The graph illustrates the number of car accidents in the United States from 2013 to 2023. The x-axis represents the years, abbreviated from '13 to '23, while the y-axis displays the annual number of crashes. Over this 11-year period, the number of accidents ranges from a low of 5,251,006 in 2020 to a high of 6,821,129 in 2016. Other notable figures include 6,756,084 crashes in 2019 and 5,686,891 in 2013. The data exhibits significant fluctuations, with a peak in 2016, a sharp decline in 2020, and subsequent variations in the following years. This information is presented in a line graph format, effectively highlighting the yearly changes and overall variability in car accidents across the United States.

The number of road accidents per one million inhabitants in the United States was forecast to continuously decrease between 2024 and 2029 by in total 2,490.4 accidents (-14.99 percent). After the eighth consecutive decreasing year, the number is estimated to reach 14,118.78 accidents and therefore a new minimum in 2029. Depicted here are the estimated number of accidents which occured in relation to road traffic. They are set in relation to the population size and depicted as accidents per one million inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road accidents per one million inhabitants in countries like Mexico and Canada.

The graph displays the number of car accident fatalities by type in the United States from 2010 to 2022. The x-axis represents the years, labeled from '10 to '22, while the y-axis indicates the number of fatalities. Each year includes data points for four categories: Passenger Vehicle, Pedestrian, Two-Wheeled Vehicle, and Large Truck fatalities. Passenger Vehicle fatalities range from a low of 21,076 in 2014 to a high of 26,650 in 2021. Pedestrian fatalities increase from 4,300 in 2010 to a peak of 7,467 in 2022. Two-Wheeled Vehicle fatalities vary between 5,022 in 2014 and 7,287 in 2022. Large Truck fatalities are the lowest among the categories, ranging from 346 in 2010 to 533 in 2022. The data reveals an overall upward trend in fatalities across all categories, particularly notable in the years 2021 and 2022.

The number of road traffic fatalities per one million inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 18.5 deaths (+13.81 percent). After the tenth consecutive increasing year, the number is estimated to reach 152.46 deaths and therefore a new peak in 2029. Depicted here are the estimated number of deaths which occured in relation to road traffic. They are set in relation to the population size and depicted as deaths per 100,000 inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road traffic fatalities per one million inhabitants in countries like Mexico and Canada.

Motor-vehicle deaths in the United States have decreased greatly since the 1970s and 1980s. In 2023, there were around **** deaths from motor vehicles per 100,000 population, compared to a rate of **** deaths per 100,000 in 1970. Laws requiring drivers and passengers to wear safety belts and advancements in safety technology in vehicles are major drivers for these reductions. Motor-vehicle accidents in the U.S. Americans spend a significant amount of time behind the wheel. Many cities lack convenient and reliable public transportation and, especially in rural areas, cars are a necessary means of transportation. In 2020, August was the month with the highest number of fatal crashes, followed by September and June. The deadliest time of day for fatal vehicle crashes is between * and * p.m., most likely due to the after-work rush hour and more people who are under the influence of alcohol. Drinking and driving among youth Drinking and driving remains a relevant problem across the United States and can be especially problematic among younger people. In 2023, around *** percent of those aged 21 to 25 years in the United States reported driving under the influence of alcohol in the preceding year. Furthermore, around ***** percent of those aged 16 to 20 drove after drinking within the past year.

About 228,200 Americans had a license to operate a motor vehicle in the United States in 2020. That year, an estimated 36,680 people died on U.S. roads. Traffic-related fatalities per 100,000 licensed drivers stood at 17.01 in 2020.

Road safety rankings
The United States has among the highest rates of road fatalities per population worldwide. Possible contributing factors to deaths on the road can include speeding, not wearing a seatbelt, driving while under the influence of drugs or alcohol, and driving while fatigued. Traffic fatalities caused by speeding in the United States have declined since 2008, with less than 10,000 deaths recorded annually over recent years.

Automation for the nation
94 percent of severe automobile crashes are due to human error — but driving safety is taken much more seriously today than in the past, with roughly 90 percent of U.S. drivers wearing their seatbelts while driving in 2020. Over recent years, car manufacturers and developers have striven to reduce car crashes even further with partially and fully automated safety features such as forward collision warnings, lane departure warnings, rearview video systems, and automatic emergency braking. Self-driving vehicles are also set to take to the roads in the future, with car brands such as Toyota, Ford, and GM registering over 350 autonomous driving patents respectively in the United States.

Some 44,800 road traffic fatalities occurred in the United States in 2023, and projections estimate 2024 fatalities could drop to 44,700. Motor vehicle crashes and drug overdoses are the leading causes of death among those under the age of 55 in the United States.

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.

The graph displays the number of truck accidents in the United States from 2020 to 2025. The x-axis represents the years from 2020 through 2025, while the y-axis indicates the annual count of reported truck accidents. The values range from a high of 165,779 accidents in 2021 to a low of 108,126 accidents in 2025, which is a partial-year figure. The data shows an increase between 2020 and 2021, followed by a gradual decline through 2024, with 2025 reflecting fewer accidents due to incomplete year reporting.

In 2022, over 4.5 million light trucks were involved in U.S. traffic crashes, accounting for 43.2 percent of the overall total. The second highest were passenger cars, being involved in four million car crashes and accounting for 38.1 percent of the total. Motor vehicle crashes are among the leading causes of death among those under the age of 55 in the United States.

The graph illustrates the number of fatalities in truck accidents in the United States from 2021 to 2025. The x-axis represents the years, ranging from 2021 to 2025, while the y-axis shows the number of fatalities. In 2022, the number of fatal accidents was the highest, reaching 5,796. Last year, in 2024, it was the lowest. 2025 figures are still partial. This information is presented in a bar graph format, effectively highlighting the annual changes and trends in fatal truck accident occurrences in the United States.

The Motor Vehicle Collisions vehicle table contains details on each vehicle involved in the crash. Each row represents a motor vehicle involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system.

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.

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.

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.

This dataset contains state-level statistics on car accidents in the US, including contributing factors (speeding, alcohol, distractions) and insurance metrics (premiums, losses). It covers all 50 states and Washington D.C.

Columns:

total – Total car accidents (per 100M vehicle miles)

speeding – % of accidents involving speeding

alcohol – % of accidents involving alcohol

not_distracted – % of accidents without driver distraction

no_previous – % of accidents by drivers with no prior incidents

ins_premium – Avg. auto insurance premium ($)

ins_losses – Insurance losses per insured driver ($)

abbrev – State abbreviation (2-letter code)

Use Cases:

Analyze accident trends by cause (speeding, alcohol, etc.)

Compare insurance costs vs. accident rates across states

Identify high-risk states for road safety initiatives

Geographic visualization of crash data

This is publicly available data on all motor vehicle crashes in the US from the National Highway Traffic Safety Administration.

FARS is a census of fatal motor vehicle crashes with a set of data files documenting all qualifying fatalities that occurred within the 50 States, the District of Columbia, and Puerto Rico since 1975. To qualify as a FARS case, the crash had to involve a motor vehicle traveling on a traffic way customarily open to the public, and must have resulted in the death of a motorist or a non-motorist within 30 days of the crash.

One of the primary objectives of the National Highway Traffic Safety Administration is to reduce the staggering human toll and property damage that motor vehicle traffic crashes impose on our society. Crashes each year result in thousands of lives lost, hundreds of thousands of injured victims, and billions of dollars in property damage. Accurate data are required to support the development, implementation, and assessment of highway safety programs aimed at reducing this toll. NHTSA uses data from many sources, including the Fatality Analysis Reporting System (FARS) that began operation in 1975. Providing data about fatal crashes involving all types of vehicles, the FARS is used to identify highway safety problem areas, provide a basis for regulatory and consumer information initiatives, and form the basis for cost and benefit analyses of highway safety initiatives.

You will want to start with the accident dataset and go from there.

Codebook and explanation of variables found here: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813556

Fatality Analysis Reporting System (FARS) was created in the United States by the National Highway Traffic Safety Administration (NHTSA) to provide an overall measure of highway safety, to help suggest solutions, and to help provide an objective basis to evaluate the effectiveness of motor vehicle safety standards and highway safety programs.

FARS contains data on a census of fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico. To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and result in the death of a person (occupant of a vehicle or a non-occupant) within 30 days of the crash. FARS has been operational since 1975 and has collected information on over 989,451 motor vehicle fatalities and collects information on over 100 different coded data elements that characterizes the crash, the vehicle, and the people involved.

FARS is vital to the mission of NHTSA to reduce the number of motor vehicle crashes and deaths on our nation's highways, and subsequently, reduce the associated economic loss to society resulting from those motor vehicle crashes and fatalities. FARS data is critical to understanding the characteristics of the environment, trafficway, vehicles, and persons involved in the crash.

NHTSA has a cooperative agreement with an agency in each state government to provide information in a standard format on fatal crashes in the state. Data is collected, coded and submitted into a micro-computer data system and transmitted to Washington, D.C. Quarterly files are produced for analytical purposes to study trends and evaluate the effectiveness highway safety programs.

Content

There are 40 separate data tables. You can find the manual, which is too large to reprint in this space, here.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.nhtsa_traffic_fatalities.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset was provided by the National Highway Traffic Safety Administration.

The graph displays the number of crashes involving ADS-equipped vehicles in the United States from July 2021 to September 2025. The x-axis represents the months, labeled from Jul'21 to Sep'25, while the y-axis indicates the monthly count of such crashes. Over this period, the monthly crash numbers range from a low of 8 crashes—in December 2021, October 2022, and January 2023—to a high of 80 crashes in December 2024. The data shows the monthly counts fluctuating between these values throughout the three-year span.

United States US: Road Fatalities: Per One Million Vehicle-km data was reported at 7.805 Ratio in 2023. This records a decrease from the previous number of 8.265 Ratio for 2022. United States US: Road Fatalities: Per One Million Vehicle-km data is updated yearly, averaging 8.404 Ratio from Dec 1994 (Median) to 2023, with 30 observations. The data reached an all-time high of 10.731 Ratio in 1994 and a record low of 6.725 Ratio in 2014. United States US: Road Fatalities: Per One Million Vehicle-km 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: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made. ROAD TRAFFIC Road traffic is any movement of a road vehicle on a given road network. When a road vehicle is being carried on another vehicle, only the movement of the carrying (active mode) is considered. [COVERAGE] ROAD TRAFFIC IRTAD - Data refer to road motor vehicle traffic of motorised two-wheelers, passenger cars, goods road motor vehicles and buses. [STAT_CONC_DEF] ROAD TRAFFIC IRTAD - Data are calculated using automatic and manual roadside traffic counts.

AbstractThis dataset comprises detailed records of motor vehicle crashes occurring in Ohio, USA, from January 1, 2017, to December 31, 2023. Collected by law enforcement agencies using standardized OH-1 crash reporting forms and centralized by the Ohio Department of Public Safety, the dataset captures detailed information on 1,679,019 crashes involving 2,656,086 vehicles and 3,577,822 occupants. Structured across three levels—crash, vehicle, and occupant—the dataset includes attributes such as crash timing and location, environmental and road conditions, vehicle specifications, operational factors, occupant demographics, injury severity, safety equipment usage, and behavioral indicators like alcohol or drug involvement. Severity information is documented at both the crash and individual occupant levels, covering outcomes ranging from no injury to fatal incidents. The dataset features a total of 119 systematically named variables at the crash, vehicle, and occupant levels. A complete list of features, along with categorical value mappings, is provided in the accompanying documentation.Description of the data and file structureThis dataset contains comprehensive records of motor vehicle crashes reported across the state of Ohio, USA, from January 1, 2017, to December 31, 2023. The data were collected by law enforcement agencies using standardized crash reporting forms (OH-1) and centralized through the Ohio Department of Public Safety’s data systems.It captures detailed, structured information related to crash events, vehicles involved, and individuals affected. Each data sample corresponds to an occupant of a vehicle. There are unique identifiers for each crash and involved vehicle. Hence, the dataset is organized into three primary levels:Crash-Level Data: Includes unique identifiers for each of the 1,679,019 reported crashes, along with temporal details (date, time), location attributes, environmental conditions (e.g., weather, light, road surface), and overall crash characteristics (e.g., number of units involved, severity classification, work zone presence). The identifier for the crash is the feature “DocumentNumber”.Vehicle-Level Data: Comprises identifiers for each of the 2,656,086 vehicles (units) involved in a crash. Attributes include vehicle type, make, model, year of manufacture, vehicle defects, and operational details such as posted speed, traffic control devices, and pre-crash actions. Interacting vehicle types and hazardous material indicators are also documented. Vehicle-Level features are identified by the prefix ”Units.” in the feature name.Occupant-Level Data: Contains 3,577,822 records detailing individuals involved in crashes. This includes demographic information (age, gender), seating position, person injury severity, use of safety equipment (e.g., seat belts, airbags, helmets), and behavioral factors such as alcohol or drug involvement, distraction status, and test results where applicable. Occupant-Level features are identified by the prefix “Units.People.” in the feature name.The severity of the accident is also documented. The “CrashSeverity” feature document the severity of the crash in the following levels: Fatal (15021), Suspected Serious Injury (83764), Suspected Minor Injury (483026), Possible Injury (461019), and No Apparent Injury (2440823). Similarly, also individual people injury levels are recorded in the feature “Units.People.Injury”. The file "summary_2023_new.pdf" is a summary file that contains data analysis of the dataset (statistics and plots).There are 119 unique features in the data, and their complete list of name and type is reported below. Their categorical levels in case of integer-encoding is found in the file “mapping.yaml”.Access informationOther publicly accessible locations of the data:The full dataset submitted to figshare is not available elsewhere in its complete and curated form. However, data covering the most recent five years, including the current year, are publicly accessible through the following sources:Ohio Department of Public Safety Crash Retrieval Portal: https://ohtrafficdata.dps.ohio.gov/crashretrievalOhio Statistics and Analytics for Traffic Safety (OSTATS): https://statepatrol.ohio.gov/dashboards-statistics/ostats-dashboardsThese public portals provide access to selected crash data but do not include the full historical dataset or the cleaned, integrated, and reformatted version provided through this submission.Data was derived from the following sources:Ohio Department of Public SafetyHuman subjects dataThis dataset was derived entirely from publicly available traffic crash reports collected and disseminated by the Ohio Department of Public Safety through the Ohio Statistics and Analytics for Traffic Safety (OSTATS) platform.To ensure compliance with ethical standards for data sharing, this dataset contains no direct identifiers (e.g., names, addresses, license plate numbers, or VINs linked to individuals). All personal identifiers have been removed or were not included in the public dataset. Furthermore, the dataset contains no more than three indirect identifiers per record. These indirect identifiers (e.g., crash year, crash county, and age group) were selected based on their relevance to the study while minimizing re-identification risk.Where possible, continuous variables were converted to categories (e.g., age groups instead of exact age), and geographic detail was limited to broader regional indicators rather than precise location data. Data cleaning and aggregation procedures were conducted to further reduce identifiability while retaining the analytic value of the dataset for modeling injury risk across system domains.As described in the associated manuscript, all analyses were conducted on this de-identified dataset, and no additional linkage to identifiable information was performed. As such, this dataset does not require IRB oversight or data use agreements and is suitable for open-access publication under CC-BY licence.No direct interaction or intervention with human participants occurred during the creation of this dataset, and no personally identifiable information (PII) is included.Given the publicly available nature of the source data and the absence of PII, explicit participant consent was not required. However, by relying exclusively on open-access government data and following de-identification protocols aligned with the Common Rule (45 CFR 46), this dataset meets ethical standards for public data sharing.

United States US: Road Fatalities: Per One Million Road Motor Vehicles data was reported at 120.615 Ratio in 2019. This records a decrease from the previous number of 123.083 Ratio for 2018. United States US: Road Fatalities: Per One Million Road Motor Vehicles data is updated yearly, averaging 165.059 Ratio from Dec 1994 (Median) to 2019, with 26 observations. The data reached an all-time high of 212.199 Ratio in 1995 and a record low of 118.903 Ratio in 2014. United States US: Road Fatalities: Per One Million Road Motor Vehicles 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: Road Traffic and Road Accident Fatalities: OECD Member: Annual. VEHICLES 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.; ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made. VEHICLES A road motor vehicle is a road vehicle fitted with an engine whence it derives its sole means of propulsion, which is normally used for carrying persons or goods or for drawing, on the road, vehicles used for the carriage of persons or goods.; VEHICLES Motor vehicle refers to any motorised (mechanically or electronically powered) road vehicle not operated on rail.