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 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

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.

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.

Summary of 2025 Charlotte crash counts, injury and fatality totals, top causes, corridors, and time patterns derived from CMPD and NCDOT public data.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, 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.

Traffic fatalities within the City of Chicago that are included in Vision Zero Chicago (VZC) statistics. Vision Zero is Chicago’s commitment to eliminating fatalities and serious injuries from traffic crashes. The VZC Traffic Fatality List is compiled by the Chicago Department of Transportation (CDOT) after monthly reviews of fatal traffic crash information provided by Chicago Police Department’s Major Accident Investigation Unit (MAIU). CDOT uses a standardized process – sometimes differing from other sources and everyday use of the term -- to determine whether a death is a “traffic fatality.” Therefore, the traffic fatalities included in this list may differ from the fatal crashes reported in the full Traffic Crashes dataset (https://data.cityofchicago.org/d/85ca-t3if). Official traffic crash data are published by the Illinois Department of Transportation (IDOT) on an annual basis. This VZC Traffic Fatality List is updated monthly. Once IDOT publishes its crash data for a year, this dataset is edited to reflect IDOT’s findings. VZC Traffic Fatalities can be linked with other traffic crash datasets using the “Person_ID” field. State of Illinois considers a “traffic fatality” as any death caused by a traffic crash involving a motor vehicle, within 30 days of the crash. Fatalities that meet this definition are included in this VZC Traffic Fatality List unless excluded by any criteria below. There may be records in this dataset that do not appear as fatalities in the other datasets. The following criteria exclude a death from being considered a "traffic fatality," and are derived from Federal and State reporting standards. The Medical Examiner determined that the primary cause of the fatality was not the traffic crash, including: a. The fatality was reported as a suicide based on a police investigation. b. The fatality was reported as a homicide in which the "party at fault" intentionally inflicted serious bodily harm that caused the victim's death. c. The fatality was caused directly and exclusively by a medical condition or the fatality was not attributable to road user movement on a public roadway. (Note: If a person driving suffers a medical emergency and consequently hits and kills another road user, the other road user is included, although the driver suffering a medical emergency is excluded.) The crash did not occur within a trafficway. The crash involved a train or other such mode of transport within the rail dedicated right-of-way. The fatality was on a roadway not under Chicago Police Department jurisdiction, including: a. The fatality was occurred on an expressway. The City of Chicago does not have oversight on the expressway system. However, a fatality on expressway ramps occurring within the City jurisdiction will be counted in VZC Traffic Fatality List. b. The fatality occurred outside City limits. Crashes on streets along the City boundary may be assigned to another jurisdiction after the investigation if it is determined that the crash started or substantially occurred on the side of the street that is outside the City limits. Jurisdiction of streets along the City boundary are split between City and neighboring jurisdictions along the street centerline. The fatality is not a person (e.g., an animal). Change 12/7/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.

The car crash dataset provides a detailed compilation of information related to common factors influencing road accidents, such as collision severity, weather conditions, road types, and contributing elements, offering valuable insights for the analysis and enhancement of overall road safety measures.

The Statistics on Terrestrial Traffic Accidents in Urban and Suburban Areas (ATUS) aims to generate annual information regarding road traffic accident rates at the national, state, and municipal levels. This is achieved by collecting and processing data on accidents that occur in non-federal areas, thus contributing to transport planning and organization.

This dataset is divided into two parts:

• ATUS Data Set: Contains all information regarding traffic accidents, including metadata, a data dictionary, and auxiliary files such as the entity-relationship diagram. This is packaged as "conjunto_de_datos_atus_anual_csv".
• ATUS Geospatial Data: Contains geospatial data for all traffic accidents in Mexico from 2019 to 2023. These are named as "atus_YYYY_shp". Inside these archives, you will find "ATUS_YYYY>conjunto_de_datos", which includes the .shp files, all associated geospatial metadata, and a .csv file containing all geographic data.

Note: For 2018, municipalities where the reporting source officially submitted a "zero certificate" (certifying no registered traffic accidents in their jurisdiction) are included.

Updates and Revisions

• On August 1, 2025, the figures for fatalities in 2024 were corrected for the municipalities of Aguascalientes (Aguascalientes), Apodaca (Nuevo León), and Hermosillo (Sonora) following verification with the reporting source. This correction affects the atus_anul_2024 dataset variables: CONDMUERTO, CONDHERIDO, PASAMUERTO, PASAHERIDO, PEATMUERTO, PEATHERIDO, CICLMUERTO, CICLHERIDO, NEMUERTO, NEHERIDO, and CLASACC.
• On August 19, 2024, the 2023 open data set was updated to reflect a recoding of municipality codes 10 and 100 in applicable cases.
• Following the application of new validation criteria to the statistical series, data was updated for the variable 'Day' (series 1998-2011) and the variable 'Day of the week the accident occurred' (series 1997-2015).
• The accident type was updated for the year 2017, and the catalogs of federal entities and municipalities were updated. Consequently, the information now holds the status of revised figures.
• Finally, 114 additional records were integrated for the year 2018, which elevates those figures to definitive status.

IMPORTANT NOTE!

This information is scheduled for cleaning and processing, which will include translating all terms from Spanish to English, changing the file encoding to UTF-8 to eliminate special characters, converting the shapefiles to geographic coordinates, among other tasks. This effort aims to make this highly relevant information accessible for research by a broader audience. public notebooks with this process will be available soon - Last update October 14th 2025

This dataset focuses on tracking fatalities from motor vehicle crashes in Virginia, with a breakdown of those fatalities by the age of the individuals involved. This dataset is crucial for understanding how age impacts the likelihood of being killed in a traffic accident and can help target safety initiatives for specific age groups.

Dataset Card for US Accidents (2016 - 2021)

  Dataset Summary





  Description

This is a countrywide car accident dataset, which covers 49 states of the USA. The accident data are collected from February 2016 to Dec 2021, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by a variety of entities, such as the US and state departments of transportation, law enforcement agencies, traffic cameras, and… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/us-accidents.

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.

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

This is a subset of dataset https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents with some columns dropped making it easier for practicing EDA
Acknowledgements If you use this dataset, please kindly cite the following papers:

Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.

Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. "Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights." In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.

Content This dataset was collected in real-time using multiple Traffic APIs. It contains accident data collected from February 2016 to March 2023 for the Contiguous United States. For more details about this dataset, please visit [here].

Inspiration The US-Accidents dataset can be used for numerous applications, such as real-time car accident prediction, studying car accident hotspot locations, casualty analysis, extracting cause and effect rules to predict car accidents, and studying the impact of precipitation or other environmental stimuli on accident occurrence. The most recent release of the dataset can also be useful for studying the impact of COVID-19 on traffic behavior and accidents.

Sampled Data (New!) For those requiring a smaller, more manageable dataset, a sampled version is available which includes 500,000 accidents. This sample is extracted from the original dataset for easier handling and analysis.

Other Details Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. Regrettably, the dataset will no longer be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the papers mentioned above.

Inquiries or need help? For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

The traffic accident and casualties statistics tabulated in this dataset are compiled and retrieved from the Transport Information System developed and enhanced by the Road Safety and Standards Division. The accidents included are those personal injury accidents reported to the Police and do not include damage-only accidents.

The dataset contains information on the Motor vehicle fatalities on U.S. roads with time of the accidents, Day of the accidents and weather conditions. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.

The National Center for Statistics and Analysis (NCSA), an office of the National Highway Traffic Safety Administration (NHTSA), is responsible for providing a wide range of analytical and statistical support to NHTSA and the highway safety community at large, in the general areas of: Human, vehicle, environmental, and roadway characteristics, as they relate to crash frequency and injuries; Identifying injury mechanisms and associated crash dynamics in motor vehicle crashes; Evaluating the effectiveness of crashworthiness, crash avoidance, and traffic safety efforts; Monitoring the magnitude of the traffic safety problem; and Quantifying the benefits resulting from proposed agency rules.

This dataset appears to represent the total number of fatal crashes in Virginia from the year 1994 to 2022.