Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating facto...Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating factors associated with speeding behavior among long-haul truck drivers(LHTDs),especially in developing nations like India.This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India.A cross-sectional study was conducted among LHTDs in Salem City,Tamil Nadu,India.The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic,work,vehicle,health-related lifestyle,and speeding-related characteristics.A total of 756 valid samples were collected and utilized for analysis purposes.While conventional statistical methods like binary logit technique lacked prediction capabilities,machine learning(ML)algorithms including decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)were employed to model speeding behavior among LHTDs.The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy(0.80),F1 score(0.77),and AUROC(0.81).From the befitting RF model,the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot.Pressured delivery of goods,sleeping duration per day,age of truck,size of truck,monthly income,driving experience,driving duration per day,and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs.Based on the developed RF model,the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots(PDPs).The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.展开更多
As per statistics,two-wheeler(TW)alone shares the highest number of vehicle registrations in India,which develops the various transportation-related issues such as traffic conflicts,congestions,and pollutions.Electric...As per statistics,two-wheeler(TW)alone shares the highest number of vehicle registrations in India,which develops the various transportation-related issues such as traffic conflicts,congestions,and pollutions.Electric two-wheelers(E-TW)are a better alternative to conventional two-wheelers because of their significant advantage in mitigating environmental impacts.But E-TWs are less attractive among road users due to unawareness of the benefits of E-TW.In addition,the traditional methods are less accurate in predicting users’mode choice behavior because of their limitations.Therefore,there is a need to conduct a study to understand the road user’s willingness to adopt E-TW and find a suitable method for predicting mode choice behavior accurately.This study analyzes the Indian road users encouraging and discouraging factors to adopt E-TW and investigates the application of non-traditional models for estimating mode shift behaviour towards E-TW.Based on the literature review and expert opinion,a detailed questionnaire form was framed,and a total of 522 samples were collected from four states of India.The data findings show that Indian road users prefer TW compared to public transport,private four-wheeler,paratransit,and non-motorized transport because of its easy to ride,low maintenance,fast and convenient travel nature.The environmental concern of reducing air pollution and lower vehicle operating costs are significant factors that encourage E-TW adoption.However,the non-availability of charging infrastructure,lower speed,higher initial purchase cost,and lack of awareness about EVs are the significant discouraging factors in adopting ETW in India.Further,Machine Learning(ML)methods were adopted to predict the mode shift behaviour from the fuel based TW to E-TW,and the results were compared with the Binary Logit(BL)method.The model results indicated that Support vector machine predicted the mode shift behavior with the highest accuracy rate compared with other methods such as Artificial Neural Network,K-Nearest Neighbor,Random Forest,and BL.The outcome of this study would help the transportation planner,EV manufacturers,researchers,and policymakers to understand the Indian user’s preference to adopt E-TW.展开更多
Road traffic injuries and crashes are one of the major public concerns contributing to mortality and morbidity figures across the globe.Researchers estimated that around 90%of all causative factors for crashes are att...Road traffic injuries and crashes are one of the major public concerns contributing to mortality and morbidity figures across the globe.Researchers estimated that around 90%of all causative factors for crashes are attributed to road users of which drivers are the principal controlling elements.Therefore,understanding complex human driver behavior and their possible violations or errors are necessary to control and prevent accident occurrence to a considerable extent.Studies on driver behavior of commercial vehicles such as trucks are scattered widely and scarcely explored hindering the possibility of road safety outcomes.This underscores the need to excavate and synthesize the past studies for an effective understanding of human factors causing truck crashes.In this paper,an attempt has been made to systematically review the pieces of literature and to identify the causative factors affecting truck driver behavior.The trend of studies shows a promising framework for improving truck driver safety on taking care of human factors influencing crashes.Most kinds of literature have cited unsafe driving behaviors as a predominant source of truck crashes.The outcomes of this research can be utilized by transportation firms and stakeholders for identifying the possible lags to develop pragmatic and possible effective preventive measures featuring truck driver safety.展开更多
文摘Speeding is one of the most common aberrant driving behaviors among the driving population.Although research on speeding behavior among drivers has increased over the decades,little is known about the motivating factors associated with speeding behavior among long-haul truck drivers(LHTDs),especially in developing nations like India.This study aims to develop a prediction model for speeding behavior and to identify the contributory factors and their influential patterns underlying speeding behavior among LHTDs in India.A cross-sectional study was conducted among LHTDs in Salem City,Tamil Nadu,India.The data were collected through face-to-face interviews using a questionnaire encompassing socio-demographic,work,vehicle,health-related lifestyle,and speeding-related characteristics.A total of 756 valid samples were collected and utilized for analysis purposes.While conventional statistical methods like binary logit technique lacked prediction capabilities,machine learning(ML)algorithms including decision tree(DT),random forest(RF),adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)were employed to model speeding behavior among LHTDs.The analysis results showed that RF demonstrated superior performance in predicting speeding behavior over other competing algorithms with accuracy(0.80),F1 score(0.77),and AUROC(0.81).From the befitting RF model,the importance of factors contributing to speeding behavior among LHTDs was determined through the variable importance plot.Pressured delivery of goods,sleeping duration per day,age of truck,size of truck,monthly income,driving experience,driving duration per day,and age of the driver were identified as the eight topmost critical factors contributing to speeding behavior among LHTDs.Based on the developed RF model,the hidden relationships behind identified critical factors in relation to the speeding behavior were investigated using partial dependence plots(PDPs).The outcomes of this research will be useful for road safety authorities and Indian trucking industries to frame suitable policies and to introduce effective strategies for mitigating speeding behavior among LHTDs to promote road safety.
文摘As per statistics,two-wheeler(TW)alone shares the highest number of vehicle registrations in India,which develops the various transportation-related issues such as traffic conflicts,congestions,and pollutions.Electric two-wheelers(E-TW)are a better alternative to conventional two-wheelers because of their significant advantage in mitigating environmental impacts.But E-TWs are less attractive among road users due to unawareness of the benefits of E-TW.In addition,the traditional methods are less accurate in predicting users’mode choice behavior because of their limitations.Therefore,there is a need to conduct a study to understand the road user’s willingness to adopt E-TW and find a suitable method for predicting mode choice behavior accurately.This study analyzes the Indian road users encouraging and discouraging factors to adopt E-TW and investigates the application of non-traditional models for estimating mode shift behaviour towards E-TW.Based on the literature review and expert opinion,a detailed questionnaire form was framed,and a total of 522 samples were collected from four states of India.The data findings show that Indian road users prefer TW compared to public transport,private four-wheeler,paratransit,and non-motorized transport because of its easy to ride,low maintenance,fast and convenient travel nature.The environmental concern of reducing air pollution and lower vehicle operating costs are significant factors that encourage E-TW adoption.However,the non-availability of charging infrastructure,lower speed,higher initial purchase cost,and lack of awareness about EVs are the significant discouraging factors in adopting ETW in India.Further,Machine Learning(ML)methods were adopted to predict the mode shift behaviour from the fuel based TW to E-TW,and the results were compared with the Binary Logit(BL)method.The model results indicated that Support vector machine predicted the mode shift behavior with the highest accuracy rate compared with other methods such as Artificial Neural Network,K-Nearest Neighbor,Random Forest,and BL.The outcome of this study would help the transportation planner,EV manufacturers,researchers,and policymakers to understand the Indian user’s preference to adopt E-TW.
文摘Road traffic injuries and crashes are one of the major public concerns contributing to mortality and morbidity figures across the globe.Researchers estimated that around 90%of all causative factors for crashes are attributed to road users of which drivers are the principal controlling elements.Therefore,understanding complex human driver behavior and their possible violations or errors are necessary to control and prevent accident occurrence to a considerable extent.Studies on driver behavior of commercial vehicles such as trucks are scattered widely and scarcely explored hindering the possibility of road safety outcomes.This underscores the need to excavate and synthesize the past studies for an effective understanding of human factors causing truck crashes.In this paper,an attempt has been made to systematically review the pieces of literature and to identify the causative factors affecting truck driver behavior.The trend of studies shows a promising framework for improving truck driver safety on taking care of human factors influencing crashes.Most kinds of literature have cited unsafe driving behaviors as a predominant source of truck crashes.The outcomes of this research can be utilized by transportation firms and stakeholders for identifying the possible lags to develop pragmatic and possible effective preventive measures featuring truck driver safety.
