This data has been consolidated from Victoria Police reports and Hospital injury information, then validated and enriched to provide a comprehensive and detailed view of road crashes and injuries across Victoria. The data provides users with information about Victorian fatal and injury road crash data based on time, location, conditions, crash type, road user type, and other relevant attributes. Data Currency This information will be updated on a monthly basis but with a 7 month lag in order to provide a comprehensive view of incidents during that time period. Data Structure The CSV data is split across multiple tables with attributes to facilitate joins between the information. This has been captured as part of the supporting documentation in the metadata. The tables and attributes include: - accident (basic accident details, time, severity, location) - person (person based details, age, gender etc) - vehicle (vehicle based data, vehicle type, make etc) - accident_event (sequence of events e.g. left road, rollover, caught fire) - road_surface_cond (whether road was wet, dry, icy etc) - atmospheric_cond (rain, winds etc) - sub_dca (detailed codes describing accident) - accident_node (master location table - NB subset of accident table) - Node Table with Lat/Long references There is also a lite Victoria Road Crash .csv dataset is a single flat file containing a subset of the attributes from the other CSV files. It provides a single set of attributes for each road crash that has occurred within Victoria. Supporting documentation in the metadata will provide further details of the attributes. This used to be a .GeoJSON file however due to feedback from a significant number of Open Data users, this was changed to a .csv file. Disclaimer No claim is made as to the accuracy or currency of the content on this site at any time, there will be instances where attributes relating to a crash are amended over time. This data is provided on the basis that users undertake responsibility for assessing the relevance and accuracy of its content. Data relating to fatal crashes that have occurred recently are provisional and are subject to change or removal. They will have a high level of incompleteness and details will be amended before they are finalised. The Victorian Government and Department of Transport and Planning accept no liability to any person or group for the data or advice (or the use of such data or advice) which is provided or incorporated into it by reference.

Community Maps provides Wisconsin's law enforcement agencies and county Traffic Safety Commissions with a statewide map of all police reported motor vehicle crashes from 2010 to the current year. Fatal crashes are included from 2001. Crashes are updated on a nightly basis using geo-coded locations from the Wisconsin Department of Transportation DT4000 police crash report. The Community Maps system was designed to support and enhance traffic safety planning, resource allocation, and decision support at the local level, in particular through the regular review of crashes at each of the county quarterly TSC meetings.

Community Maps is hosted at the University of Wisconsin-Madison by the Wisconsin Traffic Operations and Safety (TOPS) Laboratory in collaboration with the Wisconsin Department of Transportation (WisDOT) Bureau of Transportation Safety (BOTS).

New: For crash data analysis requests, please email the BOTS Program and Policy Unit at CrashDataAnalysis@dot.wi.gov.

For Community Maps technical support, please email community-maps@topslab.wisc.edu.

To request access to Community Maps Advanced features, please use the WisTransPortal online User Account Request Form.

Additional contact information:

Randy Wiessinger Statewide Law Enforcement Liaison Bureau of Transportation Safety (BOTS) Division of State Patrol, WisDOT Email: rpw@wiessinger.com

Steven T. Parker, Ph.D. Traffic Operations and Safety (TOPS) Laboratory UW-Madison Department of Civil and Environmental Engineering E-mail: sparker@engr.wisc.edu

This dataset contains crash information from the last five years to the current date. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, allowing for additions, deletions and modifications at any time, resulting in more accurate information in the database. Due to ongoing and continuous data entry, the numbers of records in subsequent extractions are subject to change.About Crash DataThe Cary Police Department strives to make crash data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. As the data is updated on this site there will be instances of adding new incidents and updating existing data with information gathered through the investigative process.Not surprisingly, crash data becomes more accurate over time, as new crashes are reported and more information comes to light during investigations.This dynamic nature of crash data means that content provided here today will probably differ from content provided a week from now. Likewise, content provided on this site will probably differ somewhat from crime statistics published elsewhere by the Town of Cary, even though they draw from the same database.About Crash LocationsCrash locations reflect the approximate locations of the crash. Certain crashes may not appear on maps if there is insufficient detail to establish a specific, mappable location.

Please note that 2025 data are incomplete and will be updated as additional records become available. Data are complete through 12/31/2024. Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes, as well as access to the data sets and any supplemental data. The Engineering and Transportation Department uses this data to improve safety in Tempe.This data includes vehicle/vehicle, vehicle/bicycle, and vehicle/pedestrian crashes in Tempe. The data also includes the type of crash and location. This layer is used in the related Vision Zero story map, web maps, and operations dashboard. Time ZonesPlease note that data is stored in Arizona time, which is UTC-07:00 (7 hours behind UTC) and does not adjust for daylight saving (as Arizona does not partake in daylight saving). The data is intended to be viewed in Arizona time. Data downloaded as a CSV may appear in UTC time and, in some rare circumstances and locations, may display online in UTC or local time zones. As a reference to check data, the record with incident number 2579417 should appear as Jan. 10, 2012, 9:04 AM.Please note that 2025 data are incomplete and will be updated as additional records become available. Data are complete through 12/31/2024.This page provides data for the High Severity Traffic Crashes performance measure. The performance measure page is available at 1.08 High Severity Traffic CrashesAdditional InformationSource: Arizona Department of Transportation (ADOT)Contact (author): Shelly SeylerContact (author) E-Mail: Shelly_Seyler@tempe.govContact (maintainer): Julian DresangContact (maintainer) E-Mail: Julian_Dresang@tempe.govData Source Type: CSV files and Excel spreadsheets can be downloaded from the ADOT websitePreparation Method: Data is sorted to remove license plate numbers and other sensitive informationPublish Frequency: semi-annuallyPublish Method: ManualData Dictionary

Traffic accidents in the City and County of Denver. The data is based on the National Incident Based Reporting System (NIBRS). The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Accident data is updated Monday through Friday.

SerialNumber - The serial number for each crash incident and is applied to each unit involved. Use this field as the dissolve field to update Raw_Crash_Dissolve.PMID - A unique ID field used to summarize crashes per segment to join to the model network.

The State of Michigan’s criteria for a crash is a motor vehicle that was in transport and on the roadway, that resulted in death, injury, or property damage of $1,000 or more. Traffic crashes in this dataset are derived from SEMCOG’s Open Data Portal. Each row in the dataset represents a traffic crash that includes data about when and where the crash occurred, road conditions, number of individuals involved in the crash, and various factors that apply to the crash (Train, Bus, Deer, etc.). Also included is the number of injuries and fatalities that are associated with the crash.

Cary, NC Crash Data

Injuries, Fatalities, and Contributing Factors

By Town of Cary [source]

About this dataset

The Town of Cary Crash Database contains five years worth of detailed crash data up to the current date. Each incident is mapped based on National Incident-Based Reporting System (NIBRS) criteria, providing greater accuracy and access to all available crashes in the County.

This valuable resource is constantly being updated – every day fresh data is added and older records are subject to change. The locations featured in this dataset reflect approximate points of intersection or impact. In cases when essential detail elements are missing or rendered unmapable, certain crash incidents may not appear on maps within this source.

We invite you to explore how crashes have influenced the Town of Cary over the past five years – from changes in weather conditions and traffic controls to vehicular types, contributing factors, travel zones and more! Whether it's analyzing road design elements or assessing fatality rates – come take a deeper look at what has shaped modern day policies for safe driving today!

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How to use the dataset

• Understanding Data Elements – The first step in using this dataset is understanding what information is included in it. The data elements include location descriptions, road features, character traits of roads and more that are associated with each crash. Additionally, the data provides details about contributing factors, light conditions, weather conditions and more that can be used to understand why certain crashes happen in certain locations or under certain circumstances.

Research Ideas

• Analyzing trends in crash locations to better understand where crashes are more likely to occur. For example, using machine learning techniques and geographical mapping tools to identify patterns in the data that could enable prediction of future hotspots of crashes.
• Investigating the correlations between roadway characteristics (e.g., surface, configuration and class) and accident severities in order to recommend improvements or additional preventative measures at certain intersections or road segments which may help reduce crash-related fatalities/injuries.
• Using data from various contributing factors (e.g., traffic control, weather conditions, work area) as an input for a predictive model for analyzing the risk factors associated with different types of crashes such as head-on collisions, rear-end collisions or side swipe accidents so that safety alerts can be issued for public awareness campaigns during specific times/days/conditions where such incidents have been known to occur more often or have increased severity repercussions than usual (i.e., near schools during school days)

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - 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. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: crash-data-3.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------------------| | type | The type of crash, such as single-vehicle, multi-vehicle, or pedestrian. (String) | | features | The features of the crash, such as location, contributing factors, vehicle types, and more. (String) |

File: crash-data-1.csv | Column name | Description | |:-------------------------|:----------...

Crashes on the roadway blocks network of Washington, DC maintained by the District Department of Transportation (DDOT). In addition to locations, a related table consisting of crash details is available for each crash. This table provides some anonymized information about each of the persons involved in the crash (linked by CRASHID). These crash data are derived from the Metropolitan Police Department's (MPD) crash data management system (COBALT) and represent DDOT's attempt to summarize some of the most requested elements of the crash data. Further, DDOT has attempted to enhance this summary by locating each crash location along the DDOT roadway block line, providing a number of location references for each crash. In the event that location data is missing or incomplete for a crash, it is unable to be published within this dataset.Location points with some basic summary statistics,The DC ward the crash occurredSummary totals for: injuries (minor, major, fatal) by type (pedestrian, bicycle, car), mode of travel involved (pedestrian, bicycle, car), impaired participants (pedestrian, bicyclist, car passengers)If speeding was involvedNearest intersecting street nameDistance from nearest intersectionCardinal direction from the intersection Read more at https://ddotwiki.atlassian.net/wiki/spaces/GIS0225/pages/2053603429/Crash+Data. Questions on the contents of these layers should be emailed to Metropolitan Police Department or the DDOT Traffic Safety Division. Questions regarding the Open Data DC can be sent to @OpenDataDC

A. SUMMARY This table contains all fatalities resulting from a traffic crash in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded. The fatality table contains information about each party injured or killed in the collision, including any passengers. B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE. This table is filtered for fatal traffic crashes. C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4). D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge the Vision Zero initiative and the TransBASE database as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land u

The State Traffic Safety Information (STSI) portal is part of the larger Fatality Analysis Reporting System (FARS) Encyclopedia. STSI provides state-by-state traffic safety profiles, including: crash data, lives saved/savable, legislation, economic costs, grant funding, alcohol related crash data, performance measures, and geographic maps of crash data.

Sites of Road Crashes in South Australia.

A. SUMMARY This table contains all victims (parties who are injured) involved in a traffic crash resulting in an injury in the City of San Francisco. Fatality year-to-date crash data is obtained from the Office of the Chief Medical Examiner (OME) death records, and only includes those cases that meet the San Francisco Vision Zero Fatality Protocol maintained by the San Francisco Department of Public Health (SFDPH), San Francisco Police Department (SFPD), and San Francisco Municipal Transportation Agency (SFMTA). Injury crash data is obtained from SFPD’s Interim Collision System for 2018 to YTD, Crossroads Software Traffic Collision Database (CR) for years 2013-2017 and the Statewide Integrated Transportation Record System (SWITRS) maintained by the California Highway Patrol for all years prior to 2013. Only crashes with valid geographic information are mapped. All geocodable crash data is represented on the simplified San Francisco street centerline model maintained by the Department of Public Works (SFDPW). Collision injury data is queried and aggregated on a quarterly basis. Crashes occurring at complex intersections with multiple roadways are mapped onto a single point and injury and fatality crashes occurring on highways are excluded.

The crash, party, and victim tables have a relational structure. The traffic crashes table contains information on each crash, one record per crash. The party table contains information from all parties involved in the crashes, one record per party. Parties are individuals involved in a traffic crash including drivers, pedestrians, bicyclists, and parked vehicles. The victim table contains information about each party injured in the collision, including any passengers. Injury severity is included in the victim table.

For example, a crash occurs (1 record in the crash table) that involves a driver party and a pedestrian party (2 records in the party table). Only the pedestrian is injured and thus is the only victim (1 record in the victim table).

B. HOW THE DATASET IS CREATED Traffic crash injury data is collected from the California Highway Patrol 555 Crash Report as submitted by the police officer within 30 days after the crash occurred. All fields that match the SWITRS data schema are programmatically extracted, de-identified, geocoded, and loaded into TransBASE. See Section D below for details regarding TransBASE.

C. UPDATE PROCESS After review by SFPD and SFDPH staff, the data is made publicly available approximately a month after the end of the previous quarter (May for Q1, August for Q2, November for Q3, and February for Q4).

D. HOW TO USE THIS DATASET This data is being provided as public information as defined under San Francisco and California public records laws. SFDPH, SFMTA, and SFPD cannot limit or restrict the use of this data or its interpretation by other parties in any way. Where the data is communicated, distributed, reproduced, mapped, or used in any other way, the user should acknowledge TransBASE.sfgov.org as the source of the data, provide a reference to the original data source where also applicable, include the date the data was pulled, and note any caveats specified in the associated metadata documentation provided. However, users should not attribute their analysis or interpretation of this data to the City of San Francisco. While the data has been collected and/or produced for the use of the City of San Francisco, it cannot guarantee its accuracy or completeness. Accordingly, the City of San Francisco, including SFDPH, SFMTA, and SFPD make no representation as to the accuracy of the information or its suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. As all data is associated with methodological assumptions and limitations, the City recommends that users review methodological documentation associated with the data prior to its analysis, interpretation, or communication.

This dataset can also be queried on the TransBASE Dashboard. TransBASE is a geospatially enabled database maintained by SFDPH that currently includes over 200 spatially referenced variables from multiple agencies and across a range of geographic scales, including infrastructure, transportation, zoning, sociodemographic, and collision data, all linked to an intersection or street segment. TransBASE facilitates a data-driven approach to understanding and addressing transportation-related health issues, informed by a large and growing evidence base regarding the importance of transportation system design and land use decisions for health. TransBASE’s purpose is to inform public and private efforts to improve transportation system safety, sustainability, community health and equity in San Francisco.

E. RELATED DATASETS Traffic Crashes Resulting in Injury Traffic Crashes Resulting in Injury: Parties Involved TransBASE Dashboard iSWITRS TIMS

This service contains layers that are used in the Weber/Morgan County Pedestrain Safety Story map. It contains location and related information for pedestrian-related crashes occuring during a three-year analysis period (2016-18). The service was developed to assist a Move Utah meeting with Weber and Morgan Counties regarding pedestrian safety. The service also contains heat maps at two different resolutions showing the concentrations of these pedestrian-related crashes. Questions about this service and related maps can be directed to the Active Transportation Manager, Heidi Goedhart.

Police-recorded crash data has improved over time, but still fails to report all aspects of crashes that are important to developing a full understanding of crash mechanisms, injury burdens, pre-crash conditions, and ultimately total health and cost outcomes. Traditionally, safety and injury analysis has occurred in siloed fields, with road safety researchers relying predominately on police-recorded crash reports, and public health researchers relying on hospitalization records. Depending on the context of the study and the database used, findings vary. This is the case for the micro-level (e.g., injury severity of an individual) to the macro-level (e.g., injury rate) scale. This project begins to map disparate data sets to inform questions surrounding crashes. The data-mapping process will aim to build linkages between police-crash datasets and other datasets (i.e., incident-oriented data, spatial data, emerging datasets) and scale it up to larger geographic areas. Efforts to augment crash data are not new. A notable health-oriented example which sought to link health and police records was the Crash Outcome Data Evaluation System (CODES). Although this federal program ended in 2013, some states, including California, North Carolina, and Tennessee, have continued this effort. Added data and analytics resulted in a more “complete picture” of crashes and injuries. This complete picture enables researchers to improve their modeling, assist policy makers, and contribute to visualization that helps tell compelling safety stories that guide safety improvements.

The provided crash data comes directly from the standard DMV-349 Crash Form completed by the initial officer at the scene of a crash. Only completed crash reports will be mapped in this data. The coordinates for the crash reports are entered manually by the officer and may be subject to error. Therefore, only crashes with coordinates in Raleigh will be shown on the map.

Instructions for filtering data are available on the Open Data blog.

Follow this link to access the NC DOT DMV-349 Instruction Manual for code descriptions and definitions.https://connect.ncdot.gov/business/DMV/DMV%20Documents/DMV-349%20Instructional%20Manual.pdfUpdate Frequency: DailyTime Period: 2015-PresentTerms of UseThe Raleigh Police Department does not guarantee the accuracy of the information contained herein. While all attempts are made to ensure the correctness and suitability of information under our control and to correct any errors brought to our attention, no representation or guarantee can be made as to the correctness or suitability of the information that is presented, referenced, or implied. Data is provided by initial reports received and processed by the Raleigh Police Department. Data may be amended or corrected by the Raleigh Police Department at any time to reflect changes in the investigation, nature, or accuracy of the initial report and the Raleigh Police Department is not responsible for any error or omission, or for the use of or the results obtained from the use of this information. Misuse of the data may subject a party to criminal prosecution for false advertising under NC GS § 14-117. The Raleigh Police Department may, at its discretion, discontinue or modify this service at any time without notice.

📕 The Dataset

This dataset provides a comprehensive record of 216,000+ traffic accidents reported in Nashville, Tennessee, between January 2018 and early April 2025. It is for traffic analysts, public safety researchers, and data scientists seeking to understand patterns, risk factors, and trends in vehicular incidents across time, location, and conditions.

📝 Column Descriptions

• Accident Number: Unique numeric ID assigned to each accident report.

Example: 2,008,473,471

• Date and Time: The exact date and time the accident occurred (format: MM/DD/YYYY HH:MM:SS AM/PM).

Example: 7/14/2018, 6:00 PM

• Number of Motor Vehicles: Number of motor vehicles involved in the accident.

Example: 2

• Number of Injuries: Total number of people injured during the accident.

Example: 2

• Number of Fatalities: Total number of deaths resulting from the accident.

Example: 0

• Property Damage: Indicates if property damage occurred.

Example: Y

• Hit and Run: Whether the accident was a hit-and-run.

Example: N

• Collision Type Description: Text description of the type of collision.

Example: HEAD-ON

• Weather Description: Weather condition at the time of the accident.

Example: RAIN

• Illumination Description: Describes lighting conditions during the accident.

Example: DAYLIGHT

• Street Address: Street name or location where the accident took place.

Example: 28TH AVE N & JEFFERSON ST

• City: City in which the accident occurred.

Example: ANTIOCH

• State: State where the accident occurred

Example: TN

• Precinct: Police precinct responsible for handling the accident report.

Example: NORTH

• Lat: Latitude coordinate of the accident location.

Example: 36.168

• Long: Longitude coordinate of the accident location.

Example: -86.821

• HarmfulCodes: Coded representation of harmful events or objects involved.

Example: 12;39

• HarmfulDescriptions: Text description of the harmful event from the accident.

Example: GUARDRAIL FACE

• ObjectId: Internal GIS or system-generated identifier (matches row index).

Example: 1,2,3,4,5,6,7,8,9,ect

• Zip Code: ZIP code of the accident location.

Example: 37208

• RPA: Likely a reporting or administrative code used internally.

Example: 4525

• Weather: Numerical weather condition code.

Example: 21

• IlluACCIDEmination: Numerical lighting condition code.

Example: 3

• Collision Type: Numeric collision type code.

Example: 11

• Reporting Officer: Officer badge or ID number who filed the report.

Example: 225845

• x: X-coordinate in projected spatial system (used for GIS mapping).

Example: -9664842.682

• y: Y-coordinate in projected spatial system (used for GIS mapping).

Example: 4323742.127

The provided crash data comes directly from the standard DMV-349 Crash Form completed by the initial officer at the scene of a crash. Only completed crash reports will be mapped in this data. The coordinates for the crash reports are entered manually by the officer and may be subject to error. Therefore, only crashes with coordinates in Raleigh will be shown on the map.

Instructions for filtering data are available on the Open Data blog.

Follow this link to access the NC DOT DMV-349 Instruction Manual for code descriptions and definitions.https://connect.ncdot.gov/business/DMV/DMV%20Documents/DMV-349%20Instructional%20Manual.pdfUpdate Frequency: DailyTime Period: 2015-PresentTerms of UseThe Raleigh Police Department does not guarantee the accuracy of the information contained herein. While all attempts are made to ensure the correctness and suitability of information under our control and to correct any errors brought to our attention, no representation or guarantee can be made as to the correctness or suitability of the information that is presented, referenced, or implied. Data is provided by initial reports received and processed by the Raleigh Police Department. Data may be amended or corrected by the Raleigh Police Department at any time to reflect changes in the investigation, nature, or accuracy of the initial report and the Raleigh Police Department is not responsible for any error or omission, or for the use of or the results obtained from the use of this information. Misuse of the data may subject a party to criminal prosecution for false advertising under NC GS § 14-117. The Raleigh Police Department may, at its discretion, discontinue or modify this service at any time without notice.

Pedestrian Crashes, since 2012, which have been reported by the Police or the Public through the AFP Crash Report Form.

Please note: Although crash data is updated on regular bases it will always have a backlog of entries hence it is not a true reflection of the up to date crash locations. Crash locations are only indicative and may not reflect an accurate position of persons involved in an incident. Casualty crashes are occasionally omitted from some maps or crash reports if they are still under investigation.

A companion table for the Crashes in DC layer. This is a related table linked by field attribution, CRASHID. These crash data are derived from the Metropolitan Police Department's (MPD) crash data management system (COBALT) and represent DDOT's attempt to summarize some of the most requested elements of the crash data. Further, DDOT has attempted to enhance this summary by locating each crash location along the DDOT roadway block line, providing a number of location references for each crash. In the event that location data is missing or incomplete for a crash, it is unable to be published within this dataset.Crash details related table,Type of participant (driver, occupant, bicyclist, pedestrian)Age of participantsIf injured, severity (minor, major, fatal)Type of vehicle (passenger car, large truck, taxi, government, bicycle, pedestrian, etc)If persons issued a ticketIf a vehicle, the state (jurisdiction) license plate was issued (not license plate number)Are any persons deemed ‘impaired’Was person in vehicle where speeding was indicatedRead more at https://ddotwiki.atlassian.net/wiki/spaces/GIS0225/pages/2053603429/Crash+Data. Questions on the contents of these layers should be emailed to Metropolitan Police Department or the DDOT Traffic Safety Division. Questions regarding the Open Data DC can be sent to @OpenDataDC.