The prevalence of and crash risk associated with primarily cognitive secondary tasks

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2659-22 ◽

2017 ◽

Vol 2659 (1) ◽

pp. 204-211 ◽

Cited By ~ 2

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.

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Predicting Relative Crash Risk from the Attentional Effects of the Cognitive Demand of Visual-Manual Secondary Tasks

10.4271/2017-01-1384 ◽

2017 ◽

Cited By ~ 2

Author(s):

Richard Young

Keyword(s):

Cognitive Demand ◽

Crash Risk ◽

Secondary Tasks

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Glances That Matter: Applying Quantile Regression to Assess Driver Distraction from Off-Road Glances

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931218621443 ◽

2018 ◽

Vol 62 (1) ◽

pp. 1954-1958 ◽

Cited By ~ 3

Author(s):

Shu-Yuan Liu ◽

John D. Lee ◽

Ja Young Lee ◽

Vindhya Venkatraman

Keyword(s):

Quantile Regression ◽

Secondary Task ◽

Task Type ◽

Driver Distraction ◽

Crash Risk ◽

System Delay ◽

Secondary Tasks ◽

Text Length ◽

Task Features ◽

Conditional Means

This study assessed whether quantile regression can identify design specifications that lead to particularly long glances, which might go unnoticed with traditional analyses focusing on conditional means of off-road glances. Although substantial research indicates that long glances contribute disproportionately to crash risk, few studies have directly assessed the tails of the distribution. Failing to examine the distribution tails might underestimate the disproportionate risk on long glances imposed by secondary tasks. We applied quantile regression to assess the effects of secondary task type (reading or entry), system delay (delay or no delay), and text length (long or short) on off-road glance duration at 15th, 50th, and 85th quantiles. The results show that entry task, long text, and some combinations of variables led to longer glances than that would be expected given the central tendency of glance distributions. Quantile regression identifies secondary task features that produce long glances, which might be neglected by traditional analyses with conditional means.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211043817 ◽

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.

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Prevalence of Engagement in Single versus Multiple Types of Secondary Tasks: Results from the Naturalistic Engagement in Secondary Task (NEST) Dataset

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118791394 ◽

2018 ◽

Vol 2672 (37) ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Martina Risteska ◽

Birsen Donmez ◽

Huei-Yen W. Chen ◽

Miti Modi

Keyword(s):

Secondary Task ◽

Statistical Significance ◽

Descriptive Analysis ◽

Time Frame ◽

Crash Risk ◽

Secondary Tasks ◽

Environmental Demand ◽

Task Types ◽

Short Time ◽

Single Versus

We investigated engagement in single vs. multiple types of secondary tasks in distraction-affected, safety-critical events (SCEs), i.e., crashes/near-crashes, and baselines reported in the Naturalistic Engagement in Secondary Tasks (NEST) dataset. NEST was created from Second Strategic Highway Research Program (SHRP2) data for studying distractions in detail. Early descriptive analysis on NEST found that most distraction-affected SCE and baseline epochs (10 s long) include more than one type of secondary task, suggesting that a considerable number of drivers may be engaging in multiple secondary activities within a relatively short time frame, potentially being exposed to increased demands brought on by multi-tasking and task-switching. We conducted inferential statistics on NEST focusing on engagement in single vs. multiple types of tasks across SCEs and baselines. A logit model was built to compare the odds of engaging in single vs. multiple types of tasks with the following predictors: event type (SCE, baseline), environmental demand, GPS speed, and driver age. The last three predictors were included to capture the driving demands experienced, which may have impacted drivers’ task engagement behavior. Odds of engagement in multiple types of secondary tasks was higher in SCEs than baselines. Furthermore, with marginal statistical significance, drivers 65 years and over were less likely to engage in multiple types of secondary tasks than younger drivers. Overall, engagement in multiple secondary task types is more prevalent in SCEs. Most crash risk studies to date have reported the effects associated with one type of secondary task. However, it appears that these effects may be confounded by the presence of other secondary tasks.

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