US Motor Vehicle Registrations

Historical US Vehicle Registrations

By Throwback Thursday [source]

About this dataset

Each entry in this dataset includes various attributes that contribute to its richness. Key variables include state-level data, which allows for analysis on a regional basis, as well as more granular details such as vehicle type (e.g., passenger cars, trucks) and weight class (e.g., light-duty vehicles). Moreover, additional information on annual changes in registrations is provided, enabling users to observe fluctuations within specific years or compare registration numbers across different time periods.

The value of this dataset lies not only in its extensive coverage but also in its potential for conducting research across different fields such as transportation studies, urban planning, environmental impact analysis, and automotive industry analysis. The inclusion of historical data enables researchers to explore long-term trends that may have influenced societal behavior or policy decisions related to transportation infrastructure.

How to use the dataset

• Understand the Data:

  • The dataset provides a comprehensive record of motor vehicle registrations in the United States from 1900 to 1995.

  • The columns in the dataset include:

    a. Vehicle Type: Represents different types of vehicles (e.g., cars, motorcycles, trucks).

    b. Registration Count: Indicates the number of registered vehicles for each vehicle type and year.

• Analyze Vehicle Type Distribution:

  • To understand the distribution of registered vehicles by type over time, group the data by Vehicle Type and analyze registration counts.

• Identify Trends and Patterns:

  • By analyzing trends in registration counts over time, you can gain insights into changes in vehicle ownership patterns or preferences throughout history.

• Compare Different Vehicle Types:

  • Compare registration counts between different vehicle types to determine which types are more popular during various periods.

• Visualize Data:

  • Use various visualization techniques like line charts, bar graphs, or stacked area plots to represent registration counts with respect to time or compare different vehicle types side by side.

• Explore Historical Events:

  • Analyze how historical events (e.g., economic recessions, oil crises) affected motor vehicle registrations at specific points in time.

• Study Specific Time Periods:

  a. Early 20th Century:

  i) Investigate registrations from 1900-1920: Understand early trends and adoption rates of motor vehicles after their introduction

  ii) Explore changes during World War I: Analyze how war impacts influenced registrations

  b) Post-World War II Boom:

  i) Focus on growth patterns during post-WWII years (1945-1960): Identify if there was an acceleration in car registrations after wartime restrictions were lifted

• Conduct Further Research:

  • Supplement this dataset with additional sources to gain comprehensive insights into motor vehicle registrations in the U.S.

• Share Visualizations and Insights:

  • Compile interesting visualizations or insights gained from this dataset to inform others about motor vehicle registration history in the United States

Research Ideas

• Analyzing the growth and trends of motor vehicle registrations over time: This dataset allows for a detailed analysis of how motor vehicle registrations have evolved and expanded in the United States from 1900 to 1995. It can be used to identify patterns, changes in adoption rates, and shifts in popularity between different types of vehicles.
• Studying the impact of historical events on motor vehicle registrations: With this dataset, it is possible to explore the impact that major historical events and periods had on motor vehicle registrations. For example, one could analyze how registrations were affected by World War II or economic recessions during this time period.
• Comparing registration rates between different states and regions: This dataset provides information at a national level as well as broken down by state or region. It can be used to compare registration rates between different states or regions within specific years or over an extended time frame. This can provide insights into socioeconomic factors, population changes, and varying transportation needs across different areas of the country

Acknowledgements

If you use this dataset in your research, please credit the or...

This geospatial dataset comprises N=19,713 real-world truck parking locations across Europe (EU-27, EFTA, and the UK). Data origintated from various sources including OpenStreetMap and commercial truck routing / geocoding software to identify publicly accessible and truck-certified parking locations. Using geospatial clustering helped to condense the dataset and reduce redundancies. Refining and enhancing the dataset involved supplementary datasets and several filters to obtain the final subset. Accordingly, GPS coordinates may not match exact locations but should be considered as reference point for detailed local analyses of ambient conditions and truck accessibility. Coverage and completeness varies among countries. Fraunhofer ISI does not assume any liability for completeness, correctness and accuracy of the information.

This dataset plays a pivotal role in identifying viable real-world locations for future alternative infrastructure sites for heavy-duty trucks, thereby acting as a crucial resource in promoting low-carbon road freight transport facilitated by electrified truck fleets. Infrastructure sites may comprise charging infrastructure for battery-electric trucks and hydrogen refuelling stations (HRS) for fuel-cell electric or hydrogen combustion trucks. Consequently, it can serve as a valuable asset for research in traffic science, future energy systems, and alternative truck powertrains. Its value extends to assisting industry stakeholders such as Charge Point Operators (CPOs), truck manufacturers, and grid network operators but also public authorities in aligning their efforts towards the deployment of alternative infrastructure.

Details and references are provided in the .pdf document. More information is available upon request.

Jordan Number of Vehicles: Large Truck: Public data was reported at 16,746.000 Unit in 2017. This records an increase from the previous number of 16,331.000 Unit for 2016. Jordan Number of Vehicles: Large Truck: Public data is updated yearly, averaging 9,719.000 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 16,746.000 Unit in 2017 and a record low of 6,878.000 Unit in 2000. Jordan Number of Vehicles: Large Truck: Public data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under Global Database’s Jordan – Table JO.TA001: Number of Vehicles.

811491 Global import shipment records of Truck with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Context

Automobile data holds immense importance as it offers insights into the functioning and efficiency of the automotive industry. It provides valuable information about car models, specifications, sales trends, consumer demographics, and preferences, which car manufacturers and dealerships can leverage to optimize their operations and enhance customer experiences. By analyzing data on vehicle reliability, fuel efficiency, safety ratings, and resale values, the automotive industry can identify trends and implement strategies to produce more reliable and environmentally friendly vehicles, improve safety standards, and enhance the overall value of cars for consumers. Moreover, regulatory bodies and policymakers rely on this data to enforce regulations, set emissions standards, and make informed decisions regarding automotive policies and environmental impacts. Researchers and analysts use car purchase data to study market trends, assess the environmental impact of various vehicle types, and develop strategies for sustainable growth within the industry. In essence, car purchase data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the automotive sector.

Content

This dataset comprises diverse parameters relating to car purchases and ownership on a global scale. The dataset prominently incorporates fields such as 'First Name', 'Last Name', 'Country', 'Car Brand', 'Car Model', 'Car Color', 'Year of Manufacture', and 'Credit Card Type'. These columns collectively provide comprehensive insights into customer demographics, vehicle details, and payment information. Researchers and industry experts can leverage this dataset to analyze trends in car purchasing behavior, optimize the customer car-buying experience, evaluate the popularity of car brands and models, and understand payment preferences within the automotive industry.

Dataset Glossary (Column-wise)

• First Name - The first name of the car purchaser.
• Last Name - The last name of the car purchaser.
• Country - The country of residence of the car purchaser.
• Car Brand - The brand or manufacturer of the purchased car.
• Car Model - The specific model of the purchased car.
• Car Color - The color of the purchased car.
• Year of Manufacture - The year the car was manufactured.
• Credit Card Type - The type of credit card used for the car purchase.

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable mock datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Freepik

Thumbnail by: Car icons created by Freepik - Flaticon

The Comprehensive Vehicle Make and Model Dataset provides a detailed list of automotive manufacturers and their corresponding models. This dataset includes data on various car makes (manufacturers) and models (specific car names under each make), making it ideal for use in automotive research, machine learning projects, or data enrichment tasks related to the automotive industry.

Dataset Features: Make: The name of the car manufacturer (e.g., Toyota, Ford, BMW). Model: The specific car model associated with each manufacturer (e.g., Camry, F-150, X5).

This dataset is structured to be easily accessible for relational databases, making it suitable for building relational models where car makes are linked to their models. It is especially useful for tasks like recommendation systems, market analysis, trend analysis, or training machine learning models that require automotive industry data.

Use Cases: Recommendation Engines: Develop systems that recommend car models based on user preferences. Market Research: Analyze the popularity or trends in specific car makes and models. Data Enrichment: Enrich datasets with car make and model information for enhanced data quality.

Data Structure: Each entry in the dataset consists of: Make: Manufacturer name. Models: List of car models associated with that make.

Key information about US Number of Registered Vehicles

• US Number of Registered Vehicles was reported at 284,614,269 Unit in Dec 2023.
• This records an increase from the previous number of 283,400,986 Unit for Dec 2022.
• US Number of Registered Vehicles data is updated yearly, averaging 93,949,852 Unit from Dec 1910 to 2023, with 114 observations.
• The data reached an all-time high of 284,614,269 Unit in 2023 and a record low of 468,500 Unit in 1910.
• US Number of Registered Vehicles data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: No of Registered Vehicles: Annual.

Introduction

Electric vehicles (EVs) have seen a remarkable evolution from their early innovations to their current status as a pivotal element in the transportation industry. This document explores the rich history of electric vehicles, focusing on their development through various periods, and provides an overview of the different types of EVs available today. Through data visualizations and analysis, we highlight global EV trends, the growth of EV sales, and the distribution of various powertrain types across regions.

The electric vehicle landscape has evolved significantly, influenced by technological advancements, environmental concerns, and shifting market dynamics. The modern resurgence of EVs reflects a growing recognition of their potential to reshape the transportation industry and drive towards a more sustainable future.

History of Electric Vehicles

The history of electric vehicles is marked by a series of innovations, declines, and revivals, spanning over a century. This section delves into the early history, the impact of the oil crises, and notable electric vehicles like the Sinclair C5.

Early History of Electric Vehicles

Late 19th Century - Early 20th Century

Origins:
Electric vehicles have their roots in the early 19th century. The first practical electric vehicle was built by Scottish inventor Robert Anderson between 1832 and 1839. This early electric carriage was powered by non-rechargeable batteries and laid the groundwork for future developments.

Early 20th Century Market Share:
By the early 1900s, electric vehicles, petrol-powered cars, and steam cars each held significant shares of the market. At this time, electric vehicles were favored for their quiet operation and ease of use compared to the noisy, cumbersome petrol cars.

In 1900, electric vehicles held about one-third of the automotive market. They were popular among urban drivers due to their reliability and the convenience of not requiring manual hand-cranking, as was needed for petrol vehicles.

Decline:
The decline of electric vehicles began with the rise of petrol-powered cars, facilitated by innovations such as the electric starter and mass production techniques introduced by Henry Ford. By the 1920s, the market for electric vehicles had diminished significantly as internal combustion engines became more widespread and infrastructure for petrol vehicles expanded.

The 1970s Oil Crisis and the Revival of Interest in EVs

Oil Crisis

The oil crises of the 1970s, including the 1973 Arab Oil Embargo and the 1979 energy crisis, renewed interest in alternative energy sources like electric vehicles. The sharp increase in oil prices and concerns about energy security highlighted the need for less oil-dependent transportation solutions.

During this period, there was a resurgence in the development of electric vehicles as a means to reduce reliance on fossil fuels and mitigate the impact of future oil shortages.

Early 1970s Efforts

Various automotive manufacturers and research institutions explored electric vehicles during this time. Despite the enthusiasm, many early attempts were constrained by the technology of the era, including limitations in battery performance and range.

Notable Vehicles and Innovations

Sinclair C5 (1985)

Overview:
The Sinclair C5, designed by Sir Clive Sinclair, was an electric vehicle launched in 1985. It was a small, three-wheeled vehicle intended for short trips and urban commuting. The C5 had a top speed of about 15 miles per hour and a range of 20-30 miles on a single charge.

Reception:
Despite its innovative concept, the Sinclair C5 faced criticism for its limited speed, range, and lack of weather protection. It was also deemed unsafe by some due to its low profile and exposure to road hazards. The vehicle was not commercially successful and was discontinued after a brief production period. Nonetheless, it remains an important historical reference in the development of electric vehicles.

Other Notable Early EVs

General Motors EV1 (1996-1999):
The GM EV1 was one of the first mass-produced electric cars of the modern era, introduced in the late 1990s. It was notable for its advanced technology and was designed specifically as an electric vehicle.

The EV1 was praised for its performance and efficiency but faced limitations due to high costs and lack of support infrastructure. GM eventually decided to discontinue the EV1 and retrieve most of the vehicles from customers.

Modern Resurgence

21st Century

The early 2000s marked a resurgence in electric vehicles, driven by advances in battery technology, increasing environmental concerns, and government incentives. Tesla Motors, founded in 2003, played a significant role in popularizing electric vehicles with models like the Tesla Roadster and Model S. Othe...

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

To date, GPS tracking data for minibus taxis has only been captured at a sampling frequency of once per minute. This is the first GPS tracking data captured on a per-second (1 Hz) basis. Minibus taxi paratransit vehicles in South Africa are notorious for their aggressive driving behaviour characterised by rapid acceleration/deceleration events, which can have a large effect on vehicle energy consumption. Infrequent sampling cannot capture these micro-mobility patterns, thus missing out on their effect on vehicle energy consumption (kWh/km). We hypothesised that to construct high fidelity estimates of vehicle energy consumption, higher resolution data that captures several samples per movement would be needed. Estimating the energy consumption of an electric equivalent (EV) to an internal combustion engine (ICE) vehicle is requisite for stakeholders to plan an effective transition to an EV fleet. Energy consumption was calculated following the kinetic model outline in "The bumpy ride to electrification: High fidelity energy consumption estimates for minibus taxi paratransit vehicles in South Africa".

Six tracking devices were used to record GPS data to an SD card at a frequency of 1Hz. The six recording devices are based on the Arduino platform and powered from alkaline battery packs. The device can therefore operate independently of any other device during tests. The acquired data is separately processed after the completion of data recording. Data captured is initiated with the press of a button, and terminated once the vehicle reached the destination. Each recorded trip creates an isolated file. This allows for different routes to be separately investigated and compared to other recordings made on the same route.

There are 62 raw trip files, all found in the attached 'raw data' folder under the corresponding route and time of day in which they were captured. The raw data includes date, time, velocity, elevation, latitude, longitude, heading, number of satellites connected, and signal quality. Data was recorded on three routes, in both directions, for a total of six distinct routes. Each route had trips recorded in the morning (before 11:30AM) , afternoon (11:30AM-4PM) and evening (after 4PM).

The processed data is available in the 'Processed Data' folder. In addition to the raw data, these processed data files include the displacement between observations, calculated using Geopy's geodesic package, and the estimated energy provided by the vehicle's battery for propulsion, braking, and offload work. The python code for the kinetic model can be found in the attached GitHub link https://github.com/ChullEPG/Bumpy-Ride.

Future research can use this data to develop standard driving cycles for paratransit vehicles, and to improve the validity of micro-traffic simulators that are used to simulate per-second paratransit vehicle drive cycles between minutely waypoints.

Korea Registered Motor Vehicles: Truck data was reported at 3,581,228.000 Unit in Oct 2018. This records an increase from the previous number of 3,578,467.000 Unit for Sep 2018. Korea Registered Motor Vehicles: Truck data is updated monthly, averaging 2,971,173.000 Unit from Jan 1988 (Median) to Oct 2018, with 370 observations. The data reached an all-time high of 3,581,228.000 Unit in Oct 2018 and a record low of 552,327.000 Unit in Jan 1988. Korea Registered Motor Vehicles: Truck data remains active status in CEIC and is reported by Ministry of Land, Infrastructure and Transport. The data is categorized under Global Database’s South Korea – Table KR.TA001: Number of Registered Motor Vehicles.

Jordan Number of Vehicles: Truck and Trailer: Private data was reported at 526.000 Unit in 2017. This records a decrease from the previous number of 564.000 Unit for 2016. Jordan Number of Vehicles: Truck and Trailer: Private data is updated yearly, averaging 570.000 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 696.000 Unit in 2007 and a record low of 170.000 Unit in 1999. Jordan Number of Vehicles: Truck and Trailer: Private data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under Global Database’s Jordan – Table JO.TA001: Number of Vehicles.

Tunisia Number of Registered Vehicles: New: Trucks and Small Trucks data was reported at 1,631.000 Unit in Dec 2017. This records a decrease from the previous number of 1,993.000 Unit for Nov 2017. Tunisia Number of Registered Vehicles: New: Trucks and Small Trucks data is updated monthly, averaging 1,266.000 Unit from Jul 1995 (Median) to Dec 2017, with 269 observations. The data reached an all-time high of 2,481.000 Unit in May 2016 and a record low of 722.000 Unit in Jul 1996. Tunisia Number of Registered Vehicles: New: Trucks and Small Trucks data remains active status in CEIC and is reported by Land Transport Technical Agency. The data is categorized under Global Database’s Tunisia – Table TN.TA001: Number of Registered Vehicles and Driving Licenses.

China Number of Motor Vehicle: Truck: Heavy data was reported at 8,819.443 Unit th in 2023. This records a decrease from the previous number of 8,941.540 Unit th for 2022. China Number of Motor Vehicle: Truck: Heavy data is updated yearly, averaging 4,872.415 Unit th from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 9,070.873 Unit th in 2021 and a record low of 1,367.943 Unit th in 2003. China Number of Motor Vehicle: Truck: Heavy data remains active status in CEIC and is reported by Ministry of Transport. The data is categorized under China Premium Database’s Automobile Sector – Table CN.RAG: No of Motor Vehicle.

Bulgaria Number of Vehicles: Lorries data was reported at 422,213.000 Unit in 2022. This records an increase from the previous number of 410,197.000 Unit for 2021. Bulgaria Number of Vehicles: Lorries data is updated yearly, averaging 293,392.500 Unit from Dec 1993 (Median) to 2022, with 30 observations. The data reached an all-time high of 422,213.000 Unit in 2022 and a record low of 185,824.000 Unit in 1993. Bulgaria Number of Vehicles: Lorries data remains active status in CEIC and is reported by National Statistical Institute. The data is categorized under Global Database’s Bulgaria – Table BG.TA005: Number of Vehicles.

Kenya Road Transport: Number of Motor Vehicles: Registered: Lorries, Trucks and Heavy Vans data was reported at 159,128.000 Unit in 2017. This records an increase from the previous number of 151,668.000 Unit for 2016. Kenya Road Transport: Number of Motor Vehicles: Registered: Lorries, Trucks and Heavy Vans data is updated yearly, averaging 98,267.500 Unit from Dec 2004 (Median) to 2017, with 14 observations. The data reached an all-time high of 159,128.000 Unit in 2017 and a record low of 63,999.000 Unit in 2004. Kenya Road Transport: Number of Motor Vehicles: Registered: Lorries, Trucks and Heavy Vans data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.TA001: Road Transport: Number of Motor Vehicles: Registered.

United States Motor Vehicle Sales: Quantity: sa: Heavy Trucks data was reported at 39.594 Unit th in Jun 2018. This records an increase from the previous number of 37.713 Unit th for May 2018. United States Motor Vehicle Sales: Quantity: sa: Heavy Trucks data is updated monthly, averaging 28.600 Unit th from Jan 1967 (Median) to Jun 2018, with 618 observations. The data reached an all-time high of 46.700 Unit th in Mar 1973 and a record low of 13.200 Unit th in Oct 1982. United States Motor Vehicle Sales: Quantity: sa: Heavy Trucks data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.H017: Motor Vehicles Sale: Bureau of Economic Analysis.

Serbia Number of Registered Vehicles: Trucks: Physical Persons data was reported at 111,594.000 Unit in 2016. This records an increase from the previous number of 107,431.000 Unit for 2015. Serbia Number of Registered Vehicles: Trucks: Physical Persons data is updated yearly, averaging 75,169.500 Unit from Dec 2001 (Median) to 2016, with 16 observations. The data reached an all-time high of 111,594.000 Unit in 2016 and a record low of 60,747.000 Unit in 2002. Serbia Number of Registered Vehicles: Trucks: Physical Persons data remains active status in CEIC and is reported by Statistical Office of the Republic of Serbia. The data is categorized under Global Database’s Serbia – Table RS.T004: Number of Registered Vehicles.

United States Motor Vehicle Sales: Quantity: sa: Light Trucks: Foreign data was reported at 235.934 Unit th in Jun 2018. This records an increase from the previous number of 231.482 Unit th for May 2018. United States Motor Vehicle Sales: Quantity: sa: Light Trucks: Foreign data is updated monthly, averaging 65.050 Unit th from Jan 1976 (Median) to Jun 2018, with 510 observations. The data reached an all-time high of 235.934 Unit th in Jun 2018 and a record low of 15.000 Unit th in Mar 1976. United States Motor Vehicle Sales: Quantity: sa: Light Trucks: Foreign data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.H017: Motor Vehicles Sale: Bureau of Economic Analysis.

Brazil Motor Vehicle: New Registration: Trucks data was reported at 9,372.000 Unit in Mar 2025. This records an increase from the previous number of 8,967.000 Unit for Feb 2025. Brazil Motor Vehicle: New Registration: Trucks data is updated monthly, averaging 4,890.000 Unit from Jan 1957 (Median) to Mar 2025, with 819 observations. The data reached an all-time high of 17,478.000 Unit in Dec 2010 and a record low of 1,037.000 Unit in Jan 1957. Brazil Motor Vehicle: New Registration: Trucks data remains active status in CEIC and is reported by National Association of Automobile Manufacturers. The data is categorized under Global Database’s Brazil – Table BR.RAF001: Automobiles Newly Registered: National Association of Automobile Manufacturers - Anfavea. Light Commercials: Gross Vehicle Weight (GVW) up to 3.5 ton. Semi-light: GVW > 3.5 ton. < 6 ton. Light: GVW ≥ 6 ton. < 10 ton. Medium: GVW ≥ 10 ton. < 15 ton. Semi-heavy: GVW ≥ 15 ton. Truck: GCW (Gross Capacity Weight) ≤ 45 ton. Truck-tractor: PBTC (Gross train weight) < 40 ton. Heavy: GVW ≥ 15 ton. Truck: GCW (Gross Capacity Weight) > 45 ton. Truck-tractor: PBTC (Gross train weight) ≥ 40 ton.