Some ** percent of drivers aged between 21 and 24 were involved in fatal alcohol-impaired accidents, representing the age group most likely to die in drunk-driving crashes. Only *** percent of over-74-year-olds were involved in such accidents.

I test the effect of harsher punishments and sanctions on driving under the influence (DUI). In this setting, punishments are determined by strict rules on blood alcohol content (BAC) and previous offenses. Regression discontinuity derived estimates suggest that having a BAC above the DUI threshold reduces recidivism by up to 2 percentage points (17 percent). Likewise having a BAC over the aggravated DUI threshold reduces recidivism by an additional percentage point (9 percent). The results suggest that the additional sanctions experienced by drunk drivers at BAC thresholds are effective in reducing repeat drunk driving. (JEL I12, K42, R41)

Ever driven in a car when you had been drinking alcohol, or been in a car driven by a friend who had been drinking (High School only) by sex, race/ethnicity, and grade, California Healthy Kids Survey, 2015-16METADATA:Notes (String): Lists table title, sourceYear (String): Year of surveyCategory (String): Lists the category representing the data: Santa Clara County is for total surveyed population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only) and grade level (9th, 11th, or non-traditional).Percent (Numeric): Percentage of high school students who have ever driven in a car when they had been drinking alcohol, or been in a car driven by a friend who had been drinking

The graph presents average settlement ranges for drunk driving accidents in the U.S., as reported by various law firms. The x-axis lists the names of the law firms: Joy Foy and Associates, Mezrano Law Firm, Phillips Law Offices, and Pacin Levine. The y-axis displays the settlement amounts in U.S. dollars. Each firm's data point includes a lower and upper bound of settlement ranges, highlighting the variability and expectations in compensation claims. The settlements span from a low of $10,000 at Phillips Law Offices to a high of $125,000, which is the upper limit for both Joy Foy and Associates and Mezrano Law Firm. This bar graph illustrates the broad spectrum of settlements that victims might anticipate from drunk driving incidents handled by these firms. The data provides a snapshot of the diverse financial outcomes in such legal cases across different law firms in the United States.

In 2023, the share of adults who claimed to have driven after consuming alcoholic beverages in Brazil amounted to nearly *** percent, up from around *** percent reported two years earlier. This figure decreased compared to 2016, when the percentage of respondents who declared to drive after consuming alcoholic beverages reached over ***** percent in the South American country. Drink-driving, or driving under the influence of alcohol, was more frequent among men than women in Brazil.

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

On average, ten percent of French people have been or nearly been involved in a car accident caused by excessive alcohol consumption in 2020. That year, the Hauts-de-France and Provence-Alpes-Côte d'Azur regions were the ones where this percentage was the highest (13 percent), while it was the lowest in Brittany (six percent).

Objective: The objective of this study was to examine the effects of changes to Washington State’s alcohol ignition interlock laws: moving issuance of interlock orders from the courts to the driver licensing department (July 2003); extending the interlock order requirement to all persons convicted of driving under the influence (DUI; June 2004); allowing an interlock in lieu of an administrative driver’s license suspension (January 2009); and requiring proof of interlock installation to reinstate the driver’s license (January 2011). Method: Trends in conviction types, interlock installation rates, and 2-year cumulative recidivism rates were examined for first-time and repeat offenders with convictions stemming from DUI arrests during 1999–2012. Autoregressive integrated moving average (ARIMA) models examined the association between law changes and installation rates, law changes and recidivism rates, and installation rates and recidivism rates. Results: During the study period, there was a large increase in the proportion of first-time DUI arrests reduced to alcohol-related negligent/reckless driving convictions, offenses not requiring interlock orders. The interlock installation rate increased substantially and the recidivism rate declined substantially among both first and repeat offenders. Based on the ARIMA models for first offenders, the 2004 and 2009 law changes were associated with increased interlock installation rates and lower recidivism rates. For first offenders arrested during the last quarter of 2012, the model estimates a 26% reduction in the recidivism rate (from an expected 7.7% without the 4 laws to 5.6%). A 1 percentage point increase in the interlock installation rate was associated with a 0.06 percentage point decline in the recidivism rate among first offenders. If the association carried forward and if the installation rate had been 100% rather than 38% in the last quarter of 2012, the 2-year recidivism rate would have been reduced from 5.6 to 2%. Among repeat offenders, the 2003 and 2009 law changes were associated with increased interlock installation rates, and the 2009 law change was associated with a nonsignificant decline in recidivism. Conclusions: In Washington, rates of interlock installations increased as interlock laws were strengthened, and the increase was associated with reductions in recidivism among first DUI offenders. Washington’s experience suggests that states can reduce DUI recidivism by requiring interlock orders for all offenders, allowing offenders to install interlocks in lieu of an administrative driver’s license suspension, and closing statutory loopholes that allow plea reductions to convictions without interlock orders.

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

Between January and December 2019, the percentage of alcohol-impaired drivers involved in fatal car crashes peaked in September: 9.4 percent of such accidents occurred in this month. February showed the lowest rate with 6.2 percent. Alcohol-impaired crashes are those involving a driver with a blood alcohol concentration (BAC) of 0.08 grams per deciliter or higher.

Historical Dataset of Lake Drive Program For Hearing Impaired is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),Asian Student Percentage Comparison Over Years (2009-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2014-2020),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2008-2023)

Context

Large truck and Bus Crash tracking from US Department of Transportation

Content

Fatal crashes by tractor trailers plus miles traveled. What is missing is the column showing total fatalities for the year.

Acknowledgements

https://www.fmcsa.dot.gov/safety/data-and-statistics/large-truck-and-bus-crash-facts-2014

Inspiration

I want to add to this some other quantitative data. The main idea is to show that the number of fatalities involving big trucks does not surpass fatalities caused by say, drunk drivers, or from texting. Also, nothing in DOT data shows fault. However, I think the public is slanted in thinking that professional drivers are more dangerous than four wheel drivers. Also, I think the trend will show that the percentage of crashes per year doesn't change by increasing driver regulations on sleep and rest times.

The purpose of this study was twofold. First, researchers wanted to assess the benefits of the driving while intoxicated (DWI) drug court established in the Las Cruces, New Mexico, Municipal Court in an effort to determine its future viability. This was accomplished by examining the behaviors and attitudes of three groups of convicted drunk-drivers and determining the extent to which these groups were different or similar. The three groups included: (1) non-alcoholic first- and second-time offenders (non-alcoholic offenders), (2) alcoholic first- and second-time DWI offenders (alcoholic offenders), and (3) chronic three-time (or more) DWI offenders (chronic offenders). The second purpose of this study was to explore police officers' attitudes toward court-based treatment programs for DWI offenders, while examining the distinguishing characteristics between police officers who support court-based programs for drunk drivers and those who are less likely to support such sanctions. Data for Part 1, Drug Court Survey Data, were collected using a survey questionnaire distributed to non-alcoholic, alcoholic, and chronic offenders. Part 1 variables include blood alcohol level, jail time, total number of prior arrests and convictions, the level of support from the respondents' family and friends, and whether the respondent thought DWI was wrong, could cause injury, or could ruin lives. Respondents were also asked whether they acted spontaneously in general, took risks, found trouble exciting, ever assaulted anyone, ever destroyed property, ever extorted money, ever sold or used drugs, thought lying or stealing was OK, ever stole a car, attempted breaking and entering, or had been a victim of extortion. Demographic variables for Part 1 include the age, gender, race, and marital status of each respondent. Data for Part 2, Police Officer Survey Data, were collected using a survey questionnaire designed to capture what police officers knew about the DWI Drug Court, where they learned about it, and what factors accounted for their attitudes toward the program. Variables for Part 2 include police officers' responses to whether DWI court was effective, whether DWI laws were successful, the perceived effect of mandatory jail time versus treatment alone, major problems seen with DWI policies, if DWI was considered dangerous, and how the officer had learned or been briefed about the drug court. Other variables include the number of DWI arrests, and whether respondents believed that reforms weaken police power, that DWI caused more work for them, that citizens have bad attitudes, that the public has too many rights, and that stiffer penalties for DWI offenders were more successful.

Filtered view of County crime data to show driving under the influence for liquor and drugs.

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.

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

The Reintegrative Shaming Experiments (RISE) project compared the effects of standard court processing with the effects of a restorative justice intervention known as conferencing for four kinds of cases: drunk driving (over .08 blood alcohol content) at any age, juvenile property offending with personal victims, juvenile shoplifting offenses detected by store security officers, and youth violent crimes (under age 30). Reintegrative shaming theory underpins the conferencing alternative. It entails offenders facing those harmed by their actions in the presence of family and friends whose opinions they care about, discussing their wrongdoing, and making repayment to society and to their victims for the costs of their crimes, both material and emotional. These conferences were facilitated by police officers and usually took around 90 minutes, compared with around ten minutes for court processing time. The researchers sought to test the hypotheses that (1) there would be less repeat offending after a conference than after a court treatment, (2) victims would be more satisfied with conferences than with court, (3) both offenders and victims would find conferences to be fairer than court, and (4) the public costs of providing a conference would be no greater than, and perhaps less than, the costs of processing offenders in court. This study contains data from ongoing experiments comparing the effects of court versus diversionary conferences for a select group of offenders. Part 1, Administrative Data for All Cases, consists of data from reports by police officers. These data include information on the offender's attitude, the police station and officer that referred the case, blood alcohol content level (drunk driving only), offense type, and RISE assigned treatment. Parts 2-5 are data from observations by trained RISE research staff of court and conference treatments to which offenders had been randomly assigned. Variables for Parts 2-5 include duration of the court or conference, if there was any violence or threat of violence in the court or conference, supports that the offender and victim had, how much reintegrative shaming was expressed, the extent to which the offender accepted guilt, if and in what form the offender apologized (e.g., verbal, handshake, hug, kiss), how defiant or sullen the offender was, how much the offender contributed to the outcome, what the outcome was (e.g., dismissed, imprisonment, fine, community service, bail release, driving license cancelled, counseling program), and what the outcome reflected (punishment, repaying community, repaying victims, preventing future offense, restoration). Data for Parts 6 and 7, Year 0 Survey Data from Non-Drunk-Driving Offenders Assigned to Court and Conferences and Year 0 Survey Data from Drunk-Driving Offenders Assigned to Court and Conferences, were taken from interviews with offenders by trained RISE interview staff after the court or conference proceedings. Variables for Parts 6 and 7 include how much the court or conference respected the respondent's rights, how much influence the respondent had over the agreement, the outcome that the respondent received, if the court or conference solved any problems, if police explained that the respondent had the right to refuse the court or conference, if the respondent was consulted about whom to invite to court or conference, how the respondent was treated, and if the respondent's respect for the justice system had gone up or down as a result of the court or conference. Additional variables focused on how nervous the respondent was about attending the court or conference, how severe the respondent felt the outcome was, how severe the respondent thought the punishment would be if he/she were caught again, if the respondent thought the court or conference would prevent him/her from breaking the law, if the respondent was bitter about the way he/she was treated, if the respondent understood what was going on in the court or conference, if the court or conference took account of what the respondent said, if the respondent felt pushed around by people with more power, if the respondent felt disadvantaged because of race, sex, age, or income, how police treated the respondent when arrested, if the respondent regretted what he/she did, if the respondent felt ashamed of what he/she did, what his/her family, friends, and other people thought of what the respondent did, and if the respondent had used drugs or alcohol the past year. Demographic variables in this data collection include offender's country of birth, gender, race, education, income, and employment.

Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. Abbreviations IoT: Internet of things; CNN: Convolutional Neural Network; SVM: Support vector machine; HRV: Heart rate variability; RRI: R-R Interval; MSPC: Multivariate Statistical process control; EAR: Eye aspect ratio; HUD: Head-up display; GPS: Global positioning system; CAN: Controller area network; GPU: Graphics processing unit; IR: Infrared; GSM: Global system for mobile communication; EEG: Electroencephalogram; PCA: Principal component analysis; SVC: Support vector classifier; SdsAEs: Stacked denoising sparse autoencoders; ECG: Electrocardiogram; LED: Light emitting diode; NFC: Near field communication; PSO: Personal security officer; PPG: Photoplethysmography; EDA: Electrodermal activity; EMG: Electromyography; LCD: Liquid crystal display; RF SoCs: Radiofrequency system on chip; PLR: Piecewise linear representation; BAC: Blood alcohol content; BPNN: Backpropagation Neural Network; ADSD: Automated driver sleepiness detection; EOG: Electroocoulogram; KNN: K nearest neighbor; CBR: Case-based reasoning; RF: Random forest; NIR: Near-infrared; LBP: Local binary pattern; PERCLOS: Percentage of Eye Closure; SVD: Singular value decomposition; FFT: Fast Fourier transform; LSTM: Long short-term memory; DDD: Drunk driver detection; BLE: Bluetooth low energy; SWM: Steering wheel movements; M-SVM: Mobile-based Support Vector Machine; AI: Artificial intelligence; ML: Machine learning; DL: Deep learning; PCA: Principal component analysis; IPCA: Incremental principal component analysis; ANN: Artificial neural network; CAV: Connected and automated vehicles

DISCLAIMER: This dataset may contain preliminary data that has not yet been verified and may be changed at a later date due to additional investigation. Additionally, the data entry process may include mechanical and/or human errors. Therefore, the Vermont State Police does not guarantee the accuracy, completeness, timeliness, or correct sequencing of the information. This dataset also excludes any records that would compromise the privacy of crime victims or the fidelity of ongoing investigations. Any information that could be used to uniquely identify a person or vehicle has also been excluded.

SUMMARY: This dataset contains information related to arrests for driving under the influence as recorded by the Vermont State Police between January 1, 2013 and the previous month. These data are extracted from the Vermont State Police records management system on a monthly basis. Each record is unique to an individual not an incident, so you may find multiple records with the same incident number in the dataset. This occurs when there is more than one individual associated with an incident. This particular dataset is made available in an effort to enhance the transparency of law enforcement activities in Vermont. Should you have questions about records in this dataset, please contact the specific law enforcement agency as they are each responsible for their own records. To access a summary page of this dataset, select the “About” tab on the right side of this page and scroll down to the attachments and click on the PDF document.