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.

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.

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.

Analysis of ‘Traffic Crashes - Vision Zero Chicago Traffic Fatalities’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8394b076-2e6a-4686-a123-13dcfef7b0af on 12 February 2022.

--- Dataset description provided by original source is as follows ---

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 City’s multi-departmental Fatal Crash Response Coordination Committee (FCRCC) that reviews fatal traffic crashes provided by Chicago Police Department’s Major Accident Investigation Unit (MAIU).

This committee uses a standardized process – sometimes differing from other sources and everyday use of the term -- to determine whether a death is a “traffic fatality” for VZC purposes. 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 a yearly basis. The traffic fatality list determined on an ongoing basis through the year may differ from IDOT’s official crash data for Chicago as IDOT may define the cause and location differently from the FCRCC. Once IDOT publishes its data for a year, crashes in this dataset for that year are edited to match IDOT’s determinations, unless the FCRCC disagrees with the IDOT determination – which happens only rarely and usually due to an interpretation of one of the criteria below.

VZC Traffic Fatalities can be linked with other traffic crash datasets using the “RD_NO” or “Person_ID” fields.

The FCRCC defines a “traffic fatality” for the purpose of VZC statistics as "any death caused by a traffic crash, within 30 days of the crash” and “involves a motor vehicle.” Fatalities that meet the VZC definition of a traffic fatality are included in this dataset unless excluded by the 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" for VZC purposes:

1. 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 where the fatality was not attributable to road user movement on a public roadway. (Note: If a person driving suffers a medical emergency and consequently hit and kills another road user, the other road user is included although the driver suffering a medical emergency is excluded.)

1. The crash did not occur within the public right-of-way.

2. The crash involved a train or such mode of transport within their dedicated right-of-way.

3. 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 Vision Zero Chicago Traffic Fatalities.

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 center line.

1. The fatality is not for a person (e.g., an animal).

--- Original source retains full ownership of the source dataset ---

Overview:

Information on location and characteristics of crashes in Queensland for all reported Road Traffic Crashes occurred from 1 January 2001 to 30 June 2024.

Fatal, Hospitalisation, Medical treatment and Minor injury:

This dataset contains information on crashes reported to the police which resulted from the movement of at least 1 road vehicle on a road or road related area. Crashes listed in this resource have occurred on a public road and meet one of the following criteria:

• a person is killed or injured, or
• at least 1 vehicle was towed away, or
• the value of the property damage meets the appropriate criteria listed below.

Property damage:

1. $2500 or more damage to property other than vehicles (after 1 December 1999)
2. $2500 or more damage to vehicle and/or other property (after 1 December 1991 and before 1 December 1999)
3. value of property damage is greater than $1000 (before December 1991).

Please note:

• This data has been extracted from the Queensland Road Crash Database.
• Information held in the Road Crash Database on events occurring within the last 12 months is considered preliminary as investigations into crashes can take up to 1 year to finalise.
• Property damage only crashes ceased to be reported/recorded by Queensland Police Service after 31 December 2010.
• These crash location coordinates reference the current Australian geodetic datum is GDA2020 (previously it was GDA94).

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.

This dataset includes accidents with Google, Uber, Tesla, and Waymo autonomous cars. Accordingly, the inventory of accidents involvs highly automated vehicles. The database comprises 40 accidents from all over the world, which occurred between 2016 and 2021, and consists of the following fields: • year: year of accident 1..2100; • month: month of accidents (1..12); • day: day of the accident (1..31) • hour: hour of the accident (0:00..23:59) • period of the day: hour of the accident (0,00..23,59) • country of the accident: e.g. USA, China, etc. • GPS coordinates: e.g. 39°18′N 116°42′E • state: e.g. Florida • state: e.g. Florida • description: e.g. 23-year-old Gao Yuning was killed when his Tesla, with Autopilot mode engaged, slammed into the back of a stationary road sweeping truck parked at the edge of the road. • death: number of fatal injuries of the accident • • Serious injury: number of seriously injured persons related to the accident • slight injury: number of slight injury related to the accident • Uber driver: number of Uber driver involved • Total number of vehicles: number of vehicles involved • Environment: flat, elevating, mountain • Environment code: flat-1, elevating-2, mountain-3 • Period of the day :Day, evening, night • visibility: 1 clear, 0 not clear • Weather condition: rainy, snow, sunny, haze • season: summer-1, spring-2, autumn-3, winter-4 • season rate: summer-1/4, spring-2/4, autumn-3/4, winter-4/4 • speed limit: the regular speed limit at the location of the accident • speed condition of highly automated vehicle: the actual velocity of the investigated highly automated vehicle • Normalization of speed: normalized value of speed condition • Model of highly automated vehicle: the name of the model of the investigated highly automated vehicle • Autopilot mode:yes-1, no-0 • Age of highly automated vehicle: number of years from manufacturing the highly automated vehicle • Type of accident: frontal, rear-end collision, sideswipe collisions, chain-reaction collision • curvature: straight, in curve • Total number of vehicles: number of vehicles involved • Technical reasons: brief description, introducing the causes • Sources: web link

The main source of the crash data is owned and maintained by the Virginia Department of Motor Vehicle (DMV). DMV’s Traffic Records Electronic Data System (TREDS) is a state-of-the-art data system maintained by the DMV Highway Safety Office (HSO) that automates and centralizes all crash data in Virginia. Per data sharing use agreement with DMV, VDOT publishes the non-privileged crash data through Virginia Roads data portal. In providing this data, VDOT assumes no responsibility for the accuracy and completeness of the data. In the process of recording and compiling the data, some deletions and/or omissions of data may occur and VDOT is not responsible for any such occurrences. The most recent data contained in this dataset is preliminary and subject to change.

Please be advised that, under Title 23 United State Code – Section 407, this crash information cannot be used in discovery or as evidence in a Federal or State court proceeding or considered for other purposes in any action for damages against VDOT or the State of Virginia arising from any occurrence at the location identified.

All users shall comply with and be subject to all applicable laws and regulations, whether federal or state, in connection with any of the receipt and use of DMV data including, but not limited to, (1) the Federal Drivers Privacy Protection Act (18 U.S.C. § 2721 et seq.), (2) the Government Data Collection and Dissemination Practices Act (Va. Code § 2.2-3800 et seq.), (3) the Virginia Computer Crimes Act (Va. Code § 18.2-152.1 et seq.), (4) the provisions of Va. Code §§ 46.2-208 and 58.1-3, and (5) any successor rules, regulations, or guidelines adopted by DMV with regard to disclosure or dissemination of any information obtained from DMV records or files.

Details about traffic accidents reported to Metro Nashville Police Department. This dataset is updated daily.Source Link: https://www.nashville.gov/departments/police/support-services/traffic-divisionMetadata Document: Traffic Accidents Metadata.pdfContact Data Owner: opendata@nashville.gov

Road traffic accidents are accidents in which people are killed or injured or property is damaged as a result of driving on public roads and squares. The accident atlas contains accidents involving personal injury. Accidents that only result in property damage are not shown. The accident atlas contains information from the statistics of road traffic accidents based on reports from the police stations. The published CSV data was treated as follows: * - Only the accidents in the city of Konstanz were taken into account. * - A unique ID number was created for each accident. * - The historical data for the years 2016 to 2019 have been aggregated. * - The variable "IstStreet" was renamed to "FAULT STATE" after 2017. For consistency, both have been labeled "FAULT" for the entire period. * - The measurements are the same (0, 1, 2). * - The "LIGHT" variable was renamed to "ULICHTVERH" after 2017. For consistency, both were labeled "ULICHTVERH" for the entire period. The measurements are the same (0, 1, 2) * - The complete data set was also subdivided into 6 more for the years 2016 to 2019, each containing accidents involving only bicycles, passenger cars, pedestrians, motorcycles, goods vehicles and others. * - Year and month have been combined in an additional column to facilitate time series comparisons. Variables: * - AccidentID: unique number for each accident * - year-month: year and month combined 2016-1 * - UJAHR: accident year * - UMONAT: accident month * - HOURS: accident hour * - UWEEKDAY: Day of the week (1 = Sunday 2 = Monday 3 = Tuesday 4 = Wednesday 5 = Thursday 6 = Friday 7 = Saturday) * - UK CATEGORIES: Accident categories (criterion for allocation is the most serious consequence of the accident) 1 = accident with fatalities 2 = accident with serious injuries 3 = accident with minor injuries UART: Accident type 1 = collision with approaching/stopping/stationary vehicle 2 = collision with preceding/waiting vehicle 3 = collision with sideways vehicle moving in the same direction 4 = collision with oncoming vehicle 5 = Collision with turning / crossing vehicle 6 = Collision between vehicle and pedestrian 7 = Impact with lane obstacle 8 = Lane departure to the right 9 = Lane departure to the left 0 = Other type of accident * - UART: Type of accident 1 = Collision with approaching/stopping /stationary vehicle 2 = collision with vehicle ahead/waiting 3 = collision with vehicle traveling sideways in the same direction 4 = collision with oncoming vehicle 5 = collision with turning / crossing vehicle 6 = collision between vehicle and pedestrian 7 = collision with roadway obstacle 8 = departure from lane to the right 9 = departure from lane to the left 0 = accident of a different kind * - UTYP1: Accident type 1 = driving accident 2 = turning accident 3 = turning / crossing accident 4 = crossing accident 5 = accident caused by stationary traffic 6 = accident in parallel traffic 7 = other accident * - LIGHT CONDITIONS: lighting conditions 0 = daylight 1 = twilight 2 = darkness * - IstRad: accident in which at least one bicycle was involved 0 = accident without bicycle involvement 1 = accident with bicycle involvement * - IstPKW Accident with car: Accident in which at least one passenger car was involved 0 = Accident without car involvement 1 = Accident with car involvement * - IstFuss Accident with pedestrian: Accident in which at least one pedestrian was involved 0 = Accident without Pedestrian participation 1 = accident involving pedestrians * - IstKrad Accident involving a motorcycle: Accident involving at least one motorcycle, e.g. B. moped, motorcycle/scooter was involved 0 = accident without motorcycle participation 1 = accident with motorcycle participation * - IstGkfz: Accident with goods vehicle (GKFZ): Accident involving at least one truck with normal body and a total weight of more than 3.5 t truck with tank support or special body, a tractor unit or another tractor unit was involved (this category is included in "Accident with other" in 2016 and 2017) 0 = accident without goods vehicle involvement 1 = accident with goods vehicle involvement * - ActualOther: Accident with other: accident involving at least one means of transport not mentioned above, e.g. B. a bus or a tram (2016 and 2017 inclusive) accident with goods vehicle (GKFZ), from 2018 without accident with GKFZ) 0 = accident without involving a means of transport not mentioned above 1 = accident involving a means of transport not mentioned above * - LINREFX and LINREFY : Graphical coordinate 1 and graphic coordinate 2LINREFX and LINREFY form the coordinate of the accident location on the road section (UTM coordinate of the reference system ETRS89, zone 32N). XGCSWGS84 and YGCSWGS84: Graphic coordinate 1 and graphic coordinate 2 XGCSWGS84 and YGCSWGS84 form the coordinate of the accident site on the road section (coordinate of the reference system GK 3) For further explanations see destatis.de (Source: Accident Atlas of the Federal and State Statistical Offices - Open Data) ### Data source : Open Data Konstanz under DL-DE/BY 2.0

In 2022, the number of deaths due to road accidents in India among victims between 25 to 35 years amounted to nearly **** thousand, the most compared to other age groups. That year, there were over 169 thousand accidental fatalities across the south Asian country. Over-speeding was the leading contributor of accidents. Combined, state and national highways recorded around 258 thousand road accidents in 2022. This number had dropped significantly in 2016, before increasing again in recent years.

Accident demographics

The Indian subcontinent ranked first in terms of road accident deaths according to the World Road Statistics which comprised of *** countries. A majority of victims were two-wheeler commuters. Additionally, pedestrians made up a high share of victims as well, reflecting the lack of infrastructure, be it improper footpaths and the lack of foot-over bridges or negligence of traffic rules. About ** percent of the road accidents in India accounted for about *** percent of the global road traffic accidents.

Accident prevention

Poor enforcement of fines, in addition to mild punishments and corruption encourages drivers, especially among young Indians, to engage in rash driving. Accident awareness programs were initiated by the government among the motorists, along with the National Road Safety Policy to encourage safe transport, strict enforcement of safety laws and fines and establishment of road safety database.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_78ba40f0eed52ff007bccb81ee6372ed/view

This data set contains Somerville crashes that occurred from May 2018 to present. Crash reports are completed when a motor vehicle crash occurs on a public way and involves at least one of the following: Any person is killed, any person is injured, or damage is in excess of $1,000 to any one vehicle or other property. Data does not include crashes that are under active investigation, nor those that occur on state roads, which are under the jurisdiction of the Massachusetts State Police. State crash data may be accessed on the Massachusetts Department of Transportation’s crash data portal, IMPACT.

This data set should be refreshed daily with data appearing with a one-month delay (e.g. crashes that occurred from 1/1 will appear on 2/1). If a daily update does not refresh, please email data@somervillema.gov.

New Zealand NZ: Road Fatalities: Per One Million Inhabitants data was reported at 6.529 Ratio in 2023. This records a decrease from the previous number of 7.270 Ratio for 2022. New Zealand NZ: Road Fatalities: Per One Million Inhabitants data is updated yearly, averaging 8.772 Ratio from Dec 1994 (Median) to 2023, with 30 observations. The data reached an all-time high of 16.022 Ratio in 1994 and a record low of 5.696 Ratio in 2013. New Zealand NZ: Road Fatalities: Per One Million Inhabitants data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s New Zealand – Table NZ.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made.

Context

This data set contains accidents registered by the C4, a Mexican system that registers all traffic incidents.

Content

The data set has the following columns:

1. folio: a unique ID for each register
2. fecha_creacion: creation date
3. hora_creacion: creation time
4. dia_semana: day of the week when incident happens
5. codigo_cierre: internal classification. The column could contain the following codes.
6. A: Affirmative, if the incident is confirmed by emergencies team.
7. N: Negative, if the emergencies team doesn't confirm the incident at the location point.
8. I: Informative, in case attention teams want to add extra information.
9. F: False, if initial report doesn't match with the real events
10. D: Duplicated, records with closing code affirmative, negative or false but operators identify them
11. fecha_cierre: close date, the date when the incident was resolved
12. año_cierre: close year
13. mes_cierre: close month
14. hora_cierre: close time
15. delegacion_inicio: entity inside Mexico City where the incident was registered
16. incidente_c4: a brief explanation about the incident.
17. latitud: accident latitude
18. longitud: accident longitude
19. clas_con_f_alarma: code identifying the situation's severity
20. tipo_entrada: how the incident was reported
21. delegacion_cierre: entity inside Mexico City where the incident was closed
22. geopoint: latitude and longitude columns combined
23. mes: month when the incident was reported

Additional Note: To properly use and interpret the information, must consider those registers with closing codes Affirmative and Informative, these are real incidents.

Acknowledgements

All files were downloaded from here The Mexico City web page containing open data about traffic incidents.

***The Los Angeles Police Department (LAPD) has transitioned traffic collision reporting to our new Records Management System (RMS) as part of our ongoing efforts to modernize data collection and comply with the FBI’s National Incident-Based Reporting System (NIBRS). This transition will improve the accuracy and detail of reported traffic-related incidents.

During this process, there will be a delay in the availability of new traffic collision datasets while they are being developed for the new system. In the meantime, users will continue to see only historical data from the retired system. We appreciate your patience as we complete this transition. ***

This dataset reflects traffic collision incidents in the City of Los Angeles dating back to 2010. This data is transcribed from original traffic reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

The data set contains information collected from road traffic injury victims. The data was collected from all traffic injury victims, regardless of age and sex except those who were dead on arrival, comatose, and had no attendant. The variables included in this data includes, crash characteristics, hospital arrival time, road user category, availability of pre-hospital first aid, type of transportation used to transfer the victim, clinical findings, the outcome in the emergency department, and decision after evaluation at the emergency department.

 The primary outcome was time to death measured in hours between road traffic injury and the 30th day of injury. Accordingly, those victims who died between injury times to the 30th day of injury were events, and those who were still alive on the 30th day were censored cases. Secondary outcomes were pre-hospital first aid, length of hospital stay, and hospital arrival time. The exposure variable was having any degree of injury by any vehi...

List of collisions that have occurred in Montreal since 2012. This set shows collisions involving at least one motor vehicle traveling on the network and which were the subject of a police report. It includes descriptive, contextual elements and the location of the events, including the severity expressed in deaths, serious injuries, minor injuries and property damage only. ### IMPORTANT: This set is a subset of the one that was posted online by the Société de l'assurance automobile du Québec (SAAQ) on Data Québec before December 2023. It contains all the collisions identified on the territory of Montreal, including those with property damage only (DMS) of less than $2,000 recorded by the Service de Police de la Ville de Montréal (SPVM), with a geolocation compiled by the City for analysis purposes. The data recorded for the whole city only shows collisions that occurred on the road network under the trust of the agglomeration of Montreal. Collisions that occurred on the highway network are excluded from this dataset. Since the revision of the data by the SAAQ in December 2023, the contents of the 2 platforms differ for the period 2012-2021. For all collisions subsequent to 2021, it is possible to consult the data put online by the Société de l'assurance automobile du Québec (SAAQ) on Data Quebec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Indonesia Number of Road Accident: Killed: Bali data was reported at 521.000 Person in 2017. This records an increase from the previous number of 461.000 Person for 2016. Indonesia Number of Road Accident: Killed: Bali data is updated yearly, averaging 544.000 Person from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 739.000 Person in 2014 and a record low of 262.000 Person in 2003. Indonesia Number of Road Accident: Killed: Bali data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Transport and Telecommunication Sector – Table ID.TA005: Number of Road Accident.