This is a "daily difference" dataset. See dataset attachment "FMCSA Dataset Description and Data Definitions - Select Datasets" for more information. Records for carriers/brokers/freight forwarders with active, inactive, or pending authorities (common or contract). It includes the DOT number and MC/FF/MX number for the carrier/broker/freight forwarder, along with company census data, e.g., types of authority, address, types of insurance on file, and amounts of insurance on file.

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

This table contains 140 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; ...), Fleet and equipment statistics (10 items: Total powered units; Straight trucks; Road tractors; All other powered units ...).

Registration information on interstate, intrastate non-hazmat, and intrastate truck and bus companies that operate in the United States and have registered with FMCSA. Contains contact information and demographic information (number of drivers, vehicles, commodities carried, etc).

This is the data and code accompanying "The Traveling Trucker Problem" in the AEA: Papers and Proceedings. The abstract of the article is:This paper documents three new stylized facts showing that truckers in Colombia frequently choose to make complex chains of shipments in a single trip before returning home. It then provides a new model of optimal trucker trip-chaining with a general geography that is consistent with these facts.

NCCDB is a web-based information system for recording and reporting on household goods, safety violation, hazardous material, cargo tank and passenger complaints. NCCDB allows the public and FMCSA staff to submit complaints using an online form. The database contains, among other information, reports on inspection and test of cargo tanks and inventory of tanks. These reports are used in the development and amendment to regulations of cargo security which is the protection of cargo from theft.

This file contains a list of licensed interstate truckers in Texas.

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.

Graph and download economic data for Employment for Transportation and Warehousing: General Freight Trucking, Long-Distance (NAICS 48412) in the United States (IPUIN48412W010000000) from 1987 to 2024 about general, freight, warehousing, trucks, NAICS, transportation, employment, and USA.

A stated preference survey of truck drivers collected nationally to capture driving behaviors from before and during the COVID-19 pandemic. The survey was administered through the University of Arkansas. Survey data was collected between May 25th and June 1st, 2020, utilizing Qualtrics, an online electronic survey instrument. Completing the survey was voluntary, but to participate respondents must have been at least 18 years of age, held a CDL, had been operating their commercial motor vehicle for more than a year and also during the COVID-19 pandemic. The survey included a total of 67 questions divided into nine parts: socioeconomic, business, driver, driving characteristics, safety perceptions, time of day operations, driving management, and truck configuration. A total of 521 truck drivers met the survey requirements and completed the survey.

Recent headlines depict significant shifts in operations within the freight community in particular, e.g., HOS laws suspended at a national level for the first time in 82 years1; national carriers shifting operations completely to grocery supply chains2; fleet operators laying off employees in response to manufacturing closures3. As a result of the current COVID-19 pandemic, there is a great need to capture freight movement data (not otherwise collected) to measure the effects of the COVID-19 response and recovery practices on freight network resiliency. In this project, we consider an expanded definition of the freight network, beyond roads and warehouses, to include truck drivers and driver support systems.

Driver support systems include physical infrastructure like public and private rest stops as well as operational protections like Hours of Service (HOS). COVID-19 responses by public agencies and private citizens have affected drivers and driver support systems by three mechanisms. First, increased demand for medical supplies, food and packaged goods creates a need for more trucks and drivers, and the increased need for quick shipments promotes an environment in which speeding and unsafe driving practices may prevail. Second, with HOS restrictions lifted by the National Highway Transportation Safety Administration (NHTSA) driver fatigue may occur at greater frequency leading to unsafe driving conditions and higher likelihood of accidents. Third, the effects of social distancing mandates can lead to closures of critical, but oft forgotten, freight infrastructure like rest areas and truck stops, leaving drivers without necessary rest opportunities. While any single mechanism has detrimental effects on driver health and safety, the economy, and national recovery efforts, when combined, the system can be pushed to failure. Pandemic responses have only exacerbated critical industry issues like driver shortages, lack of available parking, and HOS compliance issues stemming from electronic logbooks. The purpose of this work was to develop and implement a driver health and safety survey during the pandemic.

Graph and download economic data for Producer Price Index by Industry: General Freight Trucking, Long-Distance Truckload: General Freight Trucking, Long-Distance, Truckload (PCU4841214841212) from Jun 1992 to Jun 2025 about freight, trucks, PPI, industry, inflation, price index, indexes, price, and USA.

0 Na import shipment records of Trucker from United States with prices, volume & current Buyer’s suppliers relationships based on actual Na import trade database.

Nearly 50,000 or one in five (22%) Canadian truck drivers on the road in 1998 were independent truckers or "owner-operators". However, similar to other forms of self-employment, the net-earnings and socio-economic characteristics of owner-operators have often been ignored by researchers for reasons of analytical convenience or data limitations. New data products recently released by Statistics Canada such as the Survey of Labour and Income Dynamics (SLID) have the potential to fill much of this gap. The 1997 SLID cross-sectional micro-data files offer a limited but meaningful insight into the work patterns of the owner-operator population, complementing and validating well-established business surveys such as the annual Small for-hire carrier and Owner-operator Survey (SFO). The purpose of this study, through a multivariate analysis of the 1997 SLID and the 1997 SFO survey, was to compare the work patterns and backgrounds of owner-operators to company drivers (paid truck drivers employed by carriers). The study found that while drivers may choose to be self-employed to gain independence, owner-operators tend to work longer hours to meet fixed and variable costs, in return for lower after-tax earnings and a greater likelihood of high work-life stress. The analysis also found that the odds of self-employment among truckers were highest among drivers over 40 years of age with no post-secondary training.

Snapshot img

Truck-as-a-Service Market Size 2024-2028

The truck-as-a-service market size is forecast to increase by USD 13.87 billion at a CAGR of 4.09% between 2023 and 2028.

The Truck-as-a-Service (TaaS) market is experiencing significant growth, driven by the digital transformation in the trucking industry. This shift towards technology integration is enabling more efficient and cost-effective transportation solutions. One of the key trends shaping this market is the adoption of blockchain technology. Blockchain's ability to provide secure, transparent, and tamper-proof transactions is particularly beneficial in the trucking industry, enhancing supply chain visibility and reducing fraud. However, the market faces a significant challenge: the shortage of truck drivers. With an increasing demand for freight transportation, the lack of available drivers poses a major obstacle for TaaS providers.
This shortage necessitates innovative solutions, such as driver training programs, flexible work arrangements, and the integration of autonomous vehicles. Companies seeking to capitalize on the opportunities presented by the TaaS market must navigate these challenges effectively, leveraging technology to streamline operations and attract and retain skilled drivers.

What will be the Size of the Truck-as-a-Service Market during the forecast period?

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The market continues to evolve, driven by the integration of advanced technologies and service-oriented business models. Operational efficiency and cost optimization are key priorities for large enterprises in the trucking industry, leading to the adoption of digital transformation. Connected vehicles enable real-time data analytics for fuel consumption, vehicle maintenance, and driver safety. Remote diagnostics and predictive maintenance using artificial intelligence (AI) and machine learning optimize vehicle uptime and reduce carbon footprint. Shared mobility and on-demand transportation services are disrupting traditional freight transportation, while fleet management solutions leverage cloud computing for asset utilization and risk management.
The unfolding of these market activities shapes the commercial vehicle landscape, with heavy-duty trucks adopting autonomous capabilities and emissions reduction technologies. The ongoing evolution of the market reflects the dynamic nature of the transportation industry, as it continues to adapt to changing customer needs and regulatory requirements.

How is this Truck-as-a-Service Industry segmented?

The truck-as-a-service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Service

  Rental services
  Telematics and analytics
  Truck platooning


End-Use Industry

  Chemicals
  Pharmaceutical and healthcare
  FMCG
  Food and beverages
  Retail
  Others


Business Model

  Subscription-Based
  Pay-Per-Use
  Full-Service Leasing
  On-Demand Services


Application

  Last-Mile Delivery
  Long-Haul Transportation
  Regional Distribution
  Specialized Transport


Vehicle Type

  Light-Duty Trucks
  Medium-Duty Trucks
  Heavy-Duty Trucks


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Service Insights

The rental services segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the integration of advanced technologies such as connected vehicles, autonomous driving, and remote diagnostics. These innovations prioritize driver safety and operational efficiency, leading to cost optimization and improved vehicle uptime. The trucking industry is undergoing digital transformation, embracing service-oriented business models, and on-demand transportation solutions. Large enterprises are leveraging cloud computing, machine learning, and data analytics to optimize asset utilization and supply chain management. Predictive maintenance and emissions reduction are crucial aspects, reducing the carbon footprint and enhancing risk management.

Shared mobility and subscription services are gaining popularity, offering flexible and cost-effective solutions for commercial vehicles and freight transportation. Heavy-duty trucks are being equipped with AI and fuel consumption monitoring systems, ensuring optimal performance and reducing fuel consumption. The market trends indicate a focus on fleet management, transportation management, and big data analysis to improve overall operational efficiency and competitiveness.

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The Rental services segment was

The Company Safety Profile (CSP) contains safety-related information on an individual Company's operation, including selected items from inspection reports and crash reports and the results of any reviews or enforcement actions involving the requested company. For a more detailed description of the CSP and the information it contains, please select the CSP Definition Document link shown to the right. As an aid to performing a successful CSP Request, please select the CSP Help link shown to the right, particularly if you have not previously used this service. ProVu is a viewer which allows users to electronically analyze standard company safety profile reports. Data are available in a PDF as well as XML. FMCSA provides a tool, called ProVu, in order to read the XML data.

Graph and download economic data for Truck Tonnage Index (TRUCKD11) from Jan 2000 to May 2025 about tonnage, trucks, and USA.

This dataset provides information about the number of properties, residents, and average property values for Trucker Drive cross streets in Ararat, VA.

Graph and download economic data for Employed full time: Wage and salary workers: Driver/sales workers and truck drivers occupations: 16 years and over (LEU0254521700A) from 2000 to 2024 about occupation, trucks, full-time, salaries, workers, 16 years +, wages, employment, and USA.

This dataset presents data on a cross-sectional study conducted at the Department for Applied Nutritional Psychology (180d) at the University of Hohenheim. Aim of the study was to examine food choice patterns of long-haul truck drivers driving through Germany. Data were assessed based on a self-developed self-report questionnaire in May/June 2018 on truck stops in Southern Germany. The uploaded dataset contains the raw data of the study relevant for the according published article, available as IBM SPSS data file. The English version of the questionnaire is available as pdf-file.