The Massachusetts Vehicle Census (MVC) is the first state-level dataset in the nation that joins vehicle-level odometer readings with vehicle attribute and registration transaction histories. This powerful resource allows policymakers, researchers, and other stakeholders understand state and local trends in vehicle usage and ownership.

These datasets provide vehicle counts broken down by ZIP code, model year, fuel type, make and duty (light/heavy) of registered vehicles with specific as of dates.

This dataset contains the file of vehicle, snowmobile and boat registrations in NYS. Registrations expired more than 2 years are excluded. Records that have a scofflaw, revocation and/or suspension are included with indicators specifying those kinds of records.

Total vehicle registration counts per month by county

The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.

This research tests an implicit assumption on investment in plug-in electric vehicle (PEV) charging infrastructure: people who are not already interested in PEVs will see public PEV charging and become interested in PEVs. Data from a survey of car-owning households in California are combined with data on public PEV charging and PEV registrations to estimate a structural equation model of the extent to which participants have considered acquiring a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV), and whether participants report seeing PEV charging. The model controls for socio-economic and demographic measures and participants’ awareness, knowledge, and assessments of PEVs, and the known correlation between PEV registrations and public charging locations. Using logistic ordinal regression we also assess whether charger density near workplaces affects the results. All results contradict the assumption that people will see charging infrastructure and that more charging—rather than more people seeing charging—means more people will consider purchasing a BEV or PHEV. Rather, prior interest and positive assessments of PEVs, allow people to see PEV charging. In short, interest in PEVs is a prerequisite to people seeing PEV charging. Methods We use data from car-owning households (all cars, not just PEVs) from a survey completed in 2021 by nearly 3,000 respondents across California. These data are supplemented by data on the contemporaneous (with the household survey) counts of charging locations and PEV registrations in survey participants’ home zip codes. The analysis also controls for density of PEVs in the participants’ home zip codes as there is geographic correlation between PEVs and PEV charging infrastructure. Further, the analysis includes whether participants see PEV charging. The survey instrument also measured consumer awareness, knowledge, assessments, and consideration of PEVs. The sample was recruited by a professional survey firm. Participants were recruited from panels of people maintained by such firms for the express purpose of participating in research. Participants are typically screened based on criteria relevant to each study to which they are invited. Firms providing this service typically reward participation based on how many studies a person completes and the time and effort required to do so. Because all recruiting was done by the vendor and because these firms typically maintain cooperative relationships with each other allowing them to recruit from each other’s panels, the number of initial invitations to the pre-screening questionnaire for this study is unknown. Thus, a traditional response rate cannot be calculated. What is known is the number of people who screened into this study’s questionnaire and how many completed it. The completion rate was in the low-70 percentages. Participants were screened into this study via a pre-questionnaire establishing eligibility, determined primarily by respondent age (for reasons of informed consent and for quota sampling), respondent sex, household vehicle ownership (for basic eligibility), household income, and residential zip code. Quotas for age, sex, and income were set to match regional distributions within California for car-owning households. The analysis here is for the state.

We collect comprehensive data from multiple sources, including the Alabama Department of Revenue (ALDOR), American Community Survey (ACS), and POLK, all mapped with zipcode-level granularity to enable precise geospatial analysis. Our data organization strategy categorizes information by fuel types (focusing on BEVs compared to conventional vehicles), geographical units (zip codes as primary units), and different measurement types (including absolute counts, per capita values, and proportional measurements) to facilitate multi-dimensional analysis.

The evaluation is based on the reports submitted by the registration authorities to the Federal Motor Transport Authority (KBA) on new registrations, decommissioning and transfer of ownership of motor vehicles and their trailers in Germany. The data are registered in the Central Vehicle Register (ZFZR) in the Federal Motor Transport Authority (KBA) and evaluated here according to selected characteristics, such as vehicle classes, registration district and holder groups. In the case of a regional structure, the place of residence of the holder or the registered office, the branch or the service is decisive. The postal code (postcode) is not used as an evaluation feature, it only serves to supplement the place name. For municipalities with more than one postal code, only the lowest is indicated. Due to incorporations that have been implemented in the meantime, deviations from the currently valid postal code may arise in individual cases. Until 2006, the decommissionings were referred to as deletions. The reporting period is a calendar year.

This dataset contains the vehicle registration data for all vehicles in Spain registered from January 2015 to December 2024, sourced from the Dirección General de Tráfico (DGT). Original data is provided inside zips with `txt files in fixed-width format and has been converted to CSV for convenience.

Full documentation of the fields can be found at the official DGT PDF: MATRICULACIONES_MATRABA.pdf

Field Descriptions

Column	Description
FEC_MATRICULA	Registration date of the vehicle (YYYYMMDD).
COD_CLASE_MAT	Vehicle registration class code.
FEC_TRAMITACION	Date of processing the registration (YYYYMMDD).
MARCA_ITV	Vehicle brand as recorded by ITV.
MODELO_ITV	Vehicle model as recorded by ITV.
COD_PROCEDENCIA_ITV	Code indicating the origin of the vehicle.
BASTIDOR_ITV	Vehicle chassis number (VIN).
COD_TIPO	Vehicle type code.
COD_PROPULSION_ITV	Propulsion type.
CILINDRADA_ITV	Engine displacement in cc.
POTENCIA_ITV	Engine power in CVF (tax horsepower).
TARA	Vehicle empty weight (kg).
PESO_MAX	Maximum allowed weight (kg).
NUM_PLAZAS	Number of seats.
IND_PRECINTO	Indicator for sealed vehicle.
IND_EMBARGO	Indicator if the vehicle is under embargo.
NUM_TRANSMISIONES	Number of ownership transmissions.
NUM_TITULARES	Number of registered owners.
LOCALIDAD_VEHICULO	Vehicle location (city/town).
COD_PROVINCIA_VEH	Province code of the vehicle location.
COD_PROVINCIA_MAT	Province code where registration occurred.
CLAVE_TRAMITE	Procedure key for registration type.
FEC_TRAMITE	Date of registration procedure (YYYYMMDD).
CODIGO_POSTAL	Postal code of vehicle location.
FEC_PRIM_MATRICULACION	Date of first registration (YYYYMMDD).
IND_NUEVO_USADO	Indicator if vehicle is new or used.
PERSONA_FISICA_JURIDICA	Owner type: individual or legal entity.
CODIGO_ITV	ITV code.
SERVICIO	Service type.
COD_MUNICIPIO_INE_VEH	Municipality code (INE standard).
MUNICIPIO	Name of municipality.
KW_ITV	Vehicle power in kW.
NUM_PLAZAS_MAX	Maximum number of seats.
CO2_ITV	CO₂ emissions (g/km).
RENTING	Indicator for renting vehicles.
COD_TUTELA	Indicator for vehicles owned by minors or with judicial protection.
COD_POSESION	Code indicating type of ownership.
IND_BAJA_DEF	Indicator if vehicle is permanently deregistered.
IND_BAJA_TEMP	Indicator if vehicle is temporarily deregistered.
IND_SUSTRACCION	Indicator if vehicle is stolen.
BAJA_TELEMATICA	Telematic deregistration info.
TIPO_ITV	Type of vehicle.
VARIANTE_ITV	Variant code.
VERSION_ITV	Version code.
FABRICANTE_ITV	Vehicle manufacturer as recorded in ITV.
MASA_ORDEN_MARCHA_ITV	Vehicle curb weight (kg).
MASA_MAXIMA_TECNICA_ADMISIBLE_ITV	Maximum allowed technical weight (kg).
CATEGORIA_HOMOLOGACION_EUROPEA_ITV	European homologation category.
CARROCERIA	Car body type.
PLAZAS_PIE	Standing places (for buses).
NIVEL_EMISIONES_EURO_ITV	Euro emissions standard (Euro 5, 6, etc.).
CONSUMO_WH_KM_ITV	Energy consumption (Wh/km).
CLASIFICACION_REGLAMENTO_VEHICULOS_ITV	Regulation classification.
CATEGORIA_VEHICULO_ELECTRICO	Electric vehicle category.
AUTONOMIA_VEHICULO_ELECTRICO	Electric vehicle autonomy/range (km).
MARCA_VEHICULO_BASE	Base brand of vehicle.
FABRICANTE_VEHICULO_BASE	Base manufacturer of vehicle.
TIPO_VEHICULO_BASE	Base vehicle type.
VARIANTE_VEHICULO_BASE	Base vehicle variant.
VERSION_VEHICULO_BASE	Base vehicle version.
DISTANCIA_EJES_12_ITV	Distance between axles 1-2.
VIA_ANTERIOR_ITV	Front track distance.
VIA_POSTERIOR_ITV	Rear track distance.
TIPO_ALIMENTACION_ITV	Fuel type.
CONTRASENA_HOMOLOGACION_ITV	Homologation code.
ECO_INNOVACION_ITV	Eco innovation indicator.
REDUCCION_ECO_ITV	Eco reduction indicator.
CODIGO_ECO_ITV	Eco code.
FEC_PROCESO	Date of data processing (YYYYMMDD).

Washington State Electric Vehicle Demographic Data

Metadata Updated

November 17, 2023

Description

This comprehensive dataset provides detailed information on Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) currently registered through the Washington State Department of Licensing (DOL). It offers a thorough examination of electric vehicle ownership patterns and trends, including vehicle registration, make, model, electric vehicle type, clean alternative fuel vehicle (CAFV) eligibility, electric range, base MSRP, legislative district, DOL vehicle ID, vehicle location, electric utility, and 2020 Census tract.

Key Features

• Provides a comprehensive overview of electric vehicle adoption in Washington State, including registration trends, vehicle distribution by type, and ownership patterns across various regions.
• Offers insights into the characteristics of electric vehicle owners, such as the types of vehicles they choose, their location, and their purchasing preferences.
• Enables analysis of the impact of electric vehicle incentives and policies on vehicle registration and usage.
• Serves as a valuable resource for researchers, policymakers, and industry stakeholders interested in understanding and promoting electric vehicle adoption.

Data Source

Washington State Department of Licensing (DOL)

Data Format

CSV

Columns Description

• VIN (1-10): The first ten characters of the Vehicle Identification Number.
• County: The county where the vehicle is registered.
• City: The city where the vehicle is located.
• State: The state where the vehicle is registered.
• Postal Code: The postal code of the vehicle's location.
• Model Year: The year of the vehicle model.
• Make: The make of the vehicle.
• Model: The model of the vehicle.
• Electric Vehicle Type: Type of electric vehicle (e.g., PHEV, BEV).
• CAFV Eligibility: Clean Alternative Fuel Vehicle eligibility status.
• Electric Range: The electric range of the vehicle.
• Base MSRP: The Manufacturer's Suggested Retail Price.
• Legislative District: Legislative district associated with the vehicle.
• DOL Vehicle ID: Department of Licensing Vehicle ID.
• Vehicle Location: Geographic location of the vehicle.
• Electric Utility: The electric utility associated with the vehicle.
• 2020 Census Tract: Census tract information for the year 2020.

Potential Applications

• Analyze the growth and adoption of electric vehicles in Washington State.
• Identify trends in electric vehicle ownership by geographic location, vehicle type, and other factors.
• Assess the impact of electric vehicle policies on vehicle registration and usage.
• Support research on electric vehicle technology, infrastructure, and consumer behavior.

Additional Resources

• Washington State Electric Vehicle Population Data: https://data.wa.gov/Transportation/Electric-Vehicle-Population-Data/f6w7-q2d2

The data includes the car ownership data of Helsinki Metropolitan Area for the years 2000, 2002, 2005, 2007, 2010, 2012, 2013, 2015, 2017 and 2020. Years 2000-2017 include the data from Helsinki, Espoo and Kauniainen and the year 2020 includes the data from Helsinki, Espoo and Vantaa.

The produced dataset is based on the Finnish Environment Institute’s YKR data. The regional divisions of the calculated data vary depending on the city: in Helsinki, the division is based on districts and in Vantaa, Espoo and Kauniainen on the division of statistical areas. The dataset has been composed as follows: 1. Each municipality's squares (= centres of the squares) have been separated from the data for different years according to the municipality code. 2. Information about the area division has been attached to the centres of the squares based on location (partly manually on the edges of the areas). 3. Column data has been summed up based on the area division. 4. Tables have been cleaned up by deleting redundant data and fading out some data, such as “less than 10” (in Finnish "alle 10") cases, for privacy reasons.

The dataset includes the following attributes

• ak_yht Total number of households
• ak_1 Households, number of persons 1
• ak_2 Households, number of persons 2
• ak_3 Households, number of persons 3
• ak_4 Households, number of persons 4
• ak_5 Households, number of persons 5
• ak_6 Households, number of persons 6
• ak_7 Households, number of persons 7+
• henk_yht Total persons
• ak_18v Households with children under 18 years old
• autoja_1 Number of households with 1 car
• autoja_2 Number of households with 2+ cars
• also unique identifiers, place names, etc.

Things to note about using the dataset: For privacy reasons, information is not available on all squares of the YKR dataset used as source data. The produced dataset is therefore not fully accurate.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Workers include members of the Armed Forces and civilians who were at work last week..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

This data set used in the CoIL 2000 Challenge contains information on customers of an insurance company. The data consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was collected to answer the following question: Can you predict who would be interested in buying a caravan insurance policy and give an explanation why?

Acknowledgements

DISCLAIMER

This dataset is owned and supplied by the Dutch datamining company Sentient Machine Research, and is based on real world business data. You are allowed to use this dataset and accompanying information for non commercial research and education purposes only. It is explicitly not allowed to use this dataset for commercial education or demonstration purposes. For any other use, please contact Peter van der Putten, info@smr.nl.

This dataset has been used in the CoIL Challenge 2000 datamining competition. For papers describing results on this dataset, see the TIC 2000 homepage: http://www.wi.leidenuniv.nl/~putten/library/cc2000/

Please cite/acknowledge:

P. van der Putten and M. van Someren (eds) . CoIL Challenge 2000: The Insurance Company Case. Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000.

The Data

Originally, this dataset was broken into two parts: the training set and the evaluation set. As this was a competition, the responses to the evaluation set were not given as part of the original release; they were, however, released after the end of the competition in a separate file. This dataset contains all three of these files, combined into one.

The field ORIGIN in the caravan-insurance-challenge.csv file has the values train and test, corresponding to the training and evaluation sets, respectively. To simulate the original challenge, you can ignore the test rows, and test your model's prediction on those observations once you've trained only on the training set.

Each observation corresponds to a postal code. Variables beginning with M refer to demographic statistics of the postal code, while variables beginning with P and A (as well as CARAVAN, the target variable) refer to product ownership and insurance statistics in the postal code.

The data file contains the following fields:

• ORIGIN: train or test, as described above
• MOSTYPE: Customer Subtype; see L0
• MAANTHUI: Number of houses 1 - 10
• MGEMOMV: Avg size household 1 - 6
• MGEMLEEF: Avg age; see L1
• MOSHOOFD: Customer main type; see L2

** Percentages in each group, per postal code (see L3)**:

• MGODRK: Roman catholic
• MGODPR: Protestant ...
• MGODOV: Other religion
• MGODGE: No religion
• MRELGE: Married
• MRELSA: Living together
• MRELOV: Other relation
• MFALLEEN: Singles
• MFGEKIND: Household without children
• MFWEKIND: Household with children
• MOPLHOOG: High level education
• MOPLMIDD: Medium level education
• MOPLLAAG: Lower level education
• MBERHOOG: High status
• MBERZELF: Entrepreneur
• MBERBOER: Farmer
• MBERMIDD: Middle management
• MBERARBG: Skilled labourers
• MBERARBO: Unskilled labourers
• MSKA: Social class A
• MSKB1: Social class B1
• MSKB2: Social class B2
• MSKC: Social class C
• MSKD: Social class D
• MHHUUR: Rented house
• MHKOOP: Home owners
• MAUT1: 1 car
• MAUT2: 2 cars
• MAUT0: No car
• MZFONDS: National Health Service
• MZPART: Private health insurance
• MINKM30: Income < 30.000
• MINK3045: Income 30-45.000
• MINK4575: Income 45-75.000
• MINK7512: Income 75-122.000
• MINK123M: Income >123.000
• MINKGEM: Average income
• MKOOPKLA: Purchasing power class

** Total number of variable in postal code (see L4)**:

• PWAPART: Contribution private third party insurance
• PWABEDR: Contribution third party insurance (firms) ...
• PWALAND: Contribution third party insurane (agriculture)
• PPERSAUT: Contribution car policies
• PBESAUT: Contribution delivery van policies
• PMOTSCO: Contribution motorcycle/scooter policies
• PVRAAUT: Contribution lorry policies
• PAANHANG: Contribution trailer policies
• PTRACTOR: Contribution tractor policies
• PWERKT: Contribution agricultural machines policies
• PBROM: Contribution moped policies
• PLEVEN: Contribution life insurances
• PPERSONG: Contribution private accident insurance policies
• PGEZONG: Contribution family accidents insurance policies
• PWAOREG: Contribution disability insurance policies
• PBRAND: Contribution fire policies
• PZEILPL: Contribution surfboard policies
• PPLEZIER: Contribution boat policies
• PFIETS: Contribution bicycle policies
• PINBOED: Contribution property in...

Dataset Description: This dataset contains information about electric vehicles (EVs) registered in various countries and cities. It includes data on vehicle identification numbers (VINs), countries, cities, states, postal codes, model years, makes, models, electric vehicle types, Clean Alternative Fuel Vehicle (CAFV) eligibility, electric ranges, base MSRPs, legislative districts, DOL vehicle IDs, vehicle locations, and electric utilities.

Dataset Attributes:

• VIN (1-10): The first ten digits of the vehicle identification number (VIN).
• Country: The country where the vehicle is registered.
• City: The city where the vehicle is registered.
• State: The state where the vehicle is registered.
• Postal code: The postal code of the vehicle's registration address.
• Model year: The year that the vehicle was manufactured.
• Make: The manufacturer of the vehicle.
• Model: The specific model of the vehicle.
• Electric vehicle type: The type of electric vehicle, such as battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), or hybrid electric vehicle (HEV).
• Clean Alternative Fuel Vehicle (CAFV) Eligibility: Whether or not the vehicle is eligible for CAFV benefits in the state where it is registered.
• Electric Range: The estimated electric range of the vehicle in miles.
• **Base MSRP: **The manufacturer's suggested retail price of the vehicle before any options or incentives are applied.
• **Legislative District: **The legislative district in which the vehicle is registered.
• DOL Vehicle ID: The Washington Department of Licensing vehicle identification number.
• Vehicle Location: The physical location of the vehicle.
• Electric Utility: The electric utility that provides power to the vehicle's owner.

Dataset Potential Uses:

• Analyzing the electric vehicle population in different countries and cities.
• Identifying trends in electric vehicle adoption.
• Understanding the demographics of electric vehicle owners.
• Evaluating the effectiveness of electric vehicle policies.
• Developing marketing strategies for electric vehicles.