Pedestrian deaths in the United States in 2022 peaked among age groups in their 30s and among people in their late 50s and early 60s. The highest number of pedestrian fatalities was among 30 to 34 year-olds and 60 to 64 year-olds, where fatalities reached *** people respectively.

Details of Motor Vehicle Collisions in New York City provided by the Police Department (NYPD).

To describe fatal pedestrian injury patterns in adults 25–64 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using medicolegal death investigations data of MVCs occurring in the current Canadian MV fleet. MVC-pedestrian injuries were collated in an Injury Data Collection Form (IDCF) and coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3–6), that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario database of postmortem examinations done at the Provincial Forensic Pathology Unit in Toronto, Canada and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs, and MV dynamics and pedestrian kinematics. The study was approved by the Western University Health Science Research Ethics Board. There were 318 adults: 200 (62.9%) males and 118 (37.1%) females. Adult pedestrians comprised 47.5% (318/670) of all autopsied pedestrians. Vehicle type was known in 292 cases, and cars (n = 99/292, 33.9%) were the most frequent type of vehicle in single vehicle impacts; however, collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) such as light trucks, heavy trucks and buses were in the majority. Pedestrian kinematics were known in 288/299 single impact-related deaths. Forward projection (n = 113/288, 39.2%) was the most frequent type and resulted from impacts with high hood edge vehicles. Compared to car impacts, pedestrians struck by high hood edge vehicles were more likely to be runover. Based on MAISBR ≥3 injuries, the head was the most severely injured (median MAISBR = 4), followed by neck (median MAISBR = 3), thorax (median MAISBR = 4), abdomen/retroperitoneum (median MAISBR = 4) and pelvis (median MAISBR = 3). About 70% of the pedestrians were in circumstances which increased their risk of being struck. More than half (176/318, 55.3%) had a positive toxicology result. About ¼ (27.4%) had a positive blood ethanol result. Nearly all pedestrians with positive alcohol results did not have the right of way when struck. The current study was a comprehensive analysis of fatal injury patterns and specific injuries in adult pedestrians struck by motor vehicles. By collation and analysis of comprehensive data derived from postmortem examinations, associations between injury patterns in the adult age group were correlated with a range of factors related to motor vehicle types, reflective of the current Canadian fleet, collision dynamics and pedestrian post-collision kinematics.

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.

This is a subset of a larger dataset. This dataset includes pedestrians and cyclists killed in traffic collisions in 2021.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) 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.

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.

Pedestrian road traffic injuries in the United States were particularly high among younger adults in 2022. The highest number of pedestrian injuries were among 25 to 29 year-olds, which totaled 6,130 incidents that year.

This data set maps the locations of crashes involving pedestrians in the Chapel Hill Region of North Carolina.

The data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network, public vehicular areas and private properties (if reported) from January 2007 through December 2013. Users are able to click and view information specific to each crash. Information for each crash includes: County, City, Crash Date, Crash Day, Crash Group, Crash Location, Crash Time, Crash Severity, Bike/Pedestrian Age Group, Bike/Pedestrian Alcohol Detected, Bike Direction, Bike/Pedestrian Injury, Bike/Pedestrian Position, Bike/Pedestrian Race, Bike/Pedestrian Sex, Ambulance Response, Driver Age Group, Driver Estimated Speed, Speed Limit, Driver Alcohol Detected, Driver Injury, Driver Race, Driver Sex, Driver Vehicle Type, Hit and Run, Development, Light Condition, Locality, Number of Lanes, Road Characteristics/Class/Condition/Configuration, Road Defects/Features, Traffic Control, Crash Type, and/or Weather. Crash identification numbers have been removed from the data for protection of privacy. Crash records were obtained NCDOT’s Traffic Engineering Accident Analysis System (TEAAS).

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.

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).

Data Dictionary

Column Name	Meaning
CRASH DATE	Date of the crash incident (day)
CRASH TIME	Time of the crash incident (hour and minute)
BOROUGH	Borough in which the crash occurred
ZIP CODE	Postal code of the crash location
LATITUDE	Latitude coordinates of the crash location
LONGITUDE	Longitude coordinates of the crash location
LOCATION	Geographical location description of the crash
ON STREET NAME	Name of the street where the crash occurred
CROSS STREET NAME	Name of the cross street where the crash occurred
NUMBER OF PERSONS INJURED	Total number of persons injured in the crash
NUMBER OF PERSONS KILLED	Total number of persons killed in the crash
NUMBER OF PEDESTRIANS INJURED	Number of pedestrians injured in the crash
NUMBER OF PEDESTRIANS KILLED	Number of pedestrians killed in the crash
NUMBER OF CYCLIST INJURED	Number of cyclists injured in the crash
NUMBER OF CYCLIST KILLED	Number of cyclists killed in the crash
NUMBER OF MOTORIST INJURED	Number of motorists (vehicle occupants) injured in the crash
NUMBER OF MOTORIST KILLED	Number of motorists (vehicle occupants) killed in the crash
CONTRIBUTING FACTOR VEHICLE 1	Primary contributing factor for the crash (vehicle 1)
CONTRIBUTING FACTOR VEHICLE 2	Secondary contributing factor for the crash (vehicle 2)
COLLISION_ID	Unique identifier for the collision incident
VEHICLE TYPE CODE 1	Type of vehicle involved in the crash (vehicle 1)
VEHICLE TYPE CODE 2	Type of vehicle involved in the crash (vehicle 2)

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.

In Australia in the the year ending July 2023, there were a total of *** road deaths involving pedestrians. This represents an increase from the previous year.

ABSTRACTObjective:to compare the frequency and the severity of diagnosed injuries between pedestrians struck by motor vehicles and victims of other blunt trauma mechanisms.Methods:retrospective analysis of data from the Trauma Registry, including adult blunt trauma patients admitted from 2008 to 2010. We reviewed the mechanism of trauma, vital signs on admission and the injuries identified. Severity stratification was carried using RTS, AIS-90, ISS e TRISS. Patients were assigned into group A (pedestrians struck by motor vehicle) or B (victims of other mechanisms of blunt trauma). Variables were compared between groups. We considered p<0.05 as significant.Results:a total of 5785 cases were included, and 1217 (21,0%) of which were in group A. Pedestrians struck by vehicles presented (p<0.05) higher mean age, mean heart rate upon admission, mean ISS and mean AIS in head, thorax, abdomen and extremities, as well as lower mean Glasgow coma scale, arterial blood pressure upon admission, RTS and TRISS. They also had a higher frequency of epidural hematomas, subdural hematomas, subarachnoid hemorrhage, brain swelling, cerebral contusions, costal fractures, pneumothorax, flail chest, pulmonary contusions, as well as pelvic, superior limbs and inferior limbs fractures.Conclusion:pedestrian struck by vehicles sustained intracranial, thoracic, abdominal and extremity injuries more frequently than victims of other blunt trauma mechanism as a group. They also presented worse physiologic and anatomic severity of the trauma.

The Motor Vehicle Collisions person table contains details for people involved in the crash. Each row represents a person (driver, occupant, pedestrian, bicyclist,..) 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.

The graph displays the number of car accident fatalities by type in the United States from 2010 to 2022. The x-axis represents the years, labeled from '10 to '22, while the y-axis indicates the number of fatalities. Each year includes data points for four categories: Passenger Vehicle, Pedestrian, Two-Wheeled Vehicle, and Large Truck fatalities. Passenger Vehicle fatalities range from a low of 21,076 in 2014 to a high of 26,650 in 2021. Pedestrian fatalities increase from 4,300 in 2010 to a peak of 7,467 in 2022. Two-Wheeled Vehicle fatalities vary between 5,022 in 2014 and 7,287 in 2022. Large Truck fatalities are the lowest among the categories, ranging from 346 in 2010 to 533 in 2022. The data reveals an overall upward trend in fatalities across all categories, particularly notable in the years 2021 and 2022.

List of crashes involving a motorist(s) reported to the City of Cambridge. The log also lists crashes involving a motorist(s) and a bicyclist(s) and/or a pedestrian(s). Any data or text that may identify specific persons or companies has been omitted. Data are updated most days. A more detailed dataset, updated quarterly, can be found here: https://data.cambridgema.gov/Public-Safety/Police-Department-Crash-Data-Updated/gb5w-yva3

Entries involving no motorists, bicyclists or pedestrians are most commonly hit and run crashes involving unoccupied vehicle(s).

The Measurement template document is available at the archived version of this page on the UK Government Web Archive.

DfT Business plan

Geographical coverage: Great Britain

Information broken down by: Accident site. Data are available by geographical area, age, gender and type of road user.

Latest data

In 2013:

• 1,713 people were killed in reported road traffic accidents in Great Britain, 2% (41) fewer than in 2012. This is the lowest number of fatalities since national records began in 1926. The total number of people killed in 2013 was 39% lower than the 2005-09 baseline average
• the number of fatalities decreased for pedestrians, pedal cyclists and car occupants, by 5%, 8% and 2% respectively, but increased for motorcycle users by 1%. Over the same period motor vehicle traffic remained broadly stable, with a small increase of 0.4% between 2012 and 2013
• with the exception of 2011, road deaths have fallen every year since 2004. Adverse weather (heavy snow falls) experienced in the first and last quarters of 2010 but not in 2011, is likely to be the main factor behind the increase in fatalities recorded in 2011

Year	Road accident fatalities	% change from previous year
2000	3,409	-0.4
2001	3,450	1.2
2002	3,431	-0.6
2003	3,508	2.2
2004	3,221	-8.2
2005	3,201	-0.6
2006	3,175	-0.9
2007	2,946	-7.1
2008	2,538	-13.8
2009	2,222	-12.5
2010	1,850	-16.7
2011	1,901	2.8
2012	1,754	-7.7
2013	1,713	-2.3

The complete set of data is available for download.

Background information

The indicator can be broken down by any geographical area (eg country, region, local authority) since a grid reference is collected for each accident. Information is also available by age, gender, type of road user and road type. Numbers will be relatively small for more detailed breakdowns of the total and may therefore fluctuate from year to year. This needs to be taken into account when assessing trends.

• publishing schedule: annual
• last updated: September 2014
• next update: July 2015

Other related data and information

More detailed analysis and time series can be found in Reported road casualties Great Britain: annual report.

Record level data on accidents and casualties can be found in http://data.gov.uk/dataset/road-accidents-safety-data/">Record level data

Further information

• Business Plan 2012 to 2015
• Input and impact indicators
• Analyst contact: Anil Bhagat, roadacc.stats@dft.gsi.gov.uk

The dataset contains year- and state-wise compiled data on the number of pedestrians killed in road accidents by types of impacting vehicles or the vehicles which have caused accidents and resulted in the death of pedestrians. The different types of vehicles covered in the dataset include bicycle, two wheeler, auto rickshaw, car, taxi, van and lMV, truck/lorry, bus, other motorized vehicles such as e-rickshaw, etc.

During 2023, the Crime Commission examined motor vehicle traffic crash fatalities involving drivers, passengers, pedestrians, and bicyclists. Analysis of Virginia motor vehicle traffic crash fatality data between 2017 and 2022 revealed: • 5,309 individuals were killed in motor vehicle traffic crashes during this time period, which included 4,464 (84%) drivers or passengers, 771 (15%) pedestrians, and 74 (1%) bicyclists. • The number of driver, passenger, and pedestrian fatalities increased significantly between 2020 and 2022 (725 driver/passenger fatalities in 2020 to 823 fatalities in 2022; 114 pedestrian fatalities in 2020 to 171 fatalities in 2022); whereas, bicyclist fatalities remained consistently low across the entire time period. • The number of crashes between 2020 and 2022 remained below pre-2020 levels, while the number of fatalities increased by 19% during that same time period. • The causal factors accounting for the rise in fatalities varied, with a 22% increase in unrestrained fatalities, 39% in speed-related fatalities, and 10% increase in alcoholrelated fatalities in 2022 as compared to 2017.

This data comes from police-reported bicycle-motor vehicle and pedestrian-motor vehicle collisions that occurred on the public roadway network from January 2007 through December 2021. This data does not contain crashes occurring off the roadway, such as in parking lots, driveways, private roads, yards, alleys, and other open areas.

Although effort was made to code locations with a high degree of accuracy and using all available information from the crash report, users should be aware that the accuracy and precision of these data are affected by the accuracy and level of precision in crash reporting as well as errors that may enter the data during data entry and coding. For example, crash locations are often specified to only the nearest 0.10 mile. It is further likely that many of the locations in crash data are estimated rather than resulting from an actual measurement at the crash scene. Injured cyclists may also be transported for medical treatment prior to investigating officers’ arrival on the scene, reducing their input regarding crash location. Locating crashes also sometimes requires interpreting conflicting information from the crash report to make an assessment of the most probable location. Coders with local knowledge may potentially be able to locate some of the crashes that could not be located in this Statewide effort. In order not to lose information on locations of crashes, some, more often rural, crashes were located to the most probable “on road” in relationship to one or more cross streets but actual distances and greater precision may be lacking.

Original Data

Acknowlegement

Foto von Ian Valerio auf Unsplash