Driving Style Recognition Based on Driver Behavior Questionnaire

Li, Pengfei; Shi, Jianjun; Liu, Xiaoming

doi:10.4236/ojapps.2017.74010

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The motivation for driving while fatigued may be due to overestimation of capabilities and miscalculation of the cost of outcome by the drivers [ 64 ]. The results of several studies, similar to the present study, show that the driving experience is correlated with DBQ dimensions [ 27 , 63 , 65 – 67 ]. This may be because the driving experience is correlated with anger and violent driving behavior, thereby affecting driving behavior [ 66 , 68 ].…”

Section: Discussionsupporting

confidence: 89%

“…Circadian driving hours predicted deliberate violations and lapses, which is consistent with the results of similar studies [27,63], which is justified by fatigue induced by driving [24].…”

Section: Plos Onesupporting

confidence: 89%

“…This may be because the driving experience is correlated with anger and violent driving behavior, thereby affecting driving behavior [66,68]. The results of several studies, similar to the present study, show that the driving experience is correlated with DBQ dimensions [27,63,[65][66][67]. This may be because the driving experience is correlated with anger and violent driving behavior, thereby affecting driving behavior [66,68].…”

Section: Plos Onesupporting

confidence: 73%

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Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers

et al. 2020

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Objective Road traffic injuries (RTIs) are recognized as one of the most important causes of morbidity and mortality throughout the world, especially in developing countries. Human behavior is reportedly one of the critical factors in the occurrence of such injuries. The purpose of this study is to evaluate the correlation of abnormal driving behaviors with the frequency and severity of RTIs among drivers in Hamadan, west of Iran. Methods The present cross-sectional study was conducted on 800 people driving, who were selected by multistage cluster sampling technique. Data were collected using a three-part self-administered questionnaire including demographic, social and driving characteristics; the Manchester driver behavior questionnaire (DBQ); as well as information on a history of the occurrence of the injuries caused by the crashes and the severity of them. Data were statistically analyzed using numerical indices, linear regression analysis, Pearson correlation, ordinal logistic regression model and multinomial logistic regression. Results The highest and lowest mean percentages of abnormal driving behavior were related to unintentional violations (19.13) and Lapses (16.44), respectively. "Changing radio stations and listening to music while driving", "overtaking a driver who drives slowly", and "unintentionally exceeding the speed limit" were the three highest behaviors associated with road traffic injuries, with the mean and standard deviation of (1.93 ± 1.4), (1.90±1.4), (1.58±1.3), respectively. Age, gender, educational level, driving experience and driving hours during the day were significantly associated with DBQ dimensions and severity of road traffic injuries.

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Section: Discussionsupporting

confidence: 89%

“…Circadian driving hours predicted deliberate violations and lapses, which is consistent with the results of similar studies [27,63], which is justified by fatigue induced by driving [24].…”

Section: Plos Onesupporting

confidence: 89%

Section: Plos Onesupporting

confidence: 73%

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Dimensions of aberrant driving behaviors and their association with road traffic injuries among drivers

et al. 2020

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“…One is a subjective questionnaire survey. Li [16] et al used a standard driver behavior questionnaire on 225 non-professional drivers to determine the number of driver style categories based on the fuzzy C-means (FCM) algorithm. Liu Jing [17] and others used the Multi-Dimensional Driving Style Scale and the General Decision Style Scale by surveying 199 drivers to study the relationship between various factors and driving style.…”

Section: Introductionmentioning

confidence: 99%

Examining Bayesian network modeling in identification of dangerous driving behavior

et al. 2021

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Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.

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