Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This ...Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This may result in risky behaviors,e.g.,speeding and fatigued driving.Previous research on tunnel driving behaviors mainly focuses on visual factors,neglecting the impacts of nonstationary time-series combined parameters on risky driving.Firstly,30 drivers were recruited to carry out the real test.Then,based on the evolution of time series,drawing inspiration from the concept of lineage in biology,and considering multiple driving performance indicators,driving behavior chains and the feature spectrum were constructed.The characteristics of the behavior spectrum were divided into six groups:electroencephalogram,heart rate,eye movement,speed,steering,and carfollowing behaviors.Subsequently,the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors.The study deeply explored the inducing mechanism,hidden patterns,and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’attributes.Finally,the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting(AdaBoost)and random forest(RF)algorithms.The synthetic minority over-sampling technique(SMOTE)and adaptive synthetic sampling(ADASYN)were employed for oversampling.The results indicate that the ADASYN-RF algorithm outperformed others,achieving a precise recall rate area under the curve(AUPRC)of 0.978 when using the spectral radius of the speed and steering groups as input variables.These findings offer theoretical guidance for developing tunnel traffic safety strategies.展开更多
Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group in...Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group included drivers with psychological disorders and the control group included those without any disorders.The inclusion criteria for selecting patients were:active driving at the time of the study,being 18-65 years old,having a driving license,having a psychological disorder including depression,bipolar disorder,anxiety spectrum disorder,or psychotic disorder spectrum confirmed by a psychiatrist,and completing an informed consent form.The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions.The inclusion criteria for selecting the healthy cases were:active driving at the time of the study,being 18-65 years old,having a driving license,lack of any past or present history of psychiatric problems,and completing an informed consent form.The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire.First,partition around medoids method was used to extract clusters of RDB.Then,backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.Results:The sample comprised of 344(153 with psychological disorder and 191 without confirmed psychological disorder)drivers.Backward elimination logistic regression on total data revealed that share of medical expenditure≤10%of total household expenditure(OR=3.27,95%Cl:1.48-7.24),psychological disorder(OR=3.08,95%Cl:1.67-5.70),and substance abuse class(OR=6.38,95%CI:3.55-11.48)wereassociatedwithhighlevelof RDB.Conclusion:Substance abuse,psychological illnesses,and share of medical costs from total household expenditure were found to be main predictors of RDB.Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.展开更多
基金supported by the National Natural Science Foundation of China(No.51978069)Science and Technology Project of Shandong Transportation Department(No.2022-KJ-044)+1 种基金Key Research and Development Plan of Shaanxi Province(No.2021KWZ-09)the Fundamental Research Funds for the Centrl Universities,CHD(No.300102342202).
文摘Expressway tunnels are semi-enclosed structures characterized by monotonous alignment transitions and unique lighting environments,which can easily lead to drivers developing constrained and irritable psychology.This may result in risky behaviors,e.g.,speeding and fatigued driving.Previous research on tunnel driving behaviors mainly focuses on visual factors,neglecting the impacts of nonstationary time-series combined parameters on risky driving.Firstly,30 drivers were recruited to carry out the real test.Then,based on the evolution of time series,drawing inspiration from the concept of lineage in biology,and considering multiple driving performance indicators,driving behavior chains and the feature spectrum were constructed.The characteristics of the behavior spectrum were divided into six groups:electroencephalogram,heart rate,eye movement,speed,steering,and carfollowing behaviors.Subsequently,the spectral analysis using the spectral radius property of matrix theory revealed the distinctive characteristics of risky driving behaviors.The study deeply explored the inducing mechanism,hidden patterns,and rules of risky driving behaviors under the coupling effect of tunnel environment and drivers’attributes.Finally,the significant features that influence driving behaviors were used as the input variables for constructing identification models using the adaptive boosting(AdaBoost)and random forest(RF)algorithms.The synthetic minority over-sampling technique(SMOTE)and adaptive synthetic sampling(ADASYN)were employed for oversampling.The results indicate that the ADASYN-RF algorithm outperformed others,achieving a precise recall rate area under the curve(AUPRC)of 0.978 when using the spectral radius of the speed and steering groups as input variables.These findings offer theoretical guidance for developing tunnel traffic safety strategies.
基金supported by the National Institute for Medical Research Development(NIMAD)under Grant No.4000217。
文摘Purpose:This study aimed to investigate the possible association between psychological disorders and riskydriving behavior(RDB)in Iran.Methods:This case-control study conducted in Shiraz,Iran in 2021.The case group included drivers with psychological disorders and the control group included those without any disorders.The inclusion criteria for selecting patients were:active driving at the time of the study,being 18-65 years old,having a driving license,having a psychological disorder including depression,bipolar disorder,anxiety spectrum disorder,or psychotic disorder spectrum confirmed by a psychiatrist,and completing an informed consent form.The exclusion criterion was the existence of conditions that interfered with answering and understanding the questions.The inclusion criteria for selecting the healthy cases were:active driving at the time of the study,being 18-65 years old,having a driving license,lack of any past or present history of psychiatric problems,and completing an informed consent form.The data were gathered using a researcher-made checklist and Manchester driving behavior questionnaire.First,partition around medoids method was used to extract clusters of RDB.Then,backward logistic regression was applied to investigate the association between the independent variables and the clusters of RDB.Results:The sample comprised of 344(153 with psychological disorder and 191 without confirmed psychological disorder)drivers.Backward elimination logistic regression on total data revealed that share of medical expenditure≤10%of total household expenditure(OR=3.27,95%Cl:1.48-7.24),psychological disorder(OR=3.08,95%Cl:1.67-5.70),and substance abuse class(OR=6.38,95%CI:3.55-11.48)wereassociatedwithhighlevelof RDB.Conclusion:Substance abuse,psychological illnesses,and share of medical costs from total household expenditure were found to be main predictors of RDB.Further investigations are necessary to explain the impact of different psychological illnesses on driving behavior.
