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...

In 2023, California had the most automobile registrations: almost 13.2 million such vehicles were registered in the most populous U.S. federal state. California also had the highest number of registered motor vehicles overall: nearly 30.4 million registrations.

This dataset provides comprehensive information about used cars available for sale in the United States. It includes detailed data on various aspects of each vehicle, making it a valuable resource for car buyers, sellers, and data enthusiasts. The dataset contains the following key attributes:

• Brand: The brand or manufacturer of the car.
• Model: The specific model of the car.
• Mileage: The number of miles the car has been driven.
• Year: The manufacturing year of the car.
• Status: Indicates whether the car is new, used, or certified pre-owned.
• Dealer: Information about the dealer or seller offering the car.
• Price: The listed price of the car in USD.

This dataset is ideal for data analysis, machine learning projects, and market research related to the used car industry in the United States. Whether you are interested in predicting car prices, understanding market trends, or simply searching for your next vehicle, this dataset provides a wealth of information to explore.

Data Source: More info on my GitHub repository

Data Format: CSV

Car Theft State-by-State (With Makes and Models)

The Most Stolen Makes and Models by Number of Thefts

By Joe Boutros [source]

About this dataset

This dataset aims to explore the car theft climate in the US. It contains information on the top ten most stolen cars (make, model and year) by State, as well as the top 25 stolen model year cars and their corresponding number of thefts. This data was gathered by The National Insurance Crime Bureau which reports this data annually to provide an insight into car theft prevention strategies. Insight included is not only which vehicle might be at higher risk of being stolen in a given state but also what kind of models/makes can be found among many states or nationwide with highest frequency of being stolen. Analyzing this dataset could help answer questions like “What are the most frequently targeted makes/models?” or “Which states have seen an increase or decrease in car thefts?” - perhaps providing invaluable insight for consumers on how to best protect their vehicles from potential theft

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How to use the dataset

This dataset contains information about the car thefts that occured in the United States, organized by state and make/model of the vehicle stolen. You can use this dataset to uncover trends of car theft and investigate how different states differ when it comes to car theft patterns. Below are a few ways you can use this data:

• Explore Car Thefts by State: Use the columns State and # of Thefts to compare total thefts across all states, or specific states if you wish.

• Compare Stolen Vs Non-Stolen Cars by Make / Model: Use the columns Make/Model and Thefts to look at which make/model cars were most frequently stolen in 2015, as well as compare them with models that were not stolen at all.

• Uncover Hot Wheels for Each State: Look at columns Rank and Model Year for each state to determine which specific hot wheels vehicles experienced the most thefts during that year from each state.

  • Dive Deeper into Model Years : Utilize columns Veh model Yr and Thefts_Year to explore correlations between vehicle model years & total number of thefts per year for particular models or overall data set trends (e..g identifying increases in theft frequency)

Research Ideas

• Identifying consumer trends on automotive theft to create targeted educational materials to help drivers stay safe and protect against theft.
• Creating geographical heat maps of car theft that could be consulted by drivers when they are considering purchasing a new vehicle or relocating.
• Providing data-driven recommendations on the best types of vehicles and precautions that drivers should take if they live in an area with higher rates of car theft

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2015_State_Top10Report_wTotalThefts.csv | Column name | Description | |:---------------|:--------------------------------------------------------| | State | The state in which the car theft occurred. (String) | | Rank | The rank of the car theft in the state. (Integer) | | Make/Model | The make and model of the car that was stolen. (String) | | Model Year | The year of the car that was stolen. (Integer) | | Thefts | The number of thefts of the car in the state. (Integer) |

File: Top25-2015-Models-2015-thefts-for-release.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------------| | Theft_Year | The year in which the theft occurred. (Integer) | | Veh Model Yr | The model year of the vehicle that was stolen. (Integer) | | **...

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Car Production in the United States increased to 11.04 Million Units in August from 10.42 Million Units in July of 2025. This dataset provides - United States Car Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

Motor Vehicle Registrations by State and Type

US Motor Vehicle Registrations by Year, State, Type

By Throwback Thursday [source]

About this dataset

The dataset titled US Motor Vehicle Registrations 1900 - 1995 offers a comprehensive overview of motor vehicle registrations in the United States spanning from the early 20th century to the mid-1990s. This valuable dataset presents detailed information on the number of registered vehicles based on various factors, including year, state, vehicle type, and total registrations. It consists of five columns that represent essential data: Year (numeric), State (text), Vehicle Type (text), and Number of Registrations (numeric). With this dataset at your disposal, you can explore and analyze historical trends in motor vehicle registrations across different states and examine the preferences for various types of vehicles over time

How to use the dataset

Introduction:

Step 1: Familiarize yourself with the columns: Take a moment to understand each column present in this dataset:

• Year: Represents the specific year when motor vehicle registrations were recorded.
• State: Indicates the state in which these registrations were documented.
• Vehicle Type: Identifies the type of vehicles for which registrations were recorded (e.g., passenger cars, trucks, motorcycles).
• Number of Registrations: Indicates the total count of motor vehicle registrations for a particular year, state, and vehicle type.

Step 2: Set your research objectives: Determine your research or analysis goals before diving into data exploration. Clearly define what aspects you want to examine using this dataset. Possible research questions may include:

a) How have motor vehicle registration numbers changed over time? b) Which states have had consistently high or low registration numbers? c) What are some trends regarding different types of vehicles registered across states?

Step 3: Filter data based on specific criteria: To answer your research questions effectively, you might need to filter relevant data points. Here's how you can do it:

a) Year-wise Analysis - Filter data for specific years if you want a focused view. b) State-wise Analysis - Choose records related only to certain states if regional comparisons are required. c) Vehicle Type Analysis - Isolate data related to particular types of vehicles (passenger cars/trucks/motorcycles), if necessary.

Step 4: Analyze and visualize data: Once you have filtered the dataset based on your research objectives, it's time to analyze and visualize the information to gain insights. You can use statistical measures, charts, or graphs for a better understanding of the data distribution and trends.

a) Total Vehicle Registrations over Time - Visualize how motor vehicle registrations have changed from 1900 to 1995 using line charts or bar graphs. b) State-wise Comparisons - Utilize charts or maps to compare registration numbers between different states. c) Vehicle Type Breakdown - Explore pie charts or stacked bar graphs to understand the proportion of different vehicle types registered within specific states

Research Ideas

• Analyzing trends in motor vehicle registrations: By examining the number of registrations over a span of 95 years, researchers can identify trends and patterns in vehicle ownership and usage across different states and vehicle types. This information can be valuable for urban planning, transportation system design, and policy-making.
• Comparing vehicle preferences across states: The dataset allows for a comparison of vehicle types preferred by residents of different states. Researchers can analyze whether certain states have a higher proportion of trucks or motorcycles compared to passenger cars, which can provide insights into regional cultural preferences or economic factors.
• Studying the impact of technological advancements on vehicle registrations: Since the dataset covers a period spanning from 1900 to 1995, it provides an opportunity to study how technological advancements such as the introduction of automobiles or changes in engine efficiency impacted motor vehicle registrations over time. This information can contribute to understanding historical shifts in transportation patterns and inform predictions about future trends as well

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Week 21 - US Motor Vehicle Registrations 1900 - 1995.csv | Column name | ...

United States US: Passenger Cars: Per One Million Units of Current USD GDP data was reported at 6.065 Ratio in 2019. This records a decrease from the previous number of 6.445 Ratio for 2018. United States US: Passenger Cars: Per One Million Units of Current USD GDP data is updated yearly, averaging 9.732 Ratio from Dec 1994 (Median) to 2019, with 26 observations. The data reached an all-time high of 16.741 Ratio in 1994 and a record low of 6.065 Ratio in 2019. United States US: Passenger Cars: Per One Million Units of Current USD GDP data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Motor Vehicles Statistics: OECD Member: Annual. PASSENGER CARS The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles.; PASSENGER CARS A passenger car is a road motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). It refers to category M1 of the UN Consolidated Resolution on the Construction of Vehicles. Passenger cars, vans designed and used primarily for transport of passengers, taxis, hire cars, ambulances and motor homes are not included. Light goods road vehicles, motor-coaches and buses and mini-buses/mini-coaches are not included. Microcars (needing no permit to be driven), taxis and passenger hire cars, provided that they have fewer than ten seats, are included.; PASSENGER CARS Passenger car refers to a motor vehicle other than a motorcycle, utility vehicle or low-speed vehicle consisting of a transport device typically designed for carrying eight or fewer persons.

This table contains values from Compare.com's proprietary database of car insurance quotes about average DynamicTable.dataset.coverage.monthly_cost_total car insurance costs DynamicTable.dataset.source.stateAvgPrices

Introduction

Welcome to the US English Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native English speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of United States of America1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English voice assistant speech recognition models.

License

This US English In-car audio dataset is created by FutureBeeAI and is available for commercial use.

Total Vehicle Sales in the United States decreased to 15.30 Million in October from 16.40 Million in September of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

Used Car Prices MoM in the United States decreased to -2 percent in October from -0.20 percent in September of 2025. This dataset includes a chart with historical data for the United States Used Car Prices MoM.

Vehicle Thefts by State and Make/Model

Identifying Highest Risk Models and Locations

By Joe Boutros [source]

About this dataset

This dataset compiled by the National Insurance Crime Bureau (NICB) reveals the top ten most stolen cars in each state and across the country during the year 2015. Additionally, this dataset provides information on the top 25 stolen model year cars in 2015. It offers insights into theft trends through valuable data points such as vehicle, make and model, and thefts per state. This dataset can help you gain access to important data which will enable you to make informed decisions about car purchase, insurance rates or areas where extra precaution may be necessary when it comes to protecting your automobile investment

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How to use the dataset

This dataset provides an in-depth look at the vehicles that are most often stolen in the United States during 2015. It can be used to understand which models and makes of cars are most frequently targeted by thieves on a state-by-state basis.

The two files included in this dataset provide information about both the general trends for all U.S. states and more specific insights about the top 25 stolen 2015 model year cars.

The first file, 2015_State_Top10Report_wTotalThefts.csv, includes detailed information regarding each of the top ten most stolen vehicles per state. Each row contains data such as state and rank, make/model, thefts by number and year of thefts as well as vehicle model year (allowing users to track trends over time). It is important to note that rank specifies which car is most frequently stolen overall within a given state for all years combined (and not just within 2015), whereas thefts only indicate how many times a specific vehicle was reported stolen within that chosen state for the given year (as stated above). Additionally, # of Thefts reflects how many times each model has been reported stolen nationally regardless of individual location or date - it gives an indication to how widespread a particular car's theft rate is across America. All in all this file provides valuable information regarding individual type and make/model combinations across various states in American while being able to pinpoint exact numbers with regards to frequency of theft over time intervals This can help inform users on whether certain makes/models have risen or decreased in desirability among thieves thus impacting their decision making when purchasing or insuring their own cars..

The second file Top25-2015-Models-2015-thefts-for-release.csv offers detailed insight into exactly 25 car models which were reported as being amongst those with highest localised incidence factor when concerning theft reports coming out of various US states throughout 2015 – consequently it may also be called „most popular” list amongst criminal circles when referring specifically NATIONALLY occurring incidents within one given calendar year . The average user can utilize this data along similar means as before - reviewing „popularity” title conferred upon them either locally through investigation into variations between popular target choices between different areas but also compare those against national rates too . It has one slight difference however from previous entry – model years covered revert exclusively towards brand new releases , i,.e maybe user would want

Research Ideas

• Targeted Crime Prevention Programs: Governments, law enforcement, and insurers can use this dataset to target areas with the highest rate of car thefts and create crime prevention programs tailored to those locations.
• Vehicle Security Analysis & Upgrades: Auto manufacturers can use this data to analyze which models are being stolen the most in certain states and adjust security systems accordingly.
• Insurance Loss Modeling & Rates: Insurance providers can use this dataset to refine their loss models by creating actuarial tables based on model type and region, which would help them better define risk profiles for customers in different locations across the country

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material...

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Sales of used light vehicles in the United States came to around **** million units in 2024. In the same period, approximately **** million new light trucks and automobiles were sold here. Declining availability of vehicles In the fourth quarter of 2024, about ***** million vehicles were in operation in the United States, an increase of around *** percent year-over-year. The rising demand for vehicles paired with an overall price inflation lead to a rise in new vehicle prices. In contrast, used vehicle prices slightly decreased. E-commerce: a solution for the bumpy road ahead? Financial reports have revealed how the outbreak of the coronavirus pandemic has triggered a shift in vehicle-buying behavior. With many consumer goods and services now bought online due to COVID-19, the automobile industry has also started to digitally integrate its services online to reach consumers with a preference for contactless test driving amid the global crisis. Several dealers and automobile companies had already begun to tap into online car sales before the pandemic, some of them being Carvana and Tesla.

The Florida Department of Transportation (FDOT or Department) has identified processed, authoritative datasets to support the preliminary spatial analysis of equity considerations. These processed datasets are available at larger geographies, such as the United States Census Bureau tract or county-level; however, additional raw datasets from other sources can be used to identify equity considerations. Most of this raw data is available at the Census block group, parcel, or point-level—but additional processing is required to make suitable for spatial analysis. For more information, contact Dana Reiding with the FDOT Forecasting and Trends Office (FTO).The American Community Survey (ACS) Vehicle Availability Variables – Boundaries layer is identified to support the equity community indicator of transportation accessibility. The layer contains the most current release of data from the ACS about household size by number of vehicles available. These are 5-year estimates shown by tract, county, and state boundaries. The layer is owned and managed by the ESRI Demographics Team. Data Link: https://www.arcgis.com/home/item.html?id=9a9e43ec1603446880c50d4ed1df2207 Available Geography Levels: State, County, Tract Owner/Managed By: ESRI Demographics FDOT Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

The Vehicle Inventory and Use Survey (VIUS) is conducted in partnership with the Bureau of Transportation Statistics, Federal Highway Administration, and the U.S. Department of Energy to better understand the characteristics and use of trucks on our nation's roads. The survey universe for the VIUS includes all private and commercial trucks registered (or licensed) in the United States. This includes: pickups; minivans, other light vans, and sport utility vehicles; other light single-unit trucks (GVW = 26,000 lbs.); and truck tractors. The VIUS sample excludes vehicles owned by federal, state, and local governments; ambulances; buses; motor homes; farm tractors; unpowered trailer units; and trucks reported to have been disposed of prior to January 1 of the survey year. VIUS provides data on the physical and operational characteristics of the nation's truck population. Its primary goal is to produce estimates of the total number of trucks and truck miles. This dataset provides national and state-level summary statistics for in-scope vehicles that were used at least partially for commercial purposes.

This dataset included Information about 43 brands, and 445 models of vehicles for sale in the US. The period is from 2013 to 2022 Data source: www.goodcarbadcar.net, www.marklines.com/en/vehicle_sales/index