Factors Affecting Drivers’ Cell Phone Use Behavior

Xiong, Huimin; Bao, Shan; Sayer, James R.

doi:10.3141/2434-09

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Although, studies tend to differ in the grouping of time bins, a previous observational study by Kidd et al (2016) also reported higher rates of engagement during the afternoon (11 AM–1 PM) and the evening (4:30–7 PM). In addition, several research efforts focusing solely on mobile phone use while driving have also indicated a prevalence of engagement during the afternoon periods (e.g., Xiong et al, 2014 ; Sullman et al, 2015 ). A potential explanation for this is that drivers are using the mobile phone when commuting, to make the experience more enjoyable or useful ( Jachimowicz et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

The impact of road traffic context on secondary task engagement while driving

Cuentas-Hernandez

King

et al. 2023

Front. Psychol.

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IntroductionDriver distraction has been recognized for a long time as a significant road safety issue. It has been consistently reported that drivers spend considerable time engaged in activities that are secondary to the driving task. The temporary diversion of attention from safety-critical driving tasks has often been associated with various adverse driving outcomes, from minor driving errors to serious motor vehicle crashes. This study explores the role of the driving context on a driver’s decision to engage in secondary activities non-critical to the driving task.MethodThe study utilises the Naturalistic Engagement in Secondary Tasks (NEST) dataset, a complementary dataset derived from the SHRP2 naturalistic dataset, the most extensive naturalistic study to date. An initial exploratory analysis is conducted to identify patterns of secondary task engagements in relation to context variables. Maximum likelihood Chi-square tests were applied to test for differences in engagement between types of driver distraction for the selected contextual variables. Pearson residual graphs were employed as a supplementary method to visually depict the residuals that constitute the chi-square statistic.Lastly, a two-step cluster analysis was conducted to identify common execution scenarios among secondary tasks.ResultsThe exploratory analysis revealed interesting behavioral trends among drivers, with higher engagement rates in left curves compared to right curves, while driving uphill compared to driving downhill, in low-density traffic scenarios compared to high-density traffic scenarios, and during afternoon periods compared to morning periods. Significant differences in engagement were found among secondary tasks in relation to locality, speed, and roadway design. The clustering analysis showed no significant associations between driving scenarios of similar characteristics and the type of secondary activity executed.DiscussionOverall, the findings confirm that the road traffic environment can influence how car drivers engage in distracted driving behavior.

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Section: Discussionmentioning

confidence: 99%

The impact of road traffic context on secondary task engagement while driving

Cuentas-Hernandez

King

et al. 2023

Front. Psychol.

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“…The IVBSS data are ideal for this study because the entire baseline dataset for all drivers was previously annotated for cell-phone-related behaviors for secondary data analysis purposes(e.g. [12]). Cell-phone events were divided into three categories: cell-phone interaction, dialing a cell-phone, and talking on the cell-phone.…”

Section: Video Datamentioning

confidence: 99%

Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets

Miao,

Zhang,

Flannagan

2020

Preprint

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Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While there is often a large amount of video data in NDD, only a small portion of them can be annotated by human coders and used for research, which underuses all video data. In this paper, we explored a computer vision method to automatically extract the information we need from videos. More specifically, we developed a 3D ConvNet algorithm to automatically extract cellphone-related behaviors from videos. The experiments show that our method can extract chunks from videos, most of which ( 79%) contain the automatically labeled cell phone behaviors. In conjunction with human review of the extracted chunks, this approach can find cell-phone-related driver behaviors much more efficiently than simply viewing video.

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“…This is supported by an explosion of studies. For instance, in the USA, the National Highway Safety Administration estimated that distracted driving is responsible for approximately 10% of all fatal traffic accidents [ 11 ]. Similarly, in Spain, the General Traffic Department, reported that distracted driving contributed to approximately 28% of police-reported fatal traffic accidents [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

Herrero

Febres

Boulagouas

et al. 2021

IJERPH

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Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

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Factors Affecting Drivers’ Cell Phone Use Behavior

Cited by 12 publications

References 8 publications

The impact of road traffic context on secondary task engagement while driving

The impact of road traffic context on secondary task engagement while driving

Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets

Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

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