Code
#Pulling our data sets and needed packages
library(tidyverse)
library(dplyr)
library(ggplot2)
library(knitr)
library(gt)Public Waste Collection Fleet Management: A Survey Analysis
Introduction
The management of municipal solid waste (MSW) has become increasingly critical as urban populations grow and the demand for efficient waste collection services increases. From 2004 to 2019, studies showed a significant increase of 30% to 50% in MSW generation, contributing to a global average of approximately 2.7 billion tons (Maalouf & Mavropoulos, 2023) illustrating the escalating challenges in waste management globally. Looking at the United States (US) specifically, approximately 342.6 million tons of MSW were generated in US, rising in 347.0 million tons by 2013 (Environmental Research & Education Foundation, 2016).
In 2021, the Environment Research and Education Foundation (EREF) worked to address some of these challenges by conducting a comprehensive survey (Environmental Research & Education Foundation, 2021) focused on various waste haulers across the county, both public and private. This survey aimed to gain insights into the operational landscape of waste management providers, capturing data on fleet sizes, vehicle type, regional distribution, collection services provided, and other hauler operations.
This report will specifically explore responses in this survey provided by public haulers. Analysis will be performed to assess the number of waste collection vehicles surveyed, examining regional disparities in fleets/fleet sizes, and investigating waste types collected by these fleets. This analysis aims to provide a clearer understanding of the current state of municipal waste management in the United States functions to better address overall waste management challenges.
Methods
The data used in this project was sourced from a subset of responses gathered from a survey conducted by EREF. The original data set performed by EREF encompassed fleet characteristics and operational practices reported by both public and private waste haulers. The information reported by public haulers were extracted to create the data set used in this survey. Responses provided information such as resident state, fleet size, types of waste collection, and waste collection contracted used. To protect privacy of haulers, all data was anonymized and downloaded in a raw .csv form for for subsequent data analysis.
Each response entry includes the following: an anonymous fleet ID, the resident state (in full and abbreviated form), the number of trucks owned by the fleet, and waste collection services provided for garbage, recycling, yard waste, food waste, and bulky waste, by either a self-owned/self-operated service or a collection contract. Data was further aggregated to examine by waste type collected, regardless of contract type, and by contract type, regardless of waste type.
Import R Packages
Import Data
Code
publichaulerdata <- read_csv(here::here("data/raw/fleetmanagement_publicfleetsizes_20251022.csv"))Results
Code
#| label: tbl-trucks
#| tbl-cap: Summary Statistics on Trucks Counts Reported by Public Fleets
tbl_truck_summary <- publichaulerdata |>
group_by(state) |>
filter(!is.na(trucks_owned)) |>
summarise(n = n(),
mean_trucks = mean(trucks_owned),
sd_trucks = sd(trucks_owned),
min_trucks = min(trucks_owned),
median_trucks = median(trucks_owned),
max_trucks = max(trucks_owned)) |>
arrange(desc(mean_trucks))
gt(tbl_truck_summary) | state | n | mean_trucks | sd_trucks | min_trucks | median_trucks | max_trucks |
|---|---|---|---|---|---|---|
| Texas | 5 | 169.60000 | 116.089621 | 32 | 208.0 | 316 |
| Minnesota | 1 | 83.00000 | NA | 83 | 83.0 | 83 |
| California | 8 | 73.62500 | 67.009461 | 5 | 37.5 | 170 |
| Florida | 3 | 58.00000 | 25.159491 | 42 | 45.0 | 87 |
| Indiana | 3 | 51.33333 | 58.773577 | 13 | 22.0 | 119 |
| Arizona | 3 | 46.00000 | 35.552778 | 18 | 34.0 | 86 |
| North Carolina | 3 | 46.00000 | 25.000000 | 21 | 46.0 | 71 |
| Arkansas | 2 | 43.50000 | 3.535534 | 41 | 43.5 | 46 |
| Alabama | 1 | 40.00000 | NA | 40 | 40.0 | 40 |
| Missouri | 1 | 35.00000 | NA | 35 | 35.0 | 35 |
| South Carolina | 7 | 35.00000 | 47.003546 | 3 | 12.0 | 132 |
| Iowa | 2 | 29.00000 | 19.798990 | 15 | 29.0 | 43 |
| Wisconsin | 4 | 23.75000 | 13.696107 | 9 | 23.5 | 39 |
| Maryland | 1 | 20.00000 | NA | 20 | 20.0 | 20 |
| New York | 1 | 19.00000 | NA | 19 | 19.0 | 19 |
| South Dakota | 1 | 18.00000 | NA | 18 | 18.0 | 18 |
| New Jersey | 1 | 13.00000 | NA | 13 | 13.0 | 13 |
| Utah | 1 | 11.00000 | NA | 11 | 11.0 | 11 |
| Wyoming | 2 | 9.00000 | 1.414214 | 8 | 9.0 | 10 |
| Colorado | 1 | 7.00000 | NA | 7 | 7.0 | 7 |
| Virginia | 2 | 6.00000 | 1.414214 | 5 | 6.0 | 7 |
| Georgia | 1 | 5.00000 | NA | 5 | 5.0 | 5 |
| Tennessee | 2 | 4.50000 | 3.535534 | 2 | 4.5 | 7 |
| Idaho | 1 | 2.00000 | NA | 2 | 2.0 | 2 |
Summary statistics for reported public waste collection fleet sizes are presented in ?@tbl-trucks . These statistics include the mean, standard deviation, median, minimum, median, and maximum number of trucks reported by public fleets within each states. Only states with a reported fleet were included in this analysis.
Considerable variation in fleet sizes was observed across the states. Texas reported the largest average fleet size of 169.6 trucks, as well as the largest maximum fleet size of 300 vehicles, indicating a large concentration of public waste collection for this state. California and South Carolina were close behind with higher maximum truck values of 170 and 132 trucks, respectively. In contrast, several states reported very small fleets, such as Idaho with the lowest fleet size of only two vehicles.
Variability in fleet size also differed among states. Texas and California exhibited the largest standard deviations (116.1 and 67.0) suggesting more variability in those states. In contrast, states with fewer reporting entities, such as Virginia and Wyoming, showed minimal variability, though these results should be interpreted cautiously due to the survey’s limited sample size.
Median fleet sizes further illustrate disparities in public waste collection operations. Texas and Minnesota reported large median fleet sizes of 208 and 83 trucks, while states such as Tennessee and Idaho reported medians below five trucks. These findings highlight the wide range of fleet capacities among public waste haulers across the US.
Code
#| label: fig-services
#| fig-cap: "Percent of Fleets Providing Waste Collection Services"
services=c("Garbage","Recycling", "Yard Waste", "Food Waste", "Bulky Waste")
service_count=c(
sum(publichaulerdata$garbage == TRUE),
sum(publichaulerdata$recycling == TRUE),
sum(publichaulerdata$yardwaste == TRUE), sum(publichaulerdata$foodwaste == TRUE),
sum(publichaulerdata$bulkywaste == TRUE))
#Displays the results as a percentage of haulers that offer a specific collection service.
service_perc=service_count / 63 #total entry count = 63
service_data <- data.frame(services, service_count, service_perc)
service_data$services <- factor(service_data$services, levels = c("Garbage", "Recycling", "Yard Waste", "Food Waste", "Bulky Waste"))
ggplot(service_data, aes(x = services, y = service_perc)) +
geom_bar(stat = "identity", fill = "darkgreen") +
labs(title = "Percent of Fleets Providing Waste Collection Services",
x = "Waste Type",
y = "% of Fleets Providing Waste Collection Services") +
scale_y_continuous(labels = scales::percent) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
The distribution of waste collection services reported by public fleets is shown in ?@fig-services . Almost all surveyed fleets (96.8%) reported offering garbage collection services, confirming it as the primary service offered amongst haulers. Recycling, yard waste, and bulky waste services were also commonly offered, with approximately 80% or more of fleets providing each of these services.
Food waste collection was less common. Only 31.7% of surveyed fleets reported offering food waste collections services, indicating that organics diversion remained limited among public haulers.
Code
#| label: fig-avg-trucks-map
#| fig-cap: "Average Reported Trucks Counts"
us_states <- map_data("state")
us_counties <- map_data("county")
publichauler_state <- publichaulerdata %>%
filter(trucks_owned >=0) %>%
mutate(state=tolower(state)) %>%
group_by(state) %>%
summarize(trucks_avg= mean(trucks_owned,na.rm=TRUE))
us_states %>%
left_join(publichauler_state, by=c("region"="state")) %>%
ggplot(aes(x=long, y=lat, group=group, fill=trucks_avg)) +
geom_polygon(color = "black", linewidth = 0.1) +
coord_map(projection = "albers", lat0 = 45, lat1 = 55) +
scale_fill_gradient2(low = "antiquewhite", high = "darkgreen", name = "Average Reported Trucks") +
ggtitle("Average Reported Truck Counts") +
theme(
legend.position = "bottom",
axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank()
)
?@fig-avg-trucks-map presents a spatial visualization of the average reported fleet sizes by state. Texas is distinguished as having the highest average count of waste collection vehicles, followed by Minnesota and California. Other represented states had average fleet sizes below 60 vehicles and are clustered within a narrower range.
A substantial number of state (26) did not have fleets report data and are shown without values on the map. This is depicted by the color grey. This highlights a limitation of the data set and the need for more representation within the data set to improve geographic representation and analytical robustness.
Overall, the spatial distribution illustrates regional differences in public waste collection capacity, possibly driven by population size, geographic/municipal infrastructure, and waste generation.
Conclusions
The following conclusions regarding our survey data were made:
Public waste collection fleet sizes vary across the US, with Texas reporting the largest average and maximum fleet sizes among represented states. This possibly reflects differences in population, waste generation, and other municipal service demands.
Considerable variability exists within states, particularly with larger reported fleets. High standard deviations in states such as Texas and California indicate differences in fleet scale among public agencies operating within the same region.
While waste collection for garbage, recycling, and yard waste are widely offered, food waste collection remains limited. With fewer than a third of reporting fleets offering organics collection, this suggests a significant opportunity for expansion as organics diversion rises in waste management practices.
Geographic analysis illustrated uneven reporting across states, with several states represented by few or no respondents. This limits the ability to draw fully representative conclusions for US trends and highlights the need for increased fleet representation.
Overall, results indicate substantial variation in public waste collection operations across the US, reflection differences in operation practices and regional needs. Improved data coverage and continued participation in research efforts would strengthen analyses and support decision-making related to fleet management and waste diversion strategies.
References
Environmental Research & Education Foundation. (2016). MSW Management in the U.S.: 2010 & 2013.
Environmental Research & Education Foundation. (2021). Waste Collection Fleet Management Survey [Unpublished Raw Data]. In Environmental Research & Education Foundation.
Maalouf, A., & Mavropoulos, A. (2023). Re-assessing global municipal solid waste generation. Waste Management & Research, 41(4), 936–947. https://doi.org/10.1177/0734242X221074116