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 ...).

The database contains detailed information, such as: brands, Enrollment, Plating date, Model, Model year, Vehicle type, engine displacement, market share per brand and per number of vehicles circulating in each of the countries where available.

The data compiled shows the different characteristics of the vehicles circulating in each of the countries where the data is available.

This product leverages first-party and third party data sources. It provides anonymized statistics helpful as traffic data to: - measure the overall truck & vans activity in Europe and in the UK (depending on the source), - provide insights about origins and destinations of trucks & vans across Europe, in the UK between cities, communities... - identify stops and standstill areas of trucks, vans and all types of vehicles

All these traffic data statistics can feed various use cases: - traffic study for mobility advisory firms (Origins, Destinations, Stops / Standstill) that try to understand how the EV trucks are going to develop - help with marketing and geomarketing use cases to identify where to build / open a new branch or site - help explain and / or predict performance of businesses across geographies

Use cases: ==> Traffic analytics: Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage ==> Site-selection / marketing : Our data help companies looking to open EV charging stations, new shops and stores where the traffic is adapted to their business ==> Dynamic pricing / marketing & geomarketing : Our data help companies adjust prices across geographies

Comprehensive dataset of 85,152 Trucking companies in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

At Kestrel Insights (KI) we supply and maintain a geofence data warehouse for our customers in the transportation and logistics industries. By providing access to a robust and continuously maintained multi-tier data set of geofences, KI allows your team to reallocate limited resources to those areas most valuable to your business. Make KI’s Geofence Data Warehouse your trusted data source for unlocking the full potential of business analytics.

• Access our existing database of polygon geofences
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Brazil Vehicles Fleet: Tractor Truck: Southeast: São Paulo data was reported at 174,014.000 Unit in Feb 2019. This records an increase from the previous number of 173,281.000 Unit for Jan 2019. Brazil Vehicles Fleet: Tractor Truck: Southeast: São Paulo data is updated monthly, averaging 112,144.000 Unit from Dec 2002 (Median) to Feb 2019, with 195 observations. The data reached an all-time high of 174,014.000 Unit in Feb 2019 and a record low of 53,490.000 Unit in Dec 2002. Brazil Vehicles Fleet: Tractor Truck: Southeast: São Paulo data remains active status in CEIC and is reported by National Traffic Department. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAK016: Vehicle Fleet: by Region: Tractor Truck. Tractor Truck: Motor vehicle designed to pull or drag another.

Brazil Vehicles Fleet: Truck: Southeast: São Paulo data was reported at 680,182.000 Unit in Feb 2019. This records an increase from the previous number of 679,638.000 Unit for Jan 2019. Brazil Vehicles Fleet: Truck: Southeast: São Paulo data is updated monthly, averaging 584,907.000 Unit from Dec 2002 (Median) to Feb 2019, with 195 observations. The data reached an all-time high of 680,182.000 Unit in Feb 2019 and a record low of 438,862.000 Unit in Dec 2002. Brazil Vehicles Fleet: Truck: Southeast: São Paulo data remains active status in CEIC and is reported by National Traffic Department. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAK021: Vehicle Fleet: by Region: Truck. Truck: Motor vehicle designed to transport cargo, with bodywork, and total gross weight exceeding 3,500 kg.

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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

Dataset containing information on location and usage of heavy-duty vehicles operated in New York State by large private entities and by government agencies and municipalities

This product leverages first-party and third party mobility data sources. It provides anonymized statistics helpful to markting use cases: - measure the overall truck activity in Europe and in the UK (depending on the source), potentially potentially by type of goods transported (works, goods transportation, refrigerated...) - provide insights about origins and destinations of vehicles & trucks across Europe, in the UK between cities, communities... - identify stops and standstill areas of trucks, vans and all types of vehicles

All these statistics can feed various use cases: - marketing study for mobility advisory firms (Origins, Destinations, Stops / Standstill) that try to understand how the EV trucks are going to develop - help with marketing and geomarketing use cases to identify where to build / open a new branch or site - help explain and / or predict marketing performance of businesses across geographies

Use cases: ==> Transport analytics: Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage ==> Site-selection / marketing : Our data help companies looking to open EV charging stations, new shops and stores where the traffic is adapted to their business ==> Dynamic pricing / geomarketing : Our data help companies adjust prices across geographies

Summary

Geojson files used to visualize geospatial layers relevant to identifying and assessing trucking fleet decarbonization opportunities with the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE) tool.

Relevant Links

Link to the online version of the tool (requires creation of a free user account).

Link to GitHub repo with source code to produce this dataset and deploy the Geo-TIDE tool locally.

Funding

This dataset was produced with support from the MIT Climate & Sustainability Consortium.

Original Data Sources

These geojson files draw from and synthesize a number of different datasets and tools. The original data sources and tools are described below:

Filename(s) Description of Original Data Source(s) Link(s) to Download Original Data License and Attribution for Original Data Source(s)

faf5_freight_flows/*.geojson

trucking_energy_demand.geojson

highway_assignment_links_*.geojson

infrastructure_pooling_thought_experiment/*.geojson

Regional and highway-level freight flow data obtained from the Freight Analysis Framework Version 5. Shapefiles for FAF5 region boundaries and highway links are obtained from the National Transportation Atlas Database. Emissions attributes are evaluated by incorporating data from the 2002 Vehicle Inventory and Use Survey and the GREET lifecycle emissions tool maintained by Argonne National Lab.

Shapefile for FAF5 Regions

Shapefile for FAF5 Highway Network Links

FAF5 2022 Origin-Destination Freight Flow database

FAF5 2022 Highway Assignment Results

Attribution for Shapefiles: United States Department of Transportation Bureau of Transportation Statistics National Transportation Atlas Database (NTAD). Available at: https://geodata.bts.gov/search?collection=Dataset.

License for Shapefiles: This NTAD dataset is a work of the United States government as defined in 17 U.S.C. § 101 and as such are not protected by any U.S. copyrights. This work is available for unrestricted public use.

Attribution for Origin-Destination Freight Flow database: National Transportation Research Center in the Oak Ridge National Laboratory with funding from the Bureau of Transportation Statistics and the Federal Highway Administration. Freight Analysis Framework Version 5: Origin-Destination Data. Available from: https://faf.ornl.gov/faf5/Default.aspx. Obtained on Aug 5, 2024. In the public domain.

Attribution for the 2022 Vehicle Inventory and Use Survey Data: United States Department of Transportation Bureau of Transportation Statistics. Vehicle Inventory and Use Survey (VIUS) 2002 [supporting datasets]. 2024. https://doi.org/10.21949/1506070

Attribution for the GREET tool (original publication): Argonne National Laboratory Energy Systems Division Center for Transportation Research. GREET Life-cycle Model. 2014. Available from this link.

Attribution for the GREET tool (2022 updates): Wang, Michael, et al. Summary of Expansions and Updates in GREET® 2022. United States. https://doi.org/10.2172/1891644

grid_emission_intensity/*.geojson

Emission intensity data is obtained from the eGRID database maintained by the United States Environmental Protection Agency.

eGRID subregion boundaries are obtained as a shapefile from the eGRID Mapping Files database.

eGRID database

Shapefile with eGRID subregion boundaries

Attribution for eGRID data: United States Environmental Protection Agency: eGRID with 2022 data. Available from https://www.epa.gov/egrid/download-data. In the public domain.

Attribution for shapefile: United States Environmental Protection Agency: eGRID Mapping Files. Available from https://www.epa.gov/egrid/egrid-mapping-files. In the public domain.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Locations of direct current fast chargers and refueling stations for alternative fuels along U.S. highways. Obtained directly from the Station Data for Alternative Fuel Corridors in the Alternative Fuels Data Center maintained by the United States Department of Energy Office of Energy Efficiency and Renewable Energy.

US_elec.geojson

US_hy.geojson

US_lng.geojson

US_cng.geojson

US_lpg.geojson

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy. Alternative Fueling Station Corridors. 2024. Available from: https://afdc.energy.gov/corridors. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

daily_grid_emission_profiles/*.geojson

Hourly emission intensity data obtained from ElectricityMaps.

Original data can be downloaded as csv files from the ElectricityMaps United States of America database

Shapefile with region boundaries used by ElectricityMaps

License: Open Database License (ODbL). Details here: https://www.electricitymaps.com/data-portal

Attribution for csv files: Electricity Maps (2024). United States of America 2022-23 Hourly Carbon Intensity Data (Version January 17, 2024). Electricity Maps Data Portal. https://www.electricitymaps.com/data-portal.

Attribution for shapefile with region boundaries: ElectricityMaps contributors (2024). electricitymaps-contrib (Version v1.155.0) [Computer software]. https://github.com/electricitymaps/electricitymaps-contrib.

gen_cap_2022_state_merged.geojson

trucking_energy_demand.geojson

Grid electricity generation and net summer power capacity data is obtained from the state-level electricity database maintained by the United States Energy Information Administration.

U.S. state boundaries obtained from this United States Department of the Interior U.S. Geological Survey ScienceBase-Catalog.

Annual electricity generation by state

Net summer capacity by state

Shapefile with U.S. state boundaries

Attribution for electricity generation and capacity data: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data/state/. In the public domain.

electricity_rates_by_state_merged.geojson

Commercial electricity prices are obtained from the Electricity database maintained by the United States Energy Information Administration.

Electricity rate by state

Attribution: U.S. Energy Information Administration (Aug 2024). Available from: https://www.eia.gov/electricity/data.php. In the public domain.

demand_charges_merged.geojson

demand_charges_by_state.geojson

Maximum historical demand charges for each state and zip code are derived from a dataset compiled by the National Renewable Energy Laboratory in this this Data Catalog.

Historical demand charge dataset

The original dataset is compiled by the National Renewable Energy Laboratory (NREL), the U.S. Department of Energy (DOE), and the Alliance for Sustainable Energy, LLC ('Alliance').

Attribution: McLaren, Joyce, Pieter Gagnon, Daniel Zimny-Schmitt, Michael DeMinco, and Eric Wilson. 2017. 'Maximum demand charge rates for commercial and industrial electricity tariffs in the United States.' NREL Data Catalog. Golden, CO: National Renewable Energy Laboratory. Last updated: July 24, 2024. DOI: 10.7799/1392982.

eastcoast.geojson

midwest.geojson

la_i710.geojson

h2la.geojson

bayarea.geojson

saltlake.geojson

northeast.geojson

Highway corridors and regions targeted for heavy duty vehicle infrastructure projects are derived from a public announcement on February 15, 2023 by the United States Department of Energy.

The shapefile with Bay area boundaries is obtained from this Berkeley Library dataset.

The shapefile with Utah county boundaries is obtained from this dataset from the Utah Geospatial Resource Center.

Shapefile for Bay Area country boundaries

Shapefile for counties in Utah

Attribution for public announcement: United States Department of Energy. Biden-Harris Administration Announces Funding for Zero-Emission Medium- and Heavy-Duty Vehicle Corridors, Expansion of EV Charging in Underserved Communities (2023). Available from https://www.energy.gov/articles/biden-harris-administration-announces-funding-zero-emission-medium-and-heavy-duty-vehicle.

Attribution for Bay area boundaries: San Francisco (Calif.). Department Of Telecommunications and Information Services. Bay Area Counties. 2006. In the public domain.

Attribution for Utah boundaries: Utah Geospatial Resource Center & Lieutenant Governor's Office. Utah County Boundaries (2023). Available from https://gis.utah.gov/products/sgid/boundaries/county/.

License for Utah boundaries: Creative Commons 4.0 International License.

incentives_and_regulations/*.geojson

State-level incentives and regulations targeting heavy duty vehicles are collected from the State Laws and Incentives database maintained by the United States Department of Energy's Alternative Fuels Data Center.

Data was collected manually from the State Laws and Incentives database.

Attribution: U.S. Department of Energy, Energy Efficiency and Renewable Energy, Alternative Fuels Data Center. State Laws and Incentives. Accessed on Aug 5, 2024 from: https://afdc.energy.gov/laws/state. In the public domain.

These data and software code ("Data") are provided by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC ("Alliance"), for the U.S. Department of Energy ("DOE"), and may be used for any purpose whatsoever.

costs_and_emissions/*.geojson

diesel_price_by_state.geojson

trucking_energy_demand.geojson

Lifecycle costs and emissions of electric and diesel trucking are evaluated by adapting the model developed by Moreno Sader et al., and calibrated to the Run on Less dataset for the Tesla Semi collected from the 2023 PepsiCo Semi pilot by the North American Council for Freight Efficiency.

In

The Fleet dataset tracks Shared Service Canada’s vehicle inventory. It collects data like Make, Model, Year, Maintenance, Mileage and Purchase cost. *--No information on file since these units were initially purchased and maintained by RCMP

The dataset provides vehicle (both motor vehicle and trailer) registration numbers and annual fees in Iowa by year, county and vehicle types. Vehicle types include: Autocycle, Automobile, Bus, Moped, Motor Home - A, Motor Home - B, Motor Home - C, Motorcycle, Multi-purpose, Regular Trailer, Semi Trailer, Small Regular Trailer, Small Semi Trailer, Truck Tractor, Travel Trailer, Truck, Truck - Business Trade, and Truck - Weight and List.

The Autonomous Trucking Fleet Solutions market is poised for significant transformation, driven by advancements in technology and increasing demands for efficiency in logistics. This innovative sector is revolutionizing the transportation industry by introducing self-driving trucks equipped with cutting-edge sensors

Brazil Vehicles Fleet: Truck: North: Amazonas data was reported at 20,059.000 Unit in Feb 2019. This records an increase from the previous number of 20,020.000 Unit for Jan 2019. Brazil Vehicles Fleet: Truck: North: Amazonas data is updated monthly, averaging 16,492.000 Unit from Dec 2002 (Median) to Feb 2019, with 195 observations. The data reached an all-time high of 20,059.000 Unit in Feb 2019 and a record low of 2,205.000 Unit in Sep 2005. Brazil Vehicles Fleet: Truck: North: Amazonas data remains active status in CEIC and is reported by National Traffic Department. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAK021: Vehicle Fleet: by Region: Truck. Truck: Motor vehicle designed to transport cargo, with bodywork, and total gross weight exceeding 3,500 kg.

Comprehensive dataset of 140 Trucking companies in Finland as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The global truck driving recorder market is experiencing robust growth, driven by increasing demand for enhanced fleet management, driver safety, and compliance with stringent regulations. The market, estimated at $1.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This growth is fueled by several key factors: the rising adoption of telematics and connected vehicle technologies, a focus on reducing fuel consumption and improving operational efficiency, and the increasing need to mitigate risks associated with accidents and driver liability. The integrated type of driving recorders holds a larger market share compared to portable units due to their seamless integration with fleet management systems and superior data acquisition capabilities. The light truck segment currently dominates, but heavy truck adoption is rapidly increasing due to the heightened safety and liability concerns surrounding larger vehicles. Geographically, North America and Europe are currently leading the market, but the Asia-Pacific region is showing significant growth potential, driven by expanding economies and increasing fleet sizes in countries like China and India. The competitive landscape is characterized by a mix of established players and emerging companies. Key players like VDO, Garmin, and Blackvue are leveraging their technological expertise and brand recognition to maintain market leadership. However, several smaller, innovative companies are disrupting the market with advanced features such as AI-powered driver behavior analysis and improved video quality. Future market growth will be significantly influenced by advancements in video analytics, the integration of 5G connectivity, and the development of more sophisticated driver assistance systems. The increasing use of cloud-based platforms for data storage and analysis will also contribute to market expansion. Challenges include the high initial investment costs for some systems, concerns about data privacy and security, and the need for robust after-sales service and support.

TLC authorized For-Hire vehicles that are active or inactive. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields.

The global Truck and Bus Carnet market is experiencing robust growth, driven by increasing fleet sizes, stringent government regulations mandating vehicle tracking and data management, and the rising adoption of telematics and IoT solutions for improved fleet efficiency and safety. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% between 2025 and 2033, reaching approximately $45 billion by 2033. Key players such as Google, Baidu, Alibaba, Tencent, and established automotive players like Nokia and Apple are strategically investing in this market, leveraging their expertise in data analytics, cloud computing, and mapping technologies. The integration of advanced features such as driver behavior monitoring, predictive maintenance, and fuel optimization is fueling market expansion. While initial investment costs for implementing carnet systems can be a restraint, the long-term benefits of reduced operational expenses, improved safety records, and enhanced regulatory compliance outweigh the initial investment. The market is segmented based on vehicle type (trucks, buses), deployment (cloud, on-premise), and geography, with North America and Europe currently leading the adoption curve. The competitive landscape is characterized by a mix of technology giants and specialized telematics providers. The focus is shifting towards offering integrated solutions that combine carnet services with other fleet management tools. Growth is expected to be particularly strong in developing economies as transportation infrastructure expands and governments prioritize efficient logistics and transportation systems. Future growth will depend on factors such as technological advancements, government policies promoting digitalization in the transportation sector, and the continuous evolution of data privacy and security regulations. The increasing demand for real-time visibility and data-driven decision-making in the trucking and bus industry is a significant catalyst for continued market expansion throughout the forecast period.