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

Driving Under the Influence (DUI) Crashes reports the number of recorded DUI crashes, fatalities, and injuries per town, for a given year.

the Ministry of Road Transport and Highways of the Government of India releases annual reports on road accidents and casualties in the country. Additionally, many state governments also release data on road accidents within their jurisdiction. There are many potential causes of road accidents, including: Distracted driving (e.g. using a cell phone, eating, or applying makeup while driving) Impaired driving (e.g. driving under the influence of alcohol or drugs) Reckless or aggressive driving (e.g. speeding, tailgating, or running red lights) Fatigue or drowsy driving Poor road conditions (e.g. potholes, debris, or lack of proper signage) Vehicle defects or malfunctions Poor weather conditions (e.g. rain, snow, or fog) Inadequate infrastructure (e.g. lack of proper lighting, median barriers, or guardrails) Pedestrian or bicycle errors Wildlife crossing the road. There are several datasets available on road accidents, depending on the country and region. Here are a few examples:

In the United States, the National Highway Traffic Safety Administration (NHTSA) provides data on vehicle crashes, including details such as the location, cause, and number of injuries and fatalities. The United Kingdom's Department for Transport provides data on reported road accidents, including information on the type of vehicle, number of casualties, and severity of injuries. The World Health Organization (WHO) also has a Global Status Report on Road Safety, which provides data on road accidents and fatalities for countries around the world. In India, the Ministry of Road Transport and Highways (MoRTH) provides annual data on road accidents and fatalities. The Global Road Safety Partnership (GRSP) also has a wealth of data on road accidents and fatalities, particularly in low- and middle-income countries. It is important to note that these datasets may have different data collection methodologies, and may not include all road accidents that have occurred.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

This dataset supports measure M.D.2 of SD 2023. The original source of the data is the Texas Department of Transportation supplemented by analysis from the Austin Transportation Department. Each row represents the number of crashes resulting in fatalities or injuries due to the top contributing factors for a year. This dataset can be used to understand the trends in the number and percentages of crashes resulting in serious injuries or fatalities caused by the top contributing factors.

View more details and insights related to this measure on the story page : https://data.austintexas.gov/stories/s/9ssh-bavk

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

The India Road Accident Dataset provides a comprehensive view of road accidents across various states and cities in India. The dataset includes 3,000 accident records spanning from 2018 to 2023, with detailed attributes such as accident severity, weather conditions, road type, vehicle involvement, casualties, and more.

This dataset is ideal for predictive modeling, risk assessment, trend analysis, and policy-making related to road safety in India.

Key Features 📌 State & City-Level Data – Covers multiple Indian states and cities, allowing for regional accident analysis. 📌 Time-Based Analysis – Includes year, month, day of the week, and time of the accident. 📌 Accident Severity Levels – Categorized as Fatal, Serious, or Minor. 📌 Vehicle & Driver Insights – Includes vehicle types involved, driver age, gender, and license status. 📌 Environmental & Road Conditions – Captures weather, lighting, road type, and speed limits at accident locations. 📌 Alcohol Involvement – Identifies whether the accident was linked to drunk driving.

Potential Use Cases ✅ Predictive Modeling: Build machine learning models to predict accident hotspots. ✅ Trend Analysis: Identify seasonal, temporal, or geographical trends in road accidents. ✅ Policy Making & Road Safety Improvements: Assist governments and NGOs in designing safety measures. ✅ Data Visualization & Dashboarding: Create interactive reports for accident trends.

****Dataset Description: Road Accident Records****

This dataset contains detailed records of road accidents occurring within a specific geographic region over a defined period. The data encompasses various attributes related to each accident, providing valuable insights into factors contributing to road safety issues.

Attributes Included:

1. Accident ID: A unique identifier assigned to each accident for reference and tracking purposes.
2. Date and Time: The date and time when the accident occurred, facilitating temporal analysis and trend identification.
3. Location: The precise geographical coordinates or address where the accident took place, enabling spatial analysis and mapping.
4. Severity: The severity level of the accident, categorized based on the extent of injuries, property damage, or fatalities.
5. Weather Conditions: Information about weather conditions prevailing at the time of the accident, such as clear, rainy, foggy, or snowy weather.
6. Road Conditions: Description of road conditions, including dry, wet, icy, or slippery surfaces.
7. Vehicle Involved: Details about vehicles involved in the accident, including types, models, and counts.
8. Contributing Factors: Factors contributing to the accident, such as speeding, distracted driving, drunk driving, road defects, or mechanical failures.
9. Injuries and Fatalities: Number of individuals injured or killed as a result of the accident.
10. Vehicle Maneuvers: Description of maneuvers or actions taken by vehicles involved, such as turning, braking, or overtaking.

Potential Uses:

1. Safety Analysis: Identifying high-risk locations, times, and conditions to develop targeted safety measures and interventions.
2. Predictive Modeling: Building predictive models to anticipate and prevent accidents based on historical patterns and contributing factors.
3. Policy Formulation: Informing policymaking and infrastructure planning to improve road safety and reduce accident rates.
4. Public Awareness Campaigns: Designing targeted awareness campaigns to educate drivers about common risk factors and safe driving practices.
5. Law Enforcement: Supporting law enforcement efforts by identifying areas with high accident rates and enforcing traffic regulations accordingly.

Data Source:

The dataset is sourced from official accident reports, police records, or other reliable sources authorized to collect and maintain such data. Care has been taken to ensure accuracy and completeness, although some discrepancies may exist due to reporting errors or data collection limitations.

Note: Use of this dataset for research, analysis, or other purposes should adhere to relevant data privacy and ethical guidelines, ensuring responsible use and respect for individual privacy rights.

DUI Crashes – Crashes where at least one driver involved is identified as under the influence of Medication, Drugs, or Alcohol at the time of the crash in the accident report.Code value document click HEREThis is a geographical representation of the data available in the CTCDR. Data set represents all MMUCC Crashes from January, 2015 to crashes reported to the DOT and processed within the last 30 - 60 days

Goal: Improve safety in the Champaign-Urbana Urban Area

Objective: Reduce the five-year rolling average of number of impaired driving A-injuries by 2 percent (from 13 to less than 11) by 2025 based on 2017 in the Champaign-Urbana urban area.

Performance Measure: Total number of impaired driving A-injuries in the Champaign-Urbana urban area

Data Sources: IDOT crash database

The numbers in this table is updated from Figure 22 in the Champaign-Urbana Urban Safety Plan, which did not reflect the correct A-injuries statistics.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

India has one of the highest road accident rates in the world, with over 1,50,000 people losing their lives each year due to road accidents. In order to analyze and classify road accidents in India, we can look at various factors such as the causes, types of vehicles involved, types of accidents, and location. Causes: The major causes of road accidents in India include reckless driving, speeding, drunk driving, driver fatigue, poor road infrastructure, and lack of awareness about traffic rules and regulations. Types of vehicles: Most road accidents in India involve two-wheelers and four-wheelers, with a high number of pedestrians and cyclists also being affected. Types of accidents: Some common types of road accidents in India include head-on collisions, rear-end collisions, side-impact collisions, and rollovers. Location: Road accidents in India occur across different locations, with the highest number of accidents occurring on highways, followed by urban and rural roads. Classification: Based on the above factors, road accidents in India can be classified into different categories such as: Vehicle-related accidents: These accidents involve collisions between different types of vehicles such as cars, buses, trucks, and motorcycles. Pedestrian-related accidents: These accidents involve collisions between pedestrians and vehicles, or pedestrians falling due to poor road infrastructure. Infrastructure-related accidents: These accidents occur due to poor road conditions such as potholes, uneven surfaces, and inadequate street lighting. Weather-related accidents: These accidents occur due to adverse weather conditions such as heavy rain, fog, or snow, which reduce visibility and affect vehicle control. Human error-related accidents: These accidents occur due to human factors such as reckless driving, speeding, driving under the influence of alcohol or drugs, and driver fatigue. By analyzing and classifying road accidents in India based on these factors, we can identify the root causes and take appropriate measures to prevent them. Some of the measures that can be taken include improving road infrastructure, increasing awareness about traffic rules and regulations, and enforcing stricter penalties for traffic violations.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Context

This data set contains collision data for car accidents in Canada from 1999-2014 as provided by Transport Canada. This dataset provides various features such as time of day, whether or not there were fatalities, driver gender, etc. The codes for the different categories can be found in 'drivingLegend.pdf'. The original csv file is no longer available, however it can be downloaded in portions by selecting the various features using this portal.

Content

Each feature is 100% categorical data, with some features having 2 categories, while others can have 30+. The data is not completely imputed appropriately (you can thank Stats Canada), so some data preprocessing is required. For instance, categories may have duplicates in the form of '01' and '1', or some data may be formatted as integers while others are formatted as strings. Some data is not known and is marked accordingly in 'drivingLegend.pdf'. Unfortunately, features such as location and impaired driving are not a part of this feature set, however there are plenty of others to work with.

Acknowledgements

This data is provided by Transport Canada and Statistics Canada. This data is provided under the Statistics Canada Open License Agreement.

Inspiration

Questions of particular interest: - What are the main contributing factors to accident fatalities? - Can a machine learning classifier be used to predict fatalities? Note: If attempting to predict fatalities, the data is highly skewed towards non-fatalities.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Legislation laying down a legal limit for blood alcohol concentration was introduced in New South Wales in 1968, but has had a disappointing effect on drink-driving behaviour. This survey was designed to examine what factors might be preventing the law's operating as an effective deterrent, and to obtain essential information for the planning of countermeasures to alcohol-related crashea Interviews were conducted with 1197 men and women, aged between 17 and 69 years, distributed at random through the Sydney metropolitan area. Results included the following findings: seven out of ten men at least sometimes combine drinking and driving, many of them frequently, but only two out of ten women; the group containing the highest proportion of drinking drivers is young men; six out of ten young men admitted to driving after drinking too much; the commonest place to drink away from home is the pub, but men usually drive themselves home afterwards and very rarely use alternative means of transport; young men are more likely to feel pressures to keep up with mates when drinking at a pub; half the respondents did not include alcohol in a list of the three most important factors which in their view contributed to serious traffic accidents; many men overestimate the amount of beer they can drink and still be safe to drive; there is widespread ignorance as to the legal limit for blood alcohol; the legal limit is not seen to be related to safe driving; eight of ten male drivers who drink said the new legislation had not changed their drinking-driving habits. Driving after drinking appears to be customary behaviour for men, and thus attempts to reduce alcohol-related accidents by reducing the combined incidence of drinking and driving in the community will come into direct conflict with social custom. Social pressures now exist which ensure that the custom of driving after drinking too much is likely to persist in certain sections of the male population. The present results suggest that ignorance and misinterpretation of the drink-driving law may be contributing to widespread opposition to it. Many men, especially young men, are resentful of what they see as an unrealistic attempt to set an arbitrary limit on their drinking.

Note: This resource was originally published on opengov.nsw.gov.au. The OpenGov website has been retired. If you have any questions, please contact the Agency Services team at transfer@mhnsw.au

Agency

• Traffic Accident Research Unit (TARU)

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Updated daily postings on Montgomery County’s open data website, dataMontgomery, provide the public with direct access to crime statistic databases - including raw data and search functions – of reported County crime. The data presented is derived from reported crimes classified according to the National Incident-Based Reporting System (NIBRS) of the Criminal Justice Information Services (CJIS) Division Uniform Crime Reporting (UCR) Program and documented by approved police incident reports. The data is compiled by “EJustice”, a respected law enforcement records-management system used by the Montgomery County Police Department and many other law enforcement agencies. To protect victims’ privacy, no names or other personal information are released. All data is refreshed on a quarterly basis to reflect any changes in status due to on-going police investigation.

dataMontgomery allows the public to query the Montgomery County Police Department's database of founded crime. The information contained herein includes all founded crimes reported after July 1st 2016 and entered to-date utilizing Uniform Crime Reporting (UCR) rules. Please note that under UCR rules multiple offenses may appear as part of a single founded reported incident, and each offense may have multiple victims. Please note that these crime reports are based on preliminary information supplied to the Police Department by the reporting parties. Therefore, the crime data available on this web page may reflect:

-Information not yet verified by further investigation -Information that may include attempted and reported crime -Preliminary crime classifications that may be changed at a later date based upon further investigation -Information that may include mechanical or human error -Arrest information [Note: all arrested persons are presumed innocent until proven guilty in a court of law.]

Update Frequency: Daily

Traffic collision reports recorded by the San Diego Police Department with additional details on people and vehicles involved. Generally a report is not taken for property damage-only collisions that do not involve hit & run or DUI. The California Highway Patrol is responsible for handling collisions occurring on the freeway. A single collision may involve multiple people and/or vehicles. This dataset contains one row per person involved in a collision. Each collision is uniquely identified in the report_id field. Throughout 2018 and 2019, a new collision reporting process was introduced that resulted in more detailed data collection. Data fields listed in the Data Dictionary that include ** in the description may have incomplete data values for history records.