scholarly journals Differences in Near-Crash Risk by Types of Distraction: A Comparison of Trends between Freeways and Two-Lane Highways using Naturalistic Driving Data

2021 ◽  
pp. 036119812110438
Author(s):  
Anshu Bamney ◽  
Nusayba Megat-Johari ◽  
Trevor Kirsch ◽  
Peter Savolainen
Keyword(s):  
Motor Vehicle ◽  
The United States ◽  
Distracted Driving ◽  
Personal Hygiene ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Logistic Regression Models ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Naturalistic Driving

Distracted driving is among the leading causes of motor vehicle crashes in the United States, though the magnitude of this problem is difficult to quantify given limitations of police-reported crash data. This study leveraged data from the second Strategic Highway Research Program Naturalistic Driving Study to gain important insights into the risks posed by driver distraction on both freeways and two-lane highways. More than 50 types of secondary tasks were aggregated into ten distraction type categories and mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the types of distractions that created the most pronounced risks were those that introduced a combination of cognitive, visual, and manual distractions. For example, drivers who used cell phones were subject to higher risks and these risks tended to be most pronounced when both visual and manual distractions were involved. Likewise, risks tended to be highest when drivers reached for other objects inside the vehicle, engaged in personal hygiene-related activities, or focused on activities occurring outside of the driving environment. Although the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. From a policy standpoint, the results of this study provide further motivation for more aggressive legislation and enforcement of distracted driving.

Accounting for Driver Distraction and Socioeconomic Characteristics in a Crash Risk Index: Naturalistic Driving Study

2017 ◽  
Vol 2659 (1) ◽  
pp. 204-211 ◽  
Author(s):  
Mengqiu Ye ◽  
Osama A. Osman ◽  
Sherif Ishak
Keyword(s):  
Risk Index ◽  
The United States ◽  
Distracted Driving ◽  
Crash Risk ◽  
Grading System ◽  
Secondary Tasks ◽  
Auto Insurance ◽  
Socioeconomic Characteristics ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving has long been acknowledged as one of the main contributors to crashes in the United States. According to past studies, driving behavior proved to be influenced by the socioeconomic characteristics of drivers. However, few studies attempted to quantify that influence. This study proposed a crash risk index (CRI) to estimate the crash risk associated with the socioeconomic characteristics of drivers and their tendency to experience distracted driving. The analysis was conducted with data from the SHRP 2 Naturalistic Driving Study. The proposed CRI was developed on a grading system of three measures: the crash risk associated with performing secondary tasks during driving, the effect of socioeconomic attributes (e.g., age) on the likelihood of engagement in secondary tasks, and the effect of specific categories within each socioeconomic attribute (e.g., age older than 60) on the likelihood of engagement in secondary tasks. Logistic regression analysis was performed on the secondary tasks, socioeconomic attributes, and specific socioeconomic characteristics. The results identified the significant secondary tasks with high crash risk and the socioeconomic characteristics with significant effect on determining drivers’ involvement in secondary tasks in each tested parameter. These results were used to quantify the grading system measures and hence estimate the proposed CRI. This index indicates the relative crash risk associated with the socioeconomic characteristics of drivers and considers the possibility of engagement in secondary tasks. The proposed CRI and the associated grading system are plausible methods for estimating auto insurance premiums.

Crash and Near-Crash Risk Assessment of Distracted Driving and Engagement in Secondary Tasks: A Naturalistic Driving Study

2018 ◽  
Vol 2672 (38) ◽  
pp. 245-254 ◽  
Author(s):  
Peter R. Bakhit ◽  
BeiBei Guo ◽  
Sherif Ishak
Keyword(s):  
Traffic Safety ◽  
Real World ◽  
Secondary Task ◽  
Safety Concern ◽  
Distracted Driving ◽  
Crash Risk ◽  
Secondary Tasks ◽  
Depth Analysis ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.

Driving and Public Health

2021 ◽  
Keyword(s):  
Public Health ◽  
United States ◽  
Health Problem ◽  
Cell Phone ◽  
Motor Vehicle ◽  
Electronic Device ◽  
Public Health Problem ◽  
The United States ◽  
Distracted Driving ◽  
The Past

Distracted driving is defined in the Oxford English Dictionary as “the practice of driving a motor vehicle while engaged in another activity, typically one that involves the use of a mobile phone or other electronic device.” However, other distractions not involving the use of a cell phone or texting are important as well, contributing to this burgeoning public health problem in the United States. Examples include talking to other passengers, adjusting the radio or other controls in the car, and daydreaming. Distracted driving has been linked to increased risk of motor vehicle crashes (MVCs) in the United States, representing one of the most preventable leading causes of death for youth ages 16 to 24 years. Undoubtedly, the proliferation of cell phone, global positioning system (GPS), and other in-vehicle and personal electronic device use while driving has led to this rise in distracted driving prevalence. This behavior has impacted society—including individual and commercial drivers, passengers, pedestrians—in countless numbers of ways, ranging from increased MVCs and deaths to the enactment of new driving laws. In 2016, for example, 20 percent of all US pediatric deaths (nearly 4,000 children and adolescents) were due to fatal MVCs. It has been estimated that at any given time, more than 650,000 drivers are using cell phones or manipulating electronic devices while driving. In the United States, efforts are underway to reduce this driving behavior. In the past two decades, state and federal laws have specifically targeted cell phone use and texting while driving as priority areas for legal intervention. Distracted driving laws have become “strategies of choice” for tackling this public health problem, though their enforcement has emerged as a major challenge and varies by jurisdiction and location. Multimodal interventions using models such as the “three Es” framework—Enactment of a law, Education of the public about the law and safety practices, and Enforcement of the law—have become accepted practice or viewed as necessary steps to successfully change this behavior caused by distractions while driving. This Oxford Bibliographies review introduces these and other aspects (including psychological influences and road conditions) of distracted driving through a presentation of annotated resources from peer- and non-peer-reviewed literature. This selective review aims to provide policymakers, program implementers, and researchers with a reliable source of information on the past and current state of American laws, policies, and priorities for distracted driving.

scholarly journals Association of Photopic and Mesopic Contrast Sensitivity in older drivers with risk of motor vehicle collision using naturalistic driving data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cynthia Owsley ◽  
Thomas Swain ◽  
Rong Liu ◽  
Gerald McGwin ◽  
Mi Young Kwon
Keyword(s):  
Older Adults ◽  
Contrast Sensitivity ◽  
Motor Vehicle ◽  
Age Groups ◽  
Time Of Day ◽  
Motor Vehicle Crash ◽  
Older Drivers ◽  
Crash Risk ◽  
Sensitivity Testing ◽  
Naturalistic Driving

Abstract Background Older drivers have a crash rate nearly equal to that of young drivers whose crash rate is the highest among all age groups. Contrast sensitivity impairment is common in older adults. The purpose of this study is to examine whether parameters from the photopic and mesopic contrast sensitivity functions (CSF) are associated with incident motor vehicle crash involvement by older drivers. Methods This study utilized data from older drivers (ages ≥60 years) who participated in the Strategic Highway Research Program Naturalistic Driving Study, a prospective, population-based study. At baseline participants underwent photopic and mesopic contrast sensitivity testing for targets from 1.5–18 cycles per degree. Model fitting generated area under the log CSF (AULCSF) and peak log sensitivity. Participant vehicles were instrumented with sensors that captured continuous driving data when the vehicle was operating (accelerometers, global positioning system, forward radar, 4-channel video). They participated for 1–2 years. Crashes were coded from the video and other data streams by trained analysts. Results The photopic analysis was based on 844 drivers, and the mesopic on 854 drivers. Photopic AULCSF and peak log contrast sensitivity were not associated with crash rate, whether defined as all crashes or at-fault crashes only (all p > 0.05). Mesopic AULCSF and peak log sensitivity were associated with an increased crash rate when considered for all crashes (rate ratio (RR): 1.36, 95% CI: 1.06–1.72; RR: 1.28, 95% CI: 1.01–1.63, respectively) and at-fault crashes only (RR: 1.50, 95% CI: 1.16–1.93; RR: 1.38, 95% CI: 1.07–1.78, respectively). Conclusions Results suggest that photopic contrast sensitivity testing may not help us understand future crash risk at the older-driver population level. Results highlight a previously unappreciated association between older adults’ mesopic contrast sensitivity deficits and crash involvement regardless of the time of day. Given the wide variability of light levels encountered in both day and night driving, mesopic vision tests, with their reliance on both cone and rod vision, may be a more comprehensive assessment of the visual system’s ability to process the roadway environment.

Contrasting Crash- and Non-Crash-Involved Riders: Analysis of Data from the Motorcycle Crash Causation Study

2019 ◽  
Vol 2673 (7) ◽  
pp. 122-131 ◽  
Author(s):  
Hitesh Chawla ◽  
Ilker Karaca ◽  
Peter T. Savolainen
Keyword(s):  
Risk Factors ◽  
Public Health Problem ◽  
The United States ◽  
Crash Risk ◽  
Volume Data ◽  
Logistic Regression Models ◽  
Significant Public Health Problem ◽  
Significant Public Health ◽  
Paired Control ◽  
Motorcycle Crashes

Motorcycle crashes and fatalities remain a significant public health problem as fatality rates have increased substantially as compared to other vehicle types in the United States. Analysis of causal factors for motorcycle crashes is often challenging given a lack of reliable traffic volume data and the fact that such crashes comprise a relatively small portion of all traffic crashes. Given these limitations, on-scene crash investigations represent an ideal setting through which to investigate the precipitating factors for motorcycle-involved crashes. This study examines motorcycle crash risk factors by employing data recently made available from the Federal Highway Administration Motorcycle Crash Causation Study (MCCS). The MCCS represents a comprehensive investigative effort to determine the causes of motorcycle crashes and involved the collection of in-depth data from 351 crashes, as well as the collection of comparison data from 702 paired control observations in Orange County, California. This dataset provides a unique opportunity to understand how the risk of crash involvement varies across different segments of the riding population. Logistic regression models are estimated to identify the rider and vehicle attributes associated with motorcycle crashes. The results of the study suggest that motorcycle crash risks are related to rider age, physical status, and educational attainment. In addition to such factors outside of the rider’s control, several modifiable risk factors, which arguably affect the riders’ proclivity to take risks, were also found to be significantly associated with motorcycle crash risk, including motorcycle type, helmet coverage, motorcycle ownership, speed, trip destination, and traffic violation history.

scholarly journals The Medical Referral Process and Motor-Vehicle Crash Risk for Drivers with Dementia

Geriatrics ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 91
Author(s):  
Jonathan Davis ◽  
Cara Hamann ◽  
Brandon Butcher ◽  
Corinne Peek-Asa
Keyword(s):  
Review Process ◽  
Motor Vehicle ◽  
The United States ◽  
Motor Vehicle Crash ◽  
Driving Ability ◽  
Crash Risk ◽  
Referral Process ◽  
Vehicle Crash ◽  
Increased Risk ◽  
Medical Referral

Cognitive and physical impairment can occur with dementia and reduce driving ability. In the United States, individual states have procedures to refer and evaluate drivers who may no longer be fit to drive. The license review process is not well understood for drivers with dementia. This study uses comprehensive data from the Iowa Department of Transportation to compare the referral process for drivers with and without dementia from January 2014 through November 2019. The likelihood of failing an evaluation test was compared between drivers with and without dementia using logistic regression. The risk of motor-vehicle crash after referral for review of driving ability was compared using a Cox proportional hazard model. Analysis controlled for the age and sex of the referred driver. Drivers with dementia performed worse on all tests evaluated except the visual screening test. After the referral process, the risk of crash was similar between those with and without dementia. Drivers with dementia were denied their license more frequently than referred drivers without dementia. However, drivers with dementia who successfully kept their license as a result of the license review process were not at an increased risk of crash compared to other referred drivers.

scholarly journals Effects of Rainfall on Vehicle Crashes in Six U.S. States

2016 ◽  
Vol 9 (1) ◽  
pp. 53-70 ◽  
Author(s):  
Alan W. Black ◽  
Gabriele Villarini ◽  
Thomas L. Mote
Keyword(s):  
Relative Risk ◽  
Motor Vehicle ◽  
Daily Rainfall ◽  
Crash Risk ◽  
Matched Pair ◽  
Vehicle Crashes ◽  
Injury Rates ◽  
Crash Data ◽  
Crash Rates ◽  
Matched Pair Analysis

Abstract Rainfall is one of many types of weather hazard that can lead to motor vehicle crashes. To better understand the link between rainfall and crash rates, daily gridded precipitation data and automobile crash data are gathered for six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota, Ohio) for the period 1996–2010. A matched pair analysis is used to pair rainfall days with dry days to determine the relative risk of crash, injury, and fatality. Overall, there is a statistically significant increase in crash and injury rates during rainfall days of 10% and 8%, respectively, leading to an additional 28 000 crashes and 12 000 injuries in the 1 May–30 September period each year relative to what would be expected if those days were dry. The risk of crashes and injuries increases for increasing daily rainfall totals, with an overall increase in crashes and injuries of 51% and 38% during days with more than 50 mm (2 in.) of rainfall. While urban counties and rural counties with and without interstates each saw increased crash risk during rainfall, urban counties saw the most significant increases in relative risk. There are a number of exceptions to these broad spatial patterns, indicating that relative risk varies in ways that are not explained solely by meteorological factors.

Trends in Cannabis Involvement and Risk of Alcohol Involvement in Motor Vehicle Crash Fatalities in the United States, 2000‒2018

2021 ◽  
pp. e1-e10
Author(s):  
Marlene C. Lira ◽  
Timothy C. Heeren ◽  
Magdalena Buczek ◽  
Jason G. Blanchette ◽  
Rosanna Smart ◽  
...  
Keyword(s):  
United States ◽  
Motor Vehicle ◽  
Blood Alcohol Concentration ◽  
The United States ◽  
Alcohol Concentration ◽  
Motor Vehicle Crash ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Vehicle Crash ◽  
Alcohol Involvement

Objectives. To assess cannabis and alcohol involvement among motor vehicle crash (MVC) fatalities in the United States. Methods. In this repeated cross-sectional analysis, we used data from the Fatality Analysis Reporting System from 2000 to 2018. Fatalities were cannabis-involved if an involved driver tested positive for a cannabinoid and alcohol-involved based on the highest blood alcohol concentration (BAC) of an involved driver. Multinomial mixed-effects logistic regression models assessed cannabis as a risk factor for alcohol by BAC level. Results. While trends in fatalities involving alcohol have remained stable, the percentage of fatalities involving cannabis and cannabis and alcohol increased from 9.0% in 2000 to 21.5% in 2018, and 4.8% in 2000 to 10.3% in 2018, respectively. In adjusted analyses, fatalities involving cannabis had 1.56 (95% confidence interval [CI] = 1.48, 1.65), 1.62 (95% CI = 1.52, 1.72), and 1.46 (95% CI = 1.42, 1.50) times the odds of involving BACs of 0.01% to 0.049%, 0.05% to 0.079%, and 0.08% or higher, respectively. Conclusions. The percentage of fatalities involving cannabis and coinvolving cannabis and alcohol doubled from 2000 to 2018, and cannabis was associated with alcohol coinvolvement. Further research is warranted to understand cannabis- and alcohol-involved MVC fatalities. (Am J Public Health. Published online ahead of print October 28, 2021:e1–e10. https://doi.org/10.2105/AJPH.2021.306466 )

Investigating Effect of Driver-, Vehicle-, and Road-Related Factors on Location-Specific Crashes with Naturalistic Driving Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 46-56 ◽  
Author(s):  
Grace Ashley ◽  
Osama A. Osman ◽  
Sherif Ishak ◽  
Julius Codjoe
Keyword(s):  
United States ◽  
Machine Learning ◽  
Traffic Accidents ◽  
The United States ◽  
Crash Risk ◽  
Related Factors ◽  
Data Set ◽  
Intersection Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

According to NHTSA, traffic accidents cost the United States billions of U.S. dollars each year. Intersection accidents alone accounted for 23% of the 32,675 motor crash deaths in 2014. With the advent of the largest naturalistic driving data set in the United States collected by the SHRP2 Naturalistic Driving Study project, this study performs a crash-only analysis to identify driver-, vehicle-, and roadway-related factors that affect the driving risk at different location types using a machine learning tool. The study then analyzes the most important factors obtained from the machine learning analysis to identify how they affect crash risk. The results, in order of importance of variables, were driver behavior, locality, lane occupied, alignment, and through travel lanes. Also, drivers who violated traffic signals were four times more likely to be involved in a crash than drivers who did not. Those who violated stop signs were two times more likely to be involved in crashes than those who did not. Drivers performing visual-manual (VM) tasks at uncontrolled intersections were 2.7 times more likely to be involved in crashes than those who did not engage in these tasks. At nonintersections, drivers who performed VM tasks were 3.4 times more likely to be involved in crashes than drivers who did not. These findings add to the evidence that the establishment of safety awareness programs geared toward intersection safety is imperative.

Determining If Walkability and Bikeability Indices Reflect Pedestrian and Cyclist Safety

2020 ◽  
Vol 2674 (9) ◽  
pp. 767-775
Author(s):  
Ahmed Osama ◽  
Maria Albitar ◽  
Tarek Sayed ◽  
Alexander Bigazzi
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Crash Risk ◽  
Spatial Effects ◽  
Perceived Safety ◽  
Crash Data ◽  
Neighborhood Walkability ◽  
Walk Score ◽  
Objective Risk ◽  
The City

Walkability and bikeability indices are used to succinctly quantify how conducive an environment is to walking and cycling, often including factors related to comfort and perceived safety. The potential assumption that “walkable” and “bikeable” mean safe for walking and cycling (i.e., the association with objective safety or crash risk) has not yet been examined. This study investigates the association between two widely used measures (walk score and bike score) and pedestrian and cyclist crashes in Vancouver, Canada, to determine whether more walkable and bikeable areas of the city are also safer for walking and biking, after controlling for exposure. Multivariate Bayesian crash models with random and spatial effects are developed for pedestrian–motor-vehicle and cyclist–motor-vehicle crashes in 134 traffic analysis zones using 5 years of crash data with walking, cycling, and motor-vehicle traffic volume controls for exposure. Results indicate that areas of the city with higher walkability and bikeability can be potentially associated with greater pedestrian and cyclist crash risk, respectively, even after controlling for exposure. While the clear answer is that neighborhood walkability and bikeability does not indicate safety for pedestrians and cyclists, questions remain as to whether they should, and if so, how they could be modified to better incorporate objective risk.

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