Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: A survey study in Tehran

Esfahani, Hossein Nasr; Arvin, Ramin; Song, Ziqi; Sze, N.N.

doi:10.1080/19439962.2019.1701166

Cited by 27 publications

(12 citation statements)

References 61 publications

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“…On the basis of the research conducted by Deery in 1999, young drivers usually overestimate their own driving skills compared to experienced drivers [30]. The most important risk-taking behaviors that are found to affect the decision-making of young drivers include the following: sleepiness, recklessness, distraction, using cell phones [31,32], following the vehicle in front very closely [33], failure to yield [34], and drug and alcohol use [35]. Young drivers also showed higher risks associated with speeding compared to other age groups, which led not only to more crashes but also to an increase in the injury severity [36].…”

Section: Literature Review 21 Youth Population Involvement In Crashesmentioning

confidence: 99%

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

et al. 2022

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Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

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Section: Literature Review 21 Youth Population Involvement In Crashesmentioning

confidence: 99%

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

et al. 2022

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“…The excessive and problematic usage of smart phones causes physical problems, for example, sleep problems (falling asleep late, awakening from sleep, etc. ; Demirci et al, 2015), eye health (Choi et al, 2018), musculoskeletal system (Kim & Kim, 2015), traffic and other severe accidents (Nasr Esfahani et al, 2019).…”

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confidence: 99%

Smart phone usage, sleep quality and depression in university students

Kaya

Daştan

Durar

2020

Int J Soc Psychiatry

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Background: The smart phone contains various mobile applications specifically targeting their contents, such as information, messages, e-mail, education and entertainment towards youths. Problematic and excessive smart phone usage can cause many health problems including anxiety, depression and sleep disorders. Aims: The aim of this study is to analyse the relationship between smart phone usage, sleep quality and depression. Methods: Eight hundred and four students who owned smart phones were given the Information Form, Smart Phone Addiction Scale-Short Version, Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI). The descriptive statistics, independent sample t-test, one-way ANOVA, correlation analysis and multivariate regression analysis were used for analysis data. Results: The mean age of the students in the sample was 20.93 ± 2.44. It comprised female (65.0%) and male (35.0%) students. All of the students used smart phones. The daily smart phone usage duration was 7.85 ± 4.55 hour. According to the multivariate linear regression analysis results, significant relationships were statistically determined in the positive way between the smart phone addiction and PSQI point ( p < .01) and BDI point ( p < .01). Conclusion: Consequently, a relationship exists between smart phone usage, poor sleep quality and depressive symptoms in university students. The university students, whose depression point is high and sleep quality is poor, should be followed up with regarding the smart phone addiction.

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“…While the driver is crossing an intersection, a severe accident occurs in front of his/her vehicle because of the sudden appearance of a pedestrian in the street. 6 While crossing an intersection, the driver faces a bus carelessly passing without observing the right of way. 7…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Improving Driving Safety by Detecting Negative Emotions with Biological Signals: Which Is the Best?

Habibifar

Salmanzadeh

2021

Transportation Research Record: Journal of the Transportation R

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There is ample evidence confirming the adverse effects of negative emotions such as anger, fear, and anxiety on drivers’ performance. Also, effectiveness of biological signals in emotion recognition has been confirmed. Therefore, developing advanced driver-assistance systems based on biological signals to detect negative emotions can play a major role in improving driving safety. However, since recording signals, data analysis, as well as design and implementation of a system based on one or more biological signals take time and are costly, it is necessary to conduct appropriate preliminary studies on the efficiency of these signals in identifying negative emotions. The purpose of this study was to explore the efficiency of four biological signals including electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA), and electroencephalogram (EEG) in detecting negative emotions while driving. To this end, a series of scenarios were designed to arouse negative emotions in the driving simulator environment. A total of 43 individuals participated in the experiments, during which the four signals were recorded. Next, we extracted 58 features from the collected data for analysis. Then, multi-layer perceptron and radial basis function neural networks were implemented using the features of each of these signals separately. Afterward, the four evaluation criteria of accuracy, sensitivity, specificity, and precision were calculated for the signals. Finally, TOPSIS was used to rank the signals. ECG and EDA signals, with 88% and 90% accuracy, respectively, were found to be the best signals in detecting negative emotions during driving.

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Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: A survey study in Tehran

Cited by 27 publications

References 61 publications

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Smart phone usage, sleep quality and depression in university students

Improving Driving Safety by Detecting Negative Emotions with Biological Signals: Which Is the Best?

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