Graph and download economic data for All Employees, Truck Transportation (CES4348400001) from Jan 1990 to Sep 2025 about warehousing, trucks, transportation, establishment survey, employment, and USA.

The graph illustrates the number of truck drivers in the United States from 1997 to 2024. The x-axis represents the years, ranging from 1997 to 2024, while the y-axis denotes the number of truck drivers, spanning from 2,247,000 in 2010 to 3,064,890 in 2023. Throughout this period, the number of truck drivers generally increased, starting at 264,258 in 1997 and reaching its highest point in 2024. Notable fluctuations include significant decreases in 1998 and 2002, followed by steady growth in subsequent years. Overall, the data exhibits an upward trend in the number of truck drivers over the 27-year span. This information is presented in a line graph format, effectively highlighting the annual changes and long-term growth in truck driver numbers in the United States.

This table contains 56 series, with data for years 2009 -2010 (not all combinations necessarily have data for all years), and was last released on 2015-02-06. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Prince Edward Island; New Brunswick; ...), Trucking industry employment statistics (4 items: Total salaried employees; Salaried truck drivers; Owner operators; All other salaried employees).

The statistic shows the employment in U.S. freight trucking industry from 1990 to 2020. In 2020, over *** million people were employed in the truck transportation industry in the United States.

Graph and download economic data for Employed full time: Wage and salary workers: Driver/sales workers and truck drivers occupations: 16 years and over: Men (LEU0254628500A) from 2000 to 2024 about occupation, trucks, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

This statistic shows the number of employees in the U.S. local specialized freight trucking industry from 2018 through 2021. This industry employed roughly ******* employees in 2020. This value is expected to increase to ******* in 2021.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for truck driver in the U.S.

The statistic shows the employment in U.S. freight trucking industry from 1990 to 2020, broken down by segment. In 2020, just over *********** people were employed in general freight trucking positions in the United States.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for heavy tractor trailer truck drivers in the U.S.

In 2020, the U.S. long-distance trucking market employed roughly **** million people. Over the recent years, the number of people employed in the long-distance trucking industry experienced an increasing trend, with the exception of 2020, when the coronavirus pandemic started.

In 2020, the U.S. local trucking market employed roughly ******* people. Over the recent years, the number of people employed in the local trucking industry experienced an increase despite the coronavirus (COVID-19) pandemic.

United States Employment: NF: TW: Other Specialized Trucking: Local data was reported at 226.200 Person th in May 2018. This records an increase from the previous number of 220.700 Person th for Apr 2018. United States Employment: NF: TW: Other Specialized Trucking: Local data is updated monthly, averaging 188.500 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 235.700 Person th in Aug 2014 and a record low of 138.700 Person th in Feb 1992. United States Employment: NF: TW: Other Specialized Trucking: Local data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

United States Employment: NF: sa: TW: General Freight Trucking (GFT) data was reported at 1,015.900 Person th in May 2018. This records an increase from the previous number of 1,013.300 Person th for Apr 2018. United States Employment: NF: sa: TW: General Freight Trucking (GFT) data is updated monthly, averaging 946.700 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 1,016.400 Person th in May 2007 and a record low of 790.500 Person th in Feb 1992. United States Employment: NF: sa: TW: General Freight Trucking (GFT) data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

United States Employment: NF: TW: Specialized Freight Trucking data was reported at 470.500 Person th in Sep 2018. This records a decrease from the previous number of 473.200 Person th for Aug 2018. United States Employment: NF: TW: Specialized Freight Trucking data is updated monthly, averaging 395.200 Person th from Jan 1990 (Median) to Sep 2018, with 345 observations. The data reached an all-time high of 477.100 Person th in Jul 2015 and a record low of 300.600 Person th in Feb 1992. United States Employment: NF: TW: Specialized Freight Trucking data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G024: Current Employment Statistics Survey: Employment: Non Farm.

United States Employment: NF: sa: TW: Specialized Freight Trucking data was reported at 458.500 Person th in May 2018. This records an increase from the previous number of 457.500 Person th for Apr 2018. United States Employment: NF: sa: TW: Specialized Freight Trucking data is updated monthly, averaging 392.600 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 462.700 Person th in Jun 2015 and a record low of 307.900 Person th in Jan 1992. United States Employment: NF: sa: TW: Specialized Freight Trucking data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

Abstract

Trucking Industry Survey as part of a major World Bank initiative called the Africa Infrastructure Country Diagnostic (AICD) study. The survey is carried out in several countries where the World Bank provides development loans. It is increasingly recognized that infrastructure services provide a critical platform for private sector activity and international trade. The trucking industry provides vital transportation services that facilitate both internal and external trade for the other productive sectors. The efficiency and quality of the services provided by the trucking industry is thus an important contributor to country competitiveness. In addition, as a major user of road infrastructure, trucking firms are uniquely placed to assess the functioning of road corridors. The objective of this study is to achieve a major improvement in the country level knowledge base of the infrastructure sectors in the region. The information obtained through the survey is precious as it will provide a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from the current increase in financial flows. It should also provide a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa.

Catalog of Trucking Surveys for nine sub-Saharan Africa countries is maintained by the Africa Transport unit (AFTTR).

The trucking surveys include nine sub-Saharan African countries. Each survey contains data for approximately 20 trucking companies and 60 truck owner-operators. Seven of the nine national surveys (i.e. Cameroon, Chad, Ghana, Burkina Faso, Kenya, Uganda and Zambia) were conducted for the "Transport Prices and Costs in Africa: A Review of the Main International Corridors" study by Supee Teravaninthorn and Gael Raballand (2008) mostly focusing on the trucking service on international corridors. The other two national trucking surveys (Malawi and Northern Mozambique) follow a slightly different approach with respect to sample selection mostly focusing on the link between the high agro-producing towns, the major cities and the exporting ports and using a revise survey instrument. All the surveys were performed by the Etude Economique Conseil (EEC) in 2007 and 2008 and financed by the World Bank.

Geographic coverage

The Trucking Survey in Uganda targeted trucking companies and companies conducting their own transportation.

Analysis unit

• Truckers/ Truking Operators
• Trucking Companies

Kind of data

Sample survey data [ssd]

Sampling procedure

A trucking company is defined as a company that conducts trucking as its main operation and that has five or more full-time paid employees. A company conducting its own transportation is a company for whom trucking is not its main operation, that conducts the majority of its own transportation and that has five or more full-time paid employees. The companies surveyed serve at least one of the following routes: o Kampala-Nairobi o Kampala-Eldoret o Kampala-Dar es salaam o Kampala-Mombassa o Kampala-Entebbe o Kampala-Malaba o Kampala-Mwanza o Kampala-Kigali

The survey also sampled a selection of truckers (trucking operators with less than five full-time permanent paid employees) that serve the main roads listed above.

Companies with five or more full-time paid permanent employees: A list of Ugandan trucking operators was obtained from the Statistical Office. This list was completed and updated during the pilot survey. Following the results of the validation process, a sample frame consisting of a population of 47 establishments was drawn.

An attempt was made to contact each of these establishments. During the survey, it resulted that 4 establishments were closed, 14 establishments were out of scope or were unreachable despite repeated attempts by phone, 8 establishments refused to participate, and 21 establishments agreed to participate resulting in 21 completed trucking questionnaires, among which 4 trucking companies conducting their own transportation.

Truckers In this survey, the trucker's stratum covers all establishments of the trucking industry with less than 5 employees. For many reasons, including the small size of establishments, their expected high rate of turnovers, the high level of ?informality? of establishments and consequently the difficulty to obtain trustworthy information from official sources, EEC Canada selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum according to the roads to be covered.

First, to randomly select individual truckers establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there are lorry parks or where truckers usually off-loading; ii) count all truckers which generally stop in these specific lorry parks; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to our national sources. The EEC team then went in the field to verify these national sources and to count truckers. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At head office the following procedure was followed: the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select truckers to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Uganda, enumerators were sent to each zone with instructions as to how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and consistency check 1) When data entry was finished for the day, for each type of questionnaire for which additional cases were entered or existing cases were updated, that data file were exported to SPSS format using the provided export utility. 2) The resulting SPSS script was run to open the data in SPSS. 3) The consistency and completion tests script was run in order to generate data regarding the completion status of each case with respect to the consistency checks, and to generate a report detailing these results as well as the completion status of the whole sample with respect to sales.

Kaggle Dataset: Synthetic Logistics Operations Database (2022-2024)

About this Dataset

What's Inside

A complete operational database from a fictional Class 8 trucking company spanning three years. This isn't scraped web data or simplified tutorial content—it's a realistic simulation built from 12 years of real-world logistics experience, designed specifically for analysts transitioning into supply chain and transportation domains.

The dataset contains 85,000+ records across 14 interconnected tables covering everything from driver assignments and fuel purchases to maintenance schedules and delivery performance. Each table maintains proper foreign key relationships, making this ideal for practicing complex SQL queries, building data pipelines, or developing operational dashboards.

Who This Is For

SQL Learners: Master window functions, CTEs, and multi-table JOINs using realistic business scenarios rather than contrived examples.

Data Analysts: Build portfolio projects that demonstrate understanding of operational metrics: cost-per-mile analysis, fleet utilization optimization, driver performance scorecards.

Aspiring Supply Chain Analysts: Work with authentic logistics data patterns—seasonal freight volumes, equipment utilization rates, route profitability calculations—without NDA restrictions.

Data Science Students: Develop predictive models for maintenance scheduling, driver retention, or route optimization using time-series data with actual business context.

Career Changers: If you're moving from operations into analytics (like the dataset creator), this provides a bridge—your domain knowledge becomes a competitive advantage rather than a gap to explain.

Why This Dataset Exists

Most logistics datasets are either proprietary (unavailable) or overly simplified (unrealistic). This fills the gap: operational complexity without confidentiality concerns. The data reflects real industry patterns:

• Fuel prices track the 2022 diesel spike and 2023-2024 decline
• Driver turnover sits at 15% annually (industry standard)
• Equipment utilization averages 65% (typical for dry van operations)
• On-time delivery performance ranges 85-95% (realistic service levels)
• Maintenance intervals follow Class 8 PM schedules

Dataset Structure

Core Entities (Reference Tables): - Drivers (150 records) - Demographics, employment history, CDL info - Trucks (120 records) - Fleet specs, acquisition dates, status - Trailers (180 records) - Equipment types, current assignments - Customers (200 records) - Shipper accounts, contract terms, revenue potential - Facilities (50 records) - Terminals and warehouses with geocoordinates - Routes (60+ records) - City pairs with distances and rate structures

Operational Transactions: - Loads (57,000+ records) - Shipment details, revenue, booking type - Trips (57,000+ records) - Driver-truck assignments, actual performance - Fuel Purchases (131,000+ records) - Transaction-level data with pricing - Maintenance Records (6,500+ records) - Service history, costs, downtime - Delivery Events (114,000+ records) - Pickup/delivery timestamps, detention - Safety Incidents (114 records) - Accidents, violations, claims

Aggregated Analytics: - Driver Monthly Metrics (5,400+ records) - Performance summaries - Truck Utilization Metrics (3,800+ records) - Equipment efficiency

Key Features

Temporal Coverage: January 2022 through December 2024 (3 years)

Geographic Scope: National operations across 25+ major US cities

Realistic Patterns: - Seasonal freight fluctuations (Q4 peaks) - Historical fuel price accuracy - Equipment lifecycle modeling - Driver retention dynamics - Service level variations

Data Quality: - Complete foreign key integrity - No orphaned records - Intentional 2% null rate in driver/truck assignments (reflects reality) - All timestamps properly sequenced - Financial calculations verified

Use Case Examples

Business Intelligence: Create executive dashboards showing revenue per truck, cost per mile, driver efficiency rankings, maintenance spend by equipment age, customer concentration risk.

Predictive Analytics: Build models forecasting equipment failures based on maintenance history, predict driver turnover using performance metrics, estimate route profitability for new lanes.

Operations Optimization: Analyze route efficiency, identify underutilized assets, optimize maintenance scheduling, calculate ideal fleet size, evaluate driver-to-truck ratios.

SQL Mastery: Practice window functions for running totals and rankings, write complex JOINs across 6+ tables, implement CTEs for hierarchical queries, perform cohort analysis on driver retention.

Sample Questions to Explore

1. Which routes generate the highest profit margin after fuel costs?
2. How does driver tenure correlate with fuel ef...

United States AHE: sa: PW: TW: General Freight Trucking, Long-distance LTL data was reported at 30.760 USD in Nov 2022. This records an increase from the previous number of 29.770 USD for Oct 2022. United States AHE: sa: PW: TW: General Freight Trucking, Long-distance LTL data is updated monthly, averaging 19.290 USD from Jan 1990 (Median) to Nov 2022, with 395 observations. The data reached an all-time high of 30.760 USD in Nov 2022 and a record low of 14.650 USD in Nov 1990. United States AHE: sa: PW: TW: General Freight Trucking, Long-distance LTL data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

The program helps motor carriers make more informed hiring decisions by providing electronic access to driver crash and inspection history from the FMCSA Motor Carrier Management Information System (MCMIS). PSP records are now available for motor carriers and commercial drivers. PSP is designed to assist the motor carrier industry in assessing individual operator crash and serious safety violation history as a pre-employment condition. A carrier will pay $10 for each requested driver history. An annual subscription fee of $100 also applies. Carriers with fewer than 100 power units qualify for a discounted annual fee of $25 per year.

This statistic shows the leading jobs that do not require a ********* degree in the United States as of 2024, ranked by the projected number of new job openings by 2032. By 2032, there are projected to be ******* new jobs created for delivery truck drivers in the U.S., making it the leading job that does not require a ********* degree.