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 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 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 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.

This dataset contains motor vehicle collision reports from the New York City Police Department (NYPD), covering the period January–August 2020. Each record represents an individual collision, including detailed information on the date, time, and location of the accident (borough, ZIP code, street name, latitude/longitude), as well as vehicles, victims, and contributing factors.

Key Features

• Date & Time – Exact timestamp of the reported collision.
• Location – Borough, ZIP code, street(s), and geographical coordinates.
• Vehicles Involved – Type of vehicles and their count per accident.
• Victims – Number of injuries and fatalities, broken down by category (pedestrians, cyclists, motorists).
• Contributing Factors – Primary cause(s) of the accident (e.g., distracted driving, speeding).

Potential Analyses

• Monthly trends – Compare the percentage of total accidents by month to identify seasonal patterns.
• Day & time patterns – Analyze accident frequency by day of the week and hour of the day.
• Street-level analysis – Determine which street recorded the most accidents and what share of total accidents that represents.
• Cause analysis – Identify the most common contributing factors for all accidents and for fatal accidents specifically.

Source

Data originally obtained from NYC Open Data. Licensed under Public Domain.

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.

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.

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.

Traffic Accident Prediction Dataset

This dataset contains data designed to predict the occurrence and severity of traffic accidents based on various factors affecting road conditions, driver behavior, and traffic situations.

Features:

Weather: The impact of weather conditions on the likelihood of accidents.

• Clear: No adverse weather conditions.
• Rainy: Rainy conditions increase the chance of accidents.
• Foggy: Foggy conditions reduce visibility, increasing accident chances.
• Snowy: Snow can cause slippery roads and higher accident probability.
• Stormy: Stormy weather can create hazardous driving conditions.

Road_Type: The type of road, influencing the probability of accidents.

• Highway: High-speed roads with higher chances of severe accidents.
• City Road: Roads within city limits, typically with more traffic and lower speeds.
• Rural Road: Roads outside urban areas, often with fewer vehicles and lower speeds.
• Mountain Road: Roads with curves and elevation changes, increasing accident risk.

Time_of_Day: The time of day when the accident occurs.

• Morning: The period between sunrise and noon.
• Afternoon: The period between noon and evening.
• Evening: The period just before sunset.
• Night: The nighttime, often associated with reduced visibility and higher risk.

Traffic_Density: The level of traffic on the road.

• 0: Low density (few vehicles).
• 1: Moderate density.
• 2: High density (many vehicles).

Speed_Limit: The maximum allowed speed on the road.

Number_of_Vehicles: The number of vehicles involved in the accident, ranging from 1 to 5.

Driver_Alcohol: Whether the driver consumed alcohol.

• 0: No alcohol consumption.
• 1: Alcohol consumption (which increases the likelihood of an accident).

Accident_Severity: The severity of the accident.

• Low: Minor accident.
• Moderate: Moderate accident with some damage or injuries.
• High: Severe accident with significant damage or injuries.

Road_Condition: The condition of the road surface.

• Dry: Dry roads with minimal risk.
• Wet: Wet roads due to rain, increasing the risk of accidents.
• Icy: Ice on the road, significantly increasing the risk of accidents.
• Under Construction: Roads under construction, which may have obstacles or poor road quality.

Vehicle_Type: The type of vehicle involved in the accident.

• Car: A regular passenger car.
• Truck: A large vehicle used for transporting goods.
• Motorcycle: A two-wheeled motor vehicle.
• Bus: A large vehicle used for public transportation.

Driver_Age: The age of the driver. Values range from 18 to 70 years old.

Driver_Experience: The years of experience the driver has. Values range from 0 to 50 years of experience.

Road_Light_Condition: The lighting conditions on the road.

• Daylight: Daytime, when visibility is typically good.
• Artificial Light: Road is illuminated with streetlights.
• No Light: Road is not illuminated, typically during the night in poorly lit areas.

Notes: This dataset can be used to train classification models to predict whether an accident will occur based on these factors. You can apply machine learning algorithms such as logistic regression, random forests, gradient boosting, or neural networks to build a predictive model.

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.

Crashes data includes crash event level details such as location - the Lat/Long of the nearest intersection, A and B street names, with distance and direction of the crash from nearest intersection, etc... It also includes crash level details like weather and roadway conditions, and time of day. Also included are the involved party (vehicle involved with), primary collision factor and severity of injury in terms of fatalities, and severe, moderate and minor injuries per crash.

The vehicles data includes the vehicle level details of the crash such as vehicle types, driver's (vehicle, party) age and sex, driver conditions and violations proceeding the crash, etc...

There is a one to many relationship that needs to be built that relates the crash to the vehicles involved. (i.e. there are an average of 2.07 vehicles/parties involved per crash)

Match the Crash name in vehicle data to the Name in the Crash data to relate the two sets of data.

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 dataset provides information on motor vehicle operators (drivers) involved in traffic collisions occurring on county and local roadways. The dataset reports details of all traffic collisions occurring on county and local roadways within Montgomery County, as collected via the Automated Crash Reporting System (ACRS) of the Maryland State Police, and reported by the Montgomery County Police, Gaithersburg Police, Rockville Police, or the Maryland-National Capital Park Police. This dataset shows each collision data recorded and the drivers involved. Please note that these collision reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the collision data available on this web page may reflect: -Information not yet verified by further investigation -Information that may include verified and unverified collision data -Preliminary collision classifications may be changed at a later date based upon further investigation -Information may include mechanical or human error This dataset can be joined with the other 2 Crash Reporting datasets (see URLs below) by the State Report Number. * Crash Reporting - Incidents Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf * Crash Reporting - Non-Motorists Data at https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5 Update Frequency : Weekly

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.

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.

The Motor Vehicle Collisions crash table contains information about car accidents in New York City. Each row in this table represents a single accident. This data is collected by the police whenever there is an accident involving injuries, fatalities, or significant property damage (more than $1000).

To better understand and improve traffic safety, the New York City Police Department (NYPD) created a program called TrafficStat in 1998. This program is inspired by another successful program called CompStat, which was originally used to reduce homicides. TrafficStat applies similar principles to reduce fatal traffic accidents.

When there is a car accident, the police fill out a form called MV-104AN, which contains all the details about the accident. This form is required for any accident involving injuries or deaths. Before TrafficStat, there was no consistent way to collect and analyze traffic safety data across all NYPD precincts.

In 1999, the NYPD introduced the Traffic Accident Management System (TAMS), which required precincts to manually enter some basic information from the MV-104AN forms. This information included the number of accidents, injuries, and fatalities at intersections.

As the years went by, there was a need for more detailed traffic data to better understand and address traffic safety issues. In 2014, a citywide traffic safety initiative called Vision Zero began. Its goal is to eliminate traffic-related fatalities.

In March 2016, the NYPD replaced TAMS with a new system called the Finest Online Records Management System (FORMS). FORMS allows police officers to electronically enter all the information from MV-104AN forms using department cellphones or computers. This data is stored in a centralized database.

With FORMS in place, all the details from MV-104AN forms are stored for every traffic accident. This means that analysts can conduct more detailed and thorough studies of traffic safety to help achieve the Vision Zero goal of eliminating traffic fatalities in New York City.

This dataset contains information about vehicles (or units as they are identified in crash reports) involved in a traffic crash. This dataset should be used in conjunction with the traffic Crash and People dataset available in the portal. “Vehicle” information includes motor vehicle and non-motor vehicle modes of transportation, such as bicycles and pedestrians. Each mode of transportation involved in a crash is a “unit” and get one entry here. Each vehicle, each pedestrian, each motorcyclist, and each bicyclist is considered an independent unit that can have a trajectory separate from the other units. However, people inside a vehicle including the driver do not have a trajectory separate from the vehicle in which they are travelling and hence only the vehicle they are travelling in get any entry here. This type of identification of “units” is needed to determine how each movement affected the crash. Data for occupants who do not make up an independent unit, typically drivers and passengers, are available in the People table. Many of the fields are coded to denote the type and location of damage on the vehicle. Vehicle information can be linked back to Crash data using the “CRASH_RECORD_ID” field. Since this dataset is a combination of vehicles, pedestrians, and pedal cyclists not all columns are applicable to each record. Look at the Unit Type field to determine what additional data may be available for that record.

The Chicago Police Department reports crashes on IL Traffic Crash Reporting form SR1050. The crash data published on the Chicago data portal mostly follows the data elements in SR1050 form. The current version of the SR1050 instructions manual with detailed information on each data elements is available here.

Change 11/21/2023: We have removed the RD_NO (Chicago Police Department report number) for privacy reasons.