The effects of age on crash risk associated with driver distraction

Motor vehicle crashes are the leading cause of death for 15 to 20-year olds. Young/novice drivers have long been thought to be vulnerable to the impact of peer passengers, and thus have a higher crash risk. It has been proven that perceived risky behavior of close friends was the best psychosocial predictor of risk. Additionally, young drivers (18-20 years) have the highest involvement in distraction-related crashes. The goal of this study was to examine the effect of social influence and driver distraction on young drivers’ behavior. Twenty-four pairs of participants took part in the study. Participants drove in pairs and by themselves while completing four distraction tasks. Results showed that the presence of a passenger did not show statistical significance related to drivers’ behavior. However, other social influence factors did significantly impact drivers’ behavior, including stimulating companionship, type of friendship, and their interactions.

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Vehicle Occupancy and Crash Risk

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105190800123 ◽

2005 ◽

Vol 1908 (1) ◽

pp. 187-194 ◽

Cited By ~ 1

Author(s):

Judy A. Geyer ◽

David R. Ragland

Keyword(s):

Reporting System ◽

Current Knowledge ◽

Seat Belt ◽

Driver Distraction ◽

Crash Risk ◽

Fatal Crash ◽

Increased Risk ◽

Seat Belt Use ◽

Teenage Drivers

This study explores the association between vehicle occupancy and a driver's risk of causing a fatal crash, not wearing a seat belt, and using alcohol. The survey population is the set of drivers represented in the Fatal Analysis Reporting System for 1992 to 2002. The independent variables are driver age, driver gender, passenger age, passenger gender, and vehicle occupancy. The outcome variables are whether the driver was at fault in causing the fatal crash, whether the driver wore a seat belt, and whether the driver had used alcohol. For male teenage drivers, driving with teenage passengers correlated with an increased risk of causing a crash. For all female drivers and for male drivers over age 40, passenger presence correlated with a reduced risk of causing a fatal crash. Drivers ages 15 to 30 were less likely to wear a seat belt when passengers were present than when driving solo. Drivers age 50 and older had higher rates of seat belt use when passengers were present. This protective effect of passengers was stronger for male drivers than female drivers, and for male drivers the effect increased by age. Drivers ages 15 to 34 accompanied by passengers were more likely to have consumed alcohol than solo drivers of the same age group. These results offer an interesting perspective for research in the area of driver distraction, and they update current knowledge on older drivers and the role of seat belt and alcohol awareness.

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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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Improve adaptive cruise control under driver distraction: Time headway compensation based on a random‐effect crash risk model

IET Intelligent Transport Systems ◽

10.1049/itr2.12092 ◽

2021 ◽

Author(s):

Penghui Li ◽

Mengxia Hu ◽

Feng Chen ◽

Chengliang Xu ◽

Chunjiao Dong ◽

...

Keyword(s):

Adaptive Cruise Control ◽

Risk Model ◽

Random Effect ◽

Driver Distraction ◽

Crash Risk ◽

Cruise Control ◽

Time Headway

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How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720812446965 ◽

2012 ◽

Vol 54 (6) ◽

pp. 1104-1116 ◽

Cited By ~ 88

Author(s):

Yulan Liang ◽

John D. Lee ◽

Lora Yekhshatyan

Keyword(s):

Motor Vehicle ◽

Driver Distraction ◽

Crash Risk ◽

Design Of Algorithms ◽

Data Set ◽

The Road ◽

Naturalistic Driving Study ◽

Naturalistic Driving ◽

The Relationship ◽

Better Than

Objective: In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Background: Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. Method: The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction—glance duration, glance history, and glance location—on how well the algorithms predicted crash risk. Results: Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history. Augmenting glance duration with other elements of glance behavior—1.5th power of duration and duration weighted by glance location—produced similar prediction performance as glance duration alone. Conclusions: The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. Application: The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

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The Effect of Cell-Phone User on Driver Attention

Volume 4: Dynamics, Control and Uncertainty, Parts A and B ◽

10.1115/imece2012-87689 ◽

2012 ◽

Author(s):

Cheng-Ken Yang ◽

Reza Langari

Keyword(s):

Cell Phone ◽

Cell Phones ◽

Transportation Systems ◽

Driver Distraction ◽

Driving Safety ◽

Transportation Safety ◽

Crash Risk ◽

Eeg Signal ◽

Traffic Fatalities ◽

Phone User

Driving safety is increasingly a major issue in transportation systems. One of major culprits in this respect is driver distraction or inattention. Driving inattention, which can be caused by drowsiness as well as the use of devices such as cell-phones while driving increases crash/near-crash risk [1]. In fact among all causes of distraction, cell phone driving continues to be a major issue that impacts transportation safety since this is a matter that can be regulated through policy decisions. The empirical evidence in this regard continues to underline the gravity of the situation. In 2009, there were 5474 people (or approximately 12% of all traffic fatalities) killed by distracted drivers, and 995 of them were considered to have been killed by those drivers who were using cell phone during driving [2]. This paper considers the relation between driving safety and cell-phone using. We propose a way to estimate drivers’ attention by measuring their electroencepholography (EEG) signal to estimate the inattention index.

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