Carrying a passenger and relaxation before driving: Classification of young drivers’ physiological activation

Meteier, Quentin; Capallera, Marine; Salis, Emmanuel de; Widmer, Marino; Angelini, Leonardo; Khaled, Omar Abou; Mugellini, Elena; Sonderegger, Andreas

doi:10.14814/phy2.15229

Cited by 8 publications

(6 citation statements)

References 77 publications

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“…In the same vein, it can be assumed that the presence of a passenger may lead to increased stress and thus reduced driving performance. 28 However, we cannot make any assumptions about the risky role of passenger presence, which is similar to the finding of the study conducted by Orsi et al They concluded that young drivers, carrying passengers, were particularly vulnerable in single-vehicle collisions; yet, for adult drivers, this collision was more harmful if the driver was alone in the vehicle, 16 which is in line with the results regarding the relationship between lightning status and collision type. The studies assessing rear-end crash exposure methodology revealed that daytime was attributed to many rear-end collisions.…”

Section: Discussionsupporting

confidence: 86%

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

et al. 2023

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Background: Determining suburban area crashes’ risk factors may allow for early and operative safety measures to find the main risk factors and moderating effects of crashes. Therefore, this paper has focused on a causal modeling framework. Study Design: A cross-sectional study. Methods: In this study, 52524 suburban crashes were investigated from 2015 to 2016. The hybrid-random-forest-generalized-path-analysis technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators. Results: This study analyzed 42 explanatory variables using a RF model, and it was found that collision type, distinct, driver misconduct, speed, license, prior cause, plaque description, vehicle maneuver, vehicle type, lighting, passenger presence, seatbelt use, and land use were significant factors. Further analysis using g-Path demonstrated the mediating and predicting roles of collision type, vehicle type, seatbelt use, and driver misconduct. The modified model fitted the data well, with statistical significance ( χ230 =81.29, P<0.001) and high values for comparative-fit-index and Tucker-Lewis-index exceeding 0.9, as well as a low root-mean-square-error-of-approximation of 0.031 (90% confidence interval: 0.030-0.032). Conclusion: The results of our study identified several significant variables, including collision type, vehicle type, seatbelt use, and driver misconduct, which played mediating and predicting roles. These findings provide valuable insights into the complex factors that contribute to collisions via a theoretical framework and can inform efforts to reduce their occurrence in the future.

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Section: Discussionsupporting

confidence: 86%

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

et al. 2023

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“…The presence of passengers may reduce attention to the driving task and exert direct or indirect psychological pressure to drive on less safe roads. In the same vein, it can be assumed that the presence of a passenger may lead to increased stress and, thus, reduced driving performance [ 20 ]. In addition, pedestrians are highly likely to be more vulnerable compared to other road users because they are less protected than the occupants of closed vehicles.…”

Section: Resultsmentioning

confidence: 99%

Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

2022

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Background Prevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety. Methods In this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015—March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression. Results According to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54–5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75–3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52–1.76)), rainy weather (OR = 1.32, 95%CI = (1.06–1.64)), no intersection control (OR = 1.40, 95%CI = (1.29–1.51)), double solid line(OR = 2.21, 95%CI = (1.31–3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39–2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93–2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50–1.92)), human factor (OR = 1.13, 95%CI = (1.03–1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04–3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27–1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20–1.51)), head-on collision(OR = 3.35, 95%CI = (2.85–3.93)), tourist destination(OR = 1.95, 95%CI = (1.69–2.24)), suburban areas(OR = 3.26, 95%CI = (2.65–4.01)), expressway(OR = 1.84, 95%CI = (1.59–2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63–2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26–1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67–2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26–1.56)), dark color (OR = 1.26, 95%CI = (1.17–1.35)), old vehicle(OR = 1.46, 95%CI = (1.27–1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42–3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72–5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33–1.88)), low income(OR = 2.48, 95%CI = (1.95–3.15)), old age (OR = 1.67, 95%CI = (1.44–1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51–6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44–1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44–1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29–2.76)). Conclusion This study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

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“…In order to be as close as possible to reality, the physiological signals of drivers corresponding to the four selected risk factors were collected to train machine learning algorithms. Several experiments were designed and carried out to manipulate the selected risk factors in driving simulator experiments [35], [44]. Table 1 summarizes the three user experiences conducted.…”

Section: ) Data Collectionmentioning

confidence: 99%

“…All machine learning models were trained using the same pipeline implemented in Python with the scikit learn framework [45]. The pipeline is described in detail in [35] and [44].…”

Section: ) Training Machine Learning Modelsmentioning

confidence: 99%

A Contextual Multimodal System for Increasing Situation Awareness and Takeover Quality in Conditionally Automated Driving

et al. 2023

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Carrying a passenger and relaxation before driving: Classification of young drivers’ physiological activation

Cited by 8 publications

References 77 publications

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas

Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

A Contextual Multimodal System for Increasing Situation Awareness and Takeover Quality in Conditionally Automated Driving

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