Characterising mobile phone calls while driving on limited-access roads based on SHRP 2 naturalistic driving data

Kreußlein, Maria; Morgenstern, Tina; Petzoldt, Tibor; Keinath, Andreas; Krems, Josef F.

doi:10.1016/j.trf.2020.03.002

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The rules for near-crash events are summarized from three aspects: (1) road: level roads (rules 9, 19, 22, 23, and 24), divided roads (median strip or barrier) (rule 3), roads with 2 to 7 lanes (rules 4, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, and 25), and straight roads (rules 10, 20) are more likely to be associated with near-crash events. (2) Driver: middle-aged and older (rules 2,5,11,12,13,14,15,16,17,18,19,20,21,22,23,24, and 25) male (rule 13) participants with an estimated average annual mileage over five years of more than 15,000 miles (rules 6, 7, 8, 9, 10, 12, and 15) are more likely to be associated with near-crash events when they are performing secondary tasks (rules 8, 18, 21, and 25). Note that driver impairments (rules 7, 17, 23, 24, and 25), driver behavior (rules 12, 23), or unexpected events (rule 6) are not correlated with near-crash events.…”

Section: Model Performance and Descriptive Statistics Of Thementioning

confidence: 99%

“…For instance, Papazikou et al [18] investigated vehicle kinematics during crashes to obtain reliable indicators of the time to collision (TTC). Kreusslein et al [19] focused on the characteristics of mobile phone calls, including the call duration, glance behavior, call type, and mobile phone location, to determine the influence of making mobile phone calls. Schlick et al [20] used hierarchical regression models to determine the associations between motor vehicle crashes and different contributing factors.…”

Section: Introductionmentioning

confidence: 99%

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Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

Liu

et al. 2022

Journal of Advanced Transportation

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This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.

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Section: Model Performance and Descriptive Statistics Of Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

Liu

et al. 2022

Journal of Advanced Transportation

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“…During the steady growth stage (2011-2018), the numbers of publications and citations increased smoothly. Large-scale naturalistic driving studies were conducted to understand driving distraction under natural conditions and brought about influential papers [71], and certain study programs with public accessibility like the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) have enabled follow-up studies worldwide till today [107][108][109]. Meanwhile, systematization was achieved with the clarification of relevant definitions [4] and the appearance of highly-cited meta-analyses [110,111].…”

Section: Trends Of the Driving Distraction Domainmentioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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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.

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Factors affecting behavior of mobile phone use while driving and effect of mobile phone use on driving performance

Phuksuksakul

Kanitpong

Chantranuwathana

2021

Accident Analysis & Prevention

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Characterising mobile phone calls while driving on limited-access roads based on SHRP 2 naturalistic driving data

Cited by 5 publications

References 32 publications

Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

Understanding the domain of driving distraction with knowledge graphs

Factors affecting behavior of mobile phone use while driving and effect of mobile phone use on driving performance

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