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 2024, the state of California reported 5,059 motor-vehicle deaths, an increase from the year before. Death from motor-vehicles remains a relevant problem across the United States. Motor-vehicle deaths in the United States In the United States, a person’s lifetime odds of dying in a motor vehicle accident is around 1 in 93. Death rates from motor vehicles have decreased in recent years and are significantly lower than the rates recorded in the 1970s and 1980s. This is due to a mass improvement in car safety standards and features. For example, all states, with the exception of New Hampshire, have laws against not wearing safety belts. Drinking and driving One of the biggest causes of motor-vehicle deaths is driving while under the influence of alcohol. The state with the highest number of fatalities due to alcohol-impaired driving in 2022 was Texas, followed by California and Florida. Light trucks are the vehicle type most often involved in fatal crashes caused by alcohol-impaired drivers, with around 5,406 such accidents in the United States in 2022.

Alcohol-Impaired Driving Fatalities 2005-2014; All persons killed in crashes involving a driver with BAC >= .08 g/dL. Occupant Fatalities 2005-2014; All occupants killed where body type = 1-79. Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2005-2013 Final Reports and 2014 Annual Report File

1. Data source: Road Safety Information Inquiry Network: Traffic accident statistics at a glance https://roadsafety.tw 2. Statistical conditions: All drivers (cars, motorcycles, power machinery, slow vehicles) drink-driving (exceeding 0.15mg by breath test) /L) accident, resulting in the number of deaths and injuries (including all drivers, passengers, and pedestrians affected by the incident).

Highways Statistics: Traffic Accidents: Wounded Victims data was reported at 65,137.000 Unit in 2017. This records an increase from the previous number of 63,120.000 Unit for 2016. Highways Statistics: Traffic Accidents: Wounded Victims data is updated yearly, averaging 36,454.000 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 65,755.000 Unit in 2011 and a record low of 2,884.000 Unit in 1998. Highways Statistics: Traffic Accidents: Wounded Victims data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW008: Highways Statistics: Traffic Accidents. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Group III : Vehicle Involvement and Driver Statistics - Figure 3.11 Motor vehicle drivers involved in accidents by type of driving licence and class of motor vehicle 2018(Simplified Chinese)

Over the past few years, Tesla has expanded its efforts to build fully autonomous road vehicles. This has resulted in them holding the major share of operational cars that are collecting data for autonomous driving use, at a total of 2.7 million. This could be data regarding recorded road accidents or problems with the autopilot system. The second most significant participant in data collection was Xpeng with 270,000 cars. Finally, Waymo, Baidu, Pony and Cruise show significantly smaller fleet sizes collecting data, ranging from 1,000 to as low as 300 cars.

Traffic Accidents: Dead: Quintana Roo data was reported at 4.000 Person in Dec 2017. This records a decrease from the previous number of 8.000 Person for Nov 2017. Traffic Accidents: Dead: Quintana Roo data is updated monthly, averaging 4.000 Person from Jan 2012 (Median) to Dec 2017, with 72 observations. The data reached an all-time high of 14.000 Person in Dec 2015 and a record low of 0.000 Person in Jan 2015. Traffic Accidents: Dead: Quintana Roo data remains active status in CEIC and is reported by Secretary of Communications and Transportations. The data is categorized under Global Database’s Mexico – Table MX.TA011: Traffic Accidents: by State.

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.

Group III : Vehicle Involvement and Driver Statistics - Figure 3.12 Motor vehicle drivers involved in accidents by age and class of motor vehicle 2018(Traditional Chinese)

The number of traffic accident deaths in Germany has decreased considerably in the last decade, compared to the 1990s, and especially the 1960s and 1970s. Around 2,839 deaths were recorded in 2023. Recording these numbers takes on particular significance in the country that is home to the Autobahn and some of the most famous cars in the world. Staying safe The reduction of road traffic deaths in Germany may be due to a number of positive factors with long-term potential: progressive automotive engineering, increased regulation and legislation regarding road traffic and driving, as well as expanded driving safety resources and training. Another reason may simply be less people owning cars, and therefore less drivers being out on the road. Traffic accidents happen for various reasons, be it a bad combination of circumstances or the human factor. Certain choices and consumption habits significantly increase the risk of a car accident, a common example being driving after drinking. Into the future The addition of e-scooters to German city traffic in particular, alongside various vehicles, motorcycles and bikes in often already crowded areas, poses a new challenge for preventing accidents and fatalities. There were over 8,400 injuries recorded in 2023 which were the result of an e-scooter crash. That same year, 21 fatalities also occurred following an e-scooter accident. Increasing mobility and various transportation modes attached to it will definitely keep the issue of road safety on the map.

In 2023, the number of traffic accidents caused by driving under the influence of alcohol amounted to just over 13 thousand cases, a decrease compared to the previous year but still notably lower than in the years prior to that. Drink-driving accidents in South Korea has overall decreased in the last years.

Brazil Highways Statistics: Traffic Accidents: Vehicles Involved data was reported at 200,303.000 Unit in 2017. This records an increase from the previous number of 195,017.000 Unit for 2016. Brazil Highways Statistics: Traffic Accidents: Vehicles Involved data is updated yearly, averaging 112,260.000 Unit from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 211,279.000 Unit in 2012 and a record low of 22,317.000 Unit in 1998. Brazil Highways Statistics: Traffic Accidents: Vehicles Involved data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW008: Highways Statistics: Traffic Accidents. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Annual State-reported licensed driver data from Highway Statistics for the 50 States and DC from Highway Statistics table DL-22.

Taichung City delivery platform delivery personnel traffic accident statistics table

Group III : Vehicle Involvement and Driver Statistics - Figure 3.13 Accident involvement rates of motor vehicle drivers by age and selected class of motor vehicle 2018(Traditional Chinese)

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.

In 2023, the number of deaths caused by traffic accidents amounted to approximately 11,628 cases in Vietnam. This indicated a decrease from the previous year. From 2013 to 2021, the number of traffic deaths has gradually declined, then increased dramatically in 2022, with the number of deaths due to crashes double than that in 2021.

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