Distracted Driving and Risk of Crash or Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data With a Case-Crossover Study Design

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Distracted Driving Behavior Among Adults in the Perambalur District: A Cross-Sectional Study

Background Distracted driving is a major public health concern. Distraction results in reduced speed control, lateral lane position, reduced situational awareness, and impaired response times to roadway hazards. Visual, cognitive, and manual distractions impair drivers in different ways. With the above background, this study was conducted with the objective of estimating the proportion of distracted driving behavior and its predictors among the adult population in the Perambalur district, Tamil Nadu, India, by using the Total Distracted Driving Scale. Materials and methods A cross-sectional study was conducted among 403 adults aged 18 years and above by convenience sampling technique in the Perambalur district for six months. A pretested, semi-structured proforma was used to collect data on socio-demographic characteristics such as age, sex, education, and occupation. To quantify distracted driving behavior, the Total Distracted Driving Scale was used. This scale contains seven questions about talking on the phone, five questions about texting, and five questions about using specific devices while driving. Data were entered into Microsoft Excel and analyzed using the Statistical Package for the Social Sciences (SPSS) Version 21. Descriptive statistics were used to describe the distracted driving behavior with respect to each variable, and to find out the significance, a corresponding statistical test was employed. A p-value of less than 0.05 was considered statistically significant. Results The mean age of the study participants was 24.86 ± 10.63 years. More than half of the study subjects (52.4 percent) were females, and around 87.3 percent of them were Hindu by religion. Among the study participants, around 66.74 percent mentioned that they had used a cell phone while driving. Around 38 percent of the study participants admitted that they had been in or were near-crash circumstances during the past year. Around 87 percent of the study participants who had a crash or near-crash in the past year admitted that they had been distracted while driving. On primary analysis, religion of individuals and increased driving frequency had a statistically significant association with a history of distracted driving. Study participants who were using three- and four-wheelers and those who used cellphones while driving had a statistically significant association with a history of distracted driving. According to the regression, cell phone users are 3.915 times more distracted than those who do not use cellphones (p = 0.001). Subjects with a history of crashes or near-crashes were 56.96 times more distracted than those without a history (p < 0.001). Conclusion In the present study, three-fourths of the study population used a cell phone while driving. More than three-fourths of the study participants admitted that they had been in a crash or near-crash circumstances during the past year. Distracted driving was responsible for four-fifths of all crashes or near-collisions. Use of a cell phone while driving and a history of near-crashes have a higher risk of distraction than those who do not.

Distraction "Hangover": Characterization of the Delayed Return to Baseline Driving Risk After Distracting Behaviors

OBJECTIVE

We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover).

BACKGROUND

Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time.

METHOD

Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking.

RESULTS

The participants' lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction.

CONCLUSION

Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting.

APPLICATION

Public education should include the dangers of any smartphone use during driving, including hands-free.

Understanding the domain of driving distraction with knowledge graphs

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including "objective factors", "human factors", "research methods", "data" and "data science". The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

Does intraocular straylight predict night driving visual performance? Correlations between straylight levels and contrast sensitivity, halo size, and hazard recognition distance with and without glare

Purpose

To evaluate the relationship between intraocular straylight perception and: (i) contrast sensitivity (CS), (ii) halo size, and (iii) hazard recognition distance, in the presence and absence of glare.

Subjects and methods

Participants were 15 (5 female) ophthalmologically healthy adults, aged 54.6-80.6 (median: 67.2) years. Intraocular straylight (log s) was measured using a straylight meter (C-Quant; Oculus GmbH, Wetzlar, Germany). CS with glare was measured clinically using the Optovist I device (Vistec Inc., Olching, Germany) and also within a driving simulator using Landolt Cs. These were presented under both static or dynamic viewing conditions, and either with or without glare. Hazard detection distance was measured for simulated obstacles of varying contrast. For this, the participant was required to maintain a speed of 60 km/h within a custom-built nighttime driving simulator. Glare was simulated by LED arrays, moved by cable robots to mimic an oncoming car's headlights. Halo size ("halometry") was measured by moving Landolt Cs outward originating from the center of a static glare source. The outcome measure from "halometry" was the radius of the halo (angular extent, in degrees visual angle).

Results

The correlation between intraocular straylight perception, log s, and hazard recognition distance under glare was poor for the low contrast obstacles (leading/subdominant eye: r = 0.27/r = 0.34). Conversely, log CS measured with glare strongly predicted hazard recognition distances under glare. This was true both when log CS was measured using a clinical device (Optovist I: r = 0.93) and within the driving simulator, under static (r = 0.69) and dynamic (r = 0.61) conditions, and also with "halometry" (r = 0.70). Glare reduced log CS and hazard recognition distance for almost all visual function parameters.

Conclusion

Intraocular straylight was a poor predictor of visual function and driving performance within this experiment. Conversely, CS was a strong predictor of both hazard recognition and halo extent. The presence of glare and motion lead to a degradation of CS in a driving simulator. Future studies are necessary to evaluate the effectiveness of all above-mentioned vision-related parameters for predicting fitness to drive under real-life conditions.

How many crashes does cellphone use contribute to? Population attributable risk of cellphone use while driving

BACKGROUND

Cellphone distraction is a major contributing factor for traffic crashes, a leading cause of death worldwide. The novel naturalistic driving study (NDS) study with continuously collected in situ driving videos provides an opportunity to accurately estimate the safety impact of cellphone distraction.

METHODS

We apply a case-cohort study design to the Second Strategic Highway Research Program NDS, the largest NDS up-to-date with more than 3400 participants. The data include with 842 level 1-3 crashes and 19,338 randomly selected control driving segments. We propose a partial Population Attributable Risk (PAR) estimator that provides consistent and stable estimation over time and across different driving behaviors.

RESULTS

The US population-adjusted PAR show that 8% of crashes (PAR = 0.08, 95 %CI: [0.06, 0.19]) can be reduced if cellphone distraction were switched to sober, alert, and attentive driving behavior. Young adults (age 20-29 years) and middle-aged drivers (age 30-64 years) each contribute 39% of the population level PAR. Within each age group, the PARs vary substantially from 18% for young adult drivers to 5% for middle-aged drivers. The contribution of cellphone visual-manual tasks to crashes is more than 4 times larger than cellphone talking and accounts for 87.5% of cellphone-related crashes (PAR = 0.07).

CONCLUSIONS

Cellphone distraction contributes to a considerable part of crashes. Young drivers are more susceptible to the influence of cellphone distraction and visual-manual distraction accounts for the majority of cellphone-related crashes.

Analysis of distracted driving crashes in New Jersey using mixed logit model

INTRODUCTION

Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables.

METHOD

In this study, we analyzed data on five years (2015-2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity.

RESULTS

We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it.

PRACTICAL APPLICATIONS

These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.

How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?

INTRODUCTION

Distracted driving has been well researched, however the comparison between different age-gender groups on the impact of distracted driving has not been explored. Most crash analysis research does not distinguish driver responsibility, so the role that distractions has in at-fault crashes is unknown. Without distinguishing at-fault crashes from all-cause crashes, distracted driving's detrimental effects could be underestimated.

OBJECTIVE

This study aims to systematically assess the risk of at-fault crashes associated with different sources of distraction among six groups by driver age (Teens 16-19, Adults 20-64, Seniors 65+) and gender.

METHODS

Crashes where a study participant was deemed at fault were identified using human expert annotated variables from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study dataset. Generalized linear mixed models were performed to assess the adjusted odds ratios of 10 distraction types associated with the at-fault crashes while controlling for environmental factors.

RESULTS

The main findings are (1) The highest contributing distraction types in at-fault crashes were In-Cabin Objects, Mobile Device, External Scenes, and In-Vehicle Information Systems (IVIS) as indicated by their influence on multiple age-gender groups and the magnitude of odds ratios; (2) Teens and adults were more distraction-prone than seniors, although seniors had the greatest at-fault crash risks associated with In-Cabin Objects, Mobile Device, and IVIS; (3) Distractions impacted females and males similarly; (4) At-fault crashes were more likely to have the significant distraction types present than all-cause crashes.

CONCLUSION

This study adds to the limited literature on at-fault crashes particularly as it explores the role of driver demographics and distracted driving. Analyzing the risks of distracted driving by age-gender group shows that specific activities can be riskier for a certain population. The effects of distractions may be overlooked without fault determination. Distractions by external scenes, in-vehicle technologies, and in-cabin objects should not be overlooked, in addition to mobile device use.

Mining patterns of near-crash events with and without secondary tasks

The engagement of secondary tasks, like using a phone or talking to passengers while driving, could introduce considerable risks to driving safety. This study utilizes a near-crash dataset extracted from a naturalistic driving study to explore the patterns of near-crash events with or without the involvement of secondary tasks as a surrogate approach to understand the impact of these behaviors on traffic safety. The dataset contains information about driver behaviors, such as secondary tasks, vehicle maneuvers, other conflict vehicles' maneuvers before and during near-crash events, and the driving environment. The patterns for near-crashes with or without the involvement of secondary tasks are mined by adopting the apriori association rule algorithm. Finally, the mined rules for the near-crash events with or without the involvement of the secondary tasks are analyzed and compared. The results demonstrate that near-crashes with the involvement of secondary tasks often occur with drivers in a relatively stable and presumably predictable environment, such as an interstate highway with a constant speed. This type of near-crash is highly associated with the leading vehicle's sudden slowing or stopping since there is no expectation of any interruptions for these drivers performing the secondary tasks. The most common evasive maneuver in this kind of emergency is braking. Near-crashes without the involvement of secondary tasks is often associated with lane-changing behavior and sideswipe incidents. With shorter reaction time and awareness of the driving environment, the drivers in this type of near-crash can often make more complex maneuvers, like braking and steering, to avoid a collision. Understanding the patterns of these two types of near-crash incidents could help safety researchers, traffic engineers, and even vehicle designers/engineers develop countermeasures for minimizing potential collisions caused by secondary tasks or improper lane changing behaviors.