, which can increase their size and weight. Regulatory changes, such as those around fuel economy standards possible play a role, as larger vehicles are sometimes subject to less stringent efficiency requirements.
This suggests that the US vehicle fleet is becoming bulkier over time, which could contribute to higher resource consumption and environmental impact, making it important to consider how it affects sustainability efforts.

Trend in Vehicle height

# First, we calculate mean height for each year across all vehicles
car_heights <- main_df %>%
  group_by(year) %>%
  mutate(
    mean_height = mean(height_inches_2, na.rm = TRUE)
  )


ggplot(car_heights) +
  geom_line(
    aes(x = year, y = height_inches_2, group = style),
    color = 'grey', alpha = 0.3
  ) +
  geom_line(
    aes(x = year, y = mean_height),
    size = 0.8, color = 'black'
  ) +
  annotate(
    'text', x = min(car_heights$year) + 1, y = max(car_heights$mean_height, na.rm = TRUE) - 2,
    hjust = 0, label = 'US Mean', color = 'black'
  ) +
  theme_minimal() +
  labs(
    x = 'Year',
    y = 'Height (inches)',
    title = 'Trends in Vehicle Heights (2007 - 2024)',
    subtitle = 'Height changes for top 25 bestselling vehicles, with US mean height line'
  )

Contrary to what we expected, the general trend of vehicle heights over the years shows a reduction in both that of individual vehicles and the average height of the US fleet. We will try to plot a similar graph using a calculated “volume,” which is the length * breadth * height of each vehicle.

main_df <- main_df %>%
  mutate(volume = length_inches_2 * width_without_mirrors_inches_2 * height_inches_2)

us_mean_volume <- main_df %>%
  group_by(year) %>%
  summarise(mean_volume = mean(volume, na.rm = TRUE))

ggplot(main_df) +
  geom_line(aes(x = year, y = volume, group = style), color = 'grey', alpha = 0.3) +
  geom_line(data = us_mean_volume, aes(x = year, y = mean_volume), color = 'black', size = 1) +
  annotate('text', x = max(main_df$year) - 10, y = max(us_mean_volume$mean_volume) * 1.05, hjust = 0,
           label = 'U.S. Mean Volume', color = 'black') +
  labs(y = 'Volume (cubic inches)', x = 'Year') +
  theme_minimal() +
  ggtitle("Change in Vehicle Volume Over Time for Top 25 Vehicles")

We computed a variable called volume to show the change in overall size of vehicles, and it confirmed that the overall increase in vehicle size is significant and consistent. We observed a clear upward trend in the average size of the top-selling vehicles over the years. This highlights the physical growth of vehicles in terms of length, width, and height, and it also reinforces the broader pattern of “car bloat” in the market. Larger vehicles, particularly SUVs and trucks, have become more dominant, pushing the overall fleet size higher.

The data suggests that this growth is not merely a function of changing consumer preferences for more spacious, versatile vehicles, but also a result of broader industry shifts, such as the move towards vehicles designed to meet fuel efficiency standards that inadvertently favor larger models. This poses challenges for environmental sustainability, as it contributes to higher fuel consumption, greater emissions, and more severe collisions. Additionally, it strains urban infrastructure, leading to issues such as overcrowded parking and increased road maintenance needs.

Conclusion

The growing trend of “car bloat” in the United States, characterized by an increasing preference for larger vehicles, has significant implications for both the environment and public safety. Our analysis of the 25 bestselling vehicles of 2024 reveals a clear shift toward SUVs and trucks, which now dominate the market. These vehicles, while popular for their perceived safety, utility, and marketing appeal, contribute to a larger environmental footprint due to their increased size and weight.

The data shows that over the years, despite some fluctuations, both the footprint and volume of vehicles in the U.S. have increased, particularly since the introduction of Corporate Average Fuel Economy (CAFE) standards that favor larger vehicles. This shift is not just a reflection of consumer preferences, but also of the regulatory environment and automakers’ strategies in meeting fuel efficiency targets. As larger vehicles proliferate, concerns about road safety, environmental sustainability, and urban infrastructure strain become more pronounced.

The increase in vehicle size contributes to higher resource consumption, greater emissions, and more severe accidents in the event of collisions. Urban planning challenges, such as parking shortages and road wear, are also exacerbated by these larger vehicles. Moving forward, addressing “car bloat” may require both regulatory changes to encourage smaller, more efficient vehicles and a cultural shift in consumer preferences toward more sustainable transportation options. The future of the U.S. vehicle fleet will depend on balancing consumer demand with environmental and safety concerns to mitigate the negative impacts of car bloat.

Limitations

While this analysis provides valuable insights into the trend of “car bloat” in the United States, we acknowledge that there are several limitations to the data:

• The footprint and volume metrics were derived from vehicle dimensions but may not account for factors like weight distribution, materials used, or other physical design elements influencing environmental impact.
• Our analysis focuses primarily on the bestselling vehicles of 2024, and historical data for the past decade or more was unavailable. A more extended time frame could have offered a better understanding of how car sizes have changed over longer periods.

Data Dictionary

The cleaned data file is named main_df. The table below describes each variable:

variable	description
style	Model style of the vehicle
year	Year of the model’s release or manufacture
msrp	Manufacturer’s suggested retail price (in USD), averaged per “style” and “year”
wheelbase_inches_2	Distance between front and rear axles in inches, representing the vehicle’s wheelbase size
length_inches_2	Total length of the vehicle in inches
width_without_mirrors_inches_2	Width of the vehicle without mirrors in inches
height_inches_2	Height of the vehicle in inches
type	Classification of the vehicle as “Car”, “Truck”, or “SUV”
units_sold	Estimated number of units sold in 2024 for each vehicle “style”
volume	Calulated vehicle volume (length c breadth x height)

Authors: Emmanuel Agbeko Enyo and Ifeoluwa Olaniyan