Data analysis worksheets and average crash rates by intersection type and roadway functional classification.

The Connecticut Crash Data Repository (CTCDR) is a web tool designed to provide access to select crash information collected by state and local police. This data repository enables users to query, analyze and print/export the data for research and informational purposes. The CTCDR is comprised of crash data from two separate sources; The Department of Public Safety (DPS) and The Connecticut Department of Transportation (CTDOT). The purpose of the CTCDR is to provide members of the traffic-safety community with timely, accurate, complete and uniform crash data. The CTCDR allows for complex queries of both datasets such as, by date, route, route class, collision type, injury severity, etc. For further analysis, this data can be summarized by user-defined categories to help identify trends or patterns in the crash data.

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.

With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. Methods: All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.

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.

AV crash data variables.

Contains data on large trucks and buses involved in Federally reportable crashes as per Title 49 U.S.C. Part 390.5 (crashes involving a commercial motor vehicle, and that either involve a fatalities, injury requiring treatmentaway from the scene of the crash, or a tow-away due to disabling damage). This information is reported by the States to FMCSA.

The program collects data for analysis of traffic safety crashes to identify problems, and evaluate countermeasures leading to reducing injuries and property damage resulting from motor vehicle crashes. The FARS dataset contains descriptions, in standard format, of each fatal crash reported. To qualify for inclusion, a crash must involve a motor vehicle traveling a traffic-way customarily open to the public and resulting in the death of a person (occupant of a vehicle or a non-motorist) within 30 days of the crash. Each crash has more than 100 coded data elements that characterize the crash, the vehicles, and the people involved. The specific data elements may be changed slightly each year to conform to the changing user needs, vehicle characteristics and highway safety emphasis areas. The type of information that FARS, a major application, processes is therefore motor vehicle crash data.

See our: Crash Analysis System (CAS) data user guide

This data comes from the Waka Kotahi Crash Analysis System (CAS), which records all traffic crashes reported to us by the NZ Police. CAS covers crashes on all New Zealand roadways or places where the public have legal access with a motor vehicle.

The data updates monthly, in the first week of each month.

Data is currently available from 1 January 2000. The dataset includes crash variables that are non-personal data.

To give you a quick overview of the data, see the charts in the ‘Attributes’ section below. These will give you information about each of the attributes (variables) in the dataset.

Each chart is specific to a variable, and shows all data (without any filters applied).

Crash Analysis System data - field descriptions

Data reuse caveats: we’ve taken reasonable care in compiling this information, and provide it on an ‘as is, where is’ basis. We're not liable for any action taken on the basis of the information. For further information see the terms of the CC-BY 4.0 International license.

CC-BY 4.0 International licence details

Variables in the dataset are formatted for analytical use. This can result in attribute charts that may not appear meaningful, and are not suitable for broader analysis or use. In addition, some variables aren't mutually exclusive – do not consider them in isolation.

You must not take and use these charts directly as analysis of the overall data.

Data quality statement: we aim to process all fatal crashes within one working day of receiving the crash report from NZ Police.

We aim to process all injury crashes (serious and minor injury) within 4 weeks of receiving the crash report.

It may take up to seven months for non-injury crashes to be processed into CAS.

Up-to-date information on current number of outstanding crash reports

Most unprocessed crash reports will be for crashes where there weren’t any injuries.

Data quality caveats: this data comes from the road traffic crash database Crash Analysis System (CAS) version 2.1.0. As the data is live, data can sometimes change after we receive it – that is, the data is not static after we publish it.

Waka Kotahi NZ Transport Agency maintains the Crash Analysis System. This open data is an appropriately confidentialised version of that.

After a crash, NZ Police send us a Traffic Crash Report (TCR). This may not happen immediately.

A crash must have happened on a road to be recorded in CAS. The CAS definition of a road is any street, motorway or beach, or a place that people can access with a motor vehicle.

There is a lag between the time of a crash to CAS having full and correct crash records. This is due to the police reporting time frame, and data processing.

People don’t report all crashes to the NZ Police. The level of reporting increases with the severity of the crash.

Crash severity is the severity of the worst injury in the crash. There may be more than one injury in a crash.

2020 and 2021 data is incomplete.

For API explorer users, there is a known issue with number-based attribute filters where the “AND” operator is used instead of the “BETWEEN” operator. Substituting “BETWEEN” for “AND” manually in the query URL will resolve this.Update 13/07/2021: previously, there was a 5 month buffer between our internal CAS data and our CAS open data. We have reduced this buffer to 1 month, due to user demand and improved systems.Update 10/12/2020: field type change. The field type for ‘crashFinancialYear’ has changed from integer to text.

TITLE: Motor Vehicle Crashes, New Mexico, 2020- NMCRASHDATA2020

SUMMARY: All motor vehicle crashes locations in New Mexico updated for the year 2020, with information about injuries and other characteristics.

SOURCE: NM Department of Transportation; geocoded by NMDOT and UNM TRU

NOTE: POINT FILE. N=45,915; Geocoded by NMDOT-TRU

FEATURE SERVICE: https://nmcdc.maps.arcgis.com/home/item.html?id=5d9a0e1e56ec4b60bc115f9fdbf26c09

PREPARED BY: M.A. SEELEY, NMCDC

2020

VARIABLE DEFINITION

UCRnumber CRASH REPORT NUMBER

CrashDate CRASH DATE

Year CRASH YEAR

Month MONTH

CrashTime TIME OF CRASH

Hour HOUR OF CRASH

Day DAY OF WEEK

Agency LAW ENFORCEMENT AGENCY

County COUNTY

City CITY

AStreet PRIMARY STREET

Bstreet SECONDARY STREET

Landmark LANDMARK/LOCATION

GIS_Route GIS-DERIVED ROUTE NAME

GIS_Milepo GIS-DERIVED MILEPOST

Dir CRASH DIRECTION

Ldir DIRECTION FROM INTERSECTION OR LANDMARK

Distance DISTANCE FROM LANDMARK

Measure DISTANCE FROM LANDMARK MEASUREMENT UNIT

Severity CRASH SEVERITY

Killed NUMBER OF PEOPLE KILLED IN CRASH

ClassA NUMBER OF PEOPLE WITH SUSPECTED SERIOUS INJURIES (CLASS A) IN CRASH

ClassB NUMBER OF PEOPLE WITH SUSPECTED MINOR INJURIES (CLASS B) IN CRASH

ClassC NUMBER OF PEOPLE WITH POSSIBLE INJURIES (CLASS C) IN CRASH

TOTINJFAT NUMBER OF PEOPLE INJURED (CLASS A+B+C) IN CRASH

Unhurt NUMBER OF PEOPLE NOT INJURED (CLASS O) IN CRASH

Total TOTAL NUMBER OF PEOPLE IN CRASH

nVeh NUMBER OF VEHICLES, BICYCLES, AND PEDESTRIANS INVOLVED

PeopleMV NUMBER OF PEOPLE IN MOTOR VEHICLES

NoPeopleMV NUMBER OF PEOPLE NOT IN MOTOR VEHICLES

Mvinv NUMBER OF MOTOR VEHICLES INVOLVED

HarmOcc FIRST HARMFUL EVENT OCCURRED

Class CRASH CLASSIFICATION

Analysis CRASH ANALYSIS

HarmEvent FIRST HARMFUL EVENT

HarmAnalys FIRST HARMFUL EVENT - ANALYSIS

1HarmLoc FIRST HARMFUL EVENT – LOCATION

1HarmImpac FIRST HARMFUL EVENT – MANNER OF IMPACT

1HarmCrash FIRST HARMFUL EVENT – MANNER OF CRASH

Weather WEATHER

AddWeather ADDITIONAL WEATHER

LIGHTING LIGHTING

HitRun HIT AND RUN CRASH

ALCinv ALCOHOL INVOLVEMENT

DRUGinv DRUG INVOLVEMENT

PEDinv PEDESTRIAN INVOLVEMENT

MCinv MOTORCYCLE INVOLVEMENT

PECinv PEDALCYCLE INVOLVEMENT

TRKinv HEAVY TRUCK INVOLVEMENT

COMMinv COMMERICAL MOTOR VEHICLE INVOLVEMENT

SCHBUSinv SCHOOL BUS DIRECT INVOLVEMENT

HAZMATinv HAZARDOUS MATERIAL INVOLVEMENT

NONLOCinv INVOLVEMENT OF NON-LOCAL DRIVER

STHWYprop STATE HIGHWAY DEPT. PROPERTY

RoadSystem ROAD SYSTEM: URBAN, RURAL OR RURAL INTERSTATE

MaxDam MAXIMUM VEHICLE DAMAGE

WorkZone WORK ZONE

WRKZNtype WORK ZONE - TYPE

WRKZNloc WORK ZONE – LOCATION

RoadCharac ROAD CHARACTER

RoadGrade ROAD GRADE

Intersect INTERSECTION TYPE

Relation RELATION TO JUNCTION

Secondary SECONDARY CRASH

Tribal TRIBAL JURISDICTION

GIS_Reserv GIS-DERIVED RESERVATION

GIS_STHWY GIS-DERIVED STATE HIGHWAY TRANSPORTATION DISTRICT

GIS_STPol GIS-DERIVED STATE POLICE DISTRICT

GIS_HWMain GIS-DERIVED STATE HIGHWAY MAINTENANCE DISTRICT

GIS_UTMX GIS-DERIVED UTM X COORDINATE

GIS_UTMY GIS-DERIVED UTM Y COORDINATE

GIS_Lat GIS-DERIVED LATITUDE COORDINATE

GIS_Long GIS-DERIVED LONGITUDE COORDINATE

ORIGLat ORIGINAL LATITUDE

ORIGLong ORIGINAL LONGITUDE

UCRorig ORIGINAL UCR NUMBER

CaseNumber CASE NUMBER

StationRep STATION REPORT

TRACSdata TRACS DATA

Model accuracy of AV crash severity classification tree.

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

General vehicle-specific data from the prior 10 years. Data compiled in this format for the Traffic Safety Data and Analysis website (www.iowadot.gov/tsda). Metadata available here.

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

• Eurostat [3]
• Directorate-General for Mobility and Transport (DG MOVE). European Union [4]
• The World Bank [5]
• World Health Organization (WHO) [6]
• European Transport Safety Council (ETSC) [7]
• European Road Safety Observatory (ERSO) [8]
• European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]
• EU BestPoint-Project [10]
• Ministerstvo dopravy, República Checa [11]
• Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]
• Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]
• National Statistics Office, Malta [14]
• Ministério da Economia e Transição Digital, Portugal [15]
• Ministerio de Fomento, España [16]
• Trafikverket, Suecia [17]
• Ministère de l’environnement de l’énergie et de la mer, Francia [18]
• Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]
• Statistisk sentralbyrå, Noruega [26-29]
• Instituto Nacional de Estatística, Portugal [30]
• Infraestruturas de Portugal S.A., Portugal [31–35]
• Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code	Variable and unit
fatal_pc_km	Fatalities per billion passenger-km
fatal_mIn	Fatalities per million inhabitants
accid_adj_pc_km	Accidents per billion passenger-km
p_km	Billions of passenger-km
croad_inv_km	Investment in roads construction per kilometer, €/km (2015 constant prices)
croad_maint_km	Expenditure on roads maintenance per kilometer €/km (2015 constant prices)
prop_motorwa	Proportion of motorways over the total road network (%)
populat	Population, in millions of inhabitants
unemploy	Unemployment rate (%)
petro_car	Consumption of gasolina and petrol derivatives (tons), per tourism
alcohol	Alcohol consumption, in liters per capita (age > 15)
mot_index	Motorization index, in cars per 1,000 inhabitants
den_populat	Population density, inhabitants/km2
cgdp	Gross Domestic Product (GDP), in € (2015 constant prices)
cgdp_cap	GDP per capita, in € (2015 constant prices)
precipit	Average depth of rain water during a year (mm)
prop_elder	Proportion of people over 65 years (%)
dps	Demerit Point System, dummy variable (0: no; 1: yes)
freight	Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium

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).

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

Model accuracy of collision types in classification tree.

Ordinal logistic model results for crash severity (AD mode).

Corresponding data set for Tran-SET Project No. 17ITSTSA01. Abstract of the final report is stated below for reference:

"The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The highrisk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections."