A diary study of distracted driving behaviours

Parnell, Katie J.; Rand, James L.; Plant, Katherine L.

doi:10.1016/j.trf.2020.08.003

Cited by 27 publications

(6 citation statements)

References 39 publications

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“…This could result in improper use and unsafe behaviors. Road user behavior was identified as a prominent causal factor in road traffic accidents and was thoroughly investigated [8][9][10]. Unsafe driving behaviors can be defined as a set of implemented and systematic practices that increase the risk of road accidents.…”

Section: Introductionmentioning

confidence: 99%

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Pulvirenti

Distefano

Leonardi

et al. 2021

Safety

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This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

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Section: Introductionmentioning

confidence: 99%

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Pulvirenti

Distefano

Leonardi

et al. 2021

Safety

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show abstract

“…In addition, many scholars have examined risk-compensating behavior, which refers to the avoidance of risky behavior to initiate secondary driving tasks (e.g., DD actions). For example, studies have used surveys ( Parnell et al, 2020 ), simulations ( Li et al, 2019 ), and empirical analysis ( Oviedo-Trespalacios et al, 2017 ) to analyze factors leading to a driver's decision to engage in DD.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying regions of excess injury risks associated with distracted driving: A case study in Central Ohio, USA

Lym¹,

Kim²,

Kim³

2022

SSM - Population Health

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“…Phone usage is common during driving when the driving scenario allows it. According to Parnell et al [5],…”

Section: Introductionmentioning

confidence: 99%

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Lin

Hsu

2022

IEEE Access

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Distracted driving is the most common cause of traffic accidents. According to a World Health Organization report, the number of traffic accidents has been increasing in recent years. To address this issue, distracted-driving recognition is an important area of traffic safety research. However, distracted behavior may be a part of a driver's regular tasks. For example, sometimes a delivery person must use his/her phone while driving. The use of walkie-talkies is required for container-truck drivers because they improve unloading efficiency and reduce the time cargo ships spend in a port, resulting in cost savings. While driving on the highway, it is sometimes necessary to tune the radio to receive an update on road conditions. Furthermore, drinking water is permitted while waiting for a traffic signal for an extended period of time. Therefore, for the distracted-driving alert system, the driving scenario is important. To address this issue, we present a novel framework herein that combines driving perception and driver behavior recognition to provide the driver with appropriate warnings. By combining driver perception and behavior recognition, our proposed framework can reduce false alerts. We also define different time-to-collision standards to achieve humane and effective warnings. We try to study various behaviors and define various time-tocollision standards for making safety-level decisions. For driving perception and driver behavior recognition, a modified convolutional neural network is used, which alerts the driver immediately.

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A diary study of distracted driving behaviours

Cited by 27 publications

References 39 publications

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Identifying regions of excess injury risks associated with distracted driving: A case study in Central Ohio, USA

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

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