To effectively mitigate the short-term fatigue effects of driving in extra-long tunnels,this study conducted natural driving experiments in five extra-long tunnels of varying lengths and tunnel group sections.Utilizin...To effectively mitigate the short-term fatigue effects of driving in extra-long tunnels,this study conducted natural driving experiments in five extra-long tunnels of varying lengths and tunnel group sections.Utilizing data obtained from natural driving fatigue experiments,this study identified perclos P80,variable coefficient of pupil diameter,and acceleration as fatigue sensitivity indicators,determined through significance tests of difference and correlation analysis.This study employed an ordered multi-class Logistic model to investigate the factors that influence driving fatigue in extra-long tunnels.The most significant variable in the model was perclos P80,which served as an indicator for classifying and identifying fatigue levels in extra-long tunnels.Following this,a dimensionless quantitative metric,the Fatigue Driving Degree,was formulated,and the Threshold of Driving Fatigue was established.Using the quantitative framework for driving fatigue,this paper standardized the definition of the fatigue arousal zone in extra-long tunnels.The study analyzed the operational principles and validated the key parameters of the fatigue arousal zone in extra-long tunnels.These parameters encompass the placement location,length,form,and traffic induction design of the fatigue arousal zone.The research findings can serve as a theoretical reference for the development of fatigue arousal technology in extra-long highway tunnels in China.展开更多
Long departure-taxi-out time leads to significant airport surface congestion, fuel-burn costs, and excessive emissions of greenhouse gases. To reduce these undesirable effects, a Predicted taxi-out time-based Dynamic ...Long departure-taxi-out time leads to significant airport surface congestion, fuel-burn costs, and excessive emissions of greenhouse gases. To reduce these undesirable effects, a Predicted taxi-out time-based Dynamic Pushback Control(PDPC) method is proposed. The implementation of this method requires two steps: first, the taxi-out times for aircraft are predicted by the leastsquares support-vector regression approach of which the parameters are optimized by an introduced improved Firefly algorithm. Then, a dynamic pushback control model equipped with a linear gate-hold penalty function is built, along with a proposed iterative taxiway queue-threshold optimization algorithm for solving the model. A case study with data obtained from Beijing International airport(PEK) is presented. The taxi-out time prediction model achieves predictive accuracy within 3 min and 5 min by 84.71% and 95.66%, respectively. The results of the proposed pushback method show that total operation cost and fuel-burn cost achieve a 14.0% and 21.1%reduction, respectively, as compared to the traditional K-control policy.(3) From the perspective of implementation, using PDPC policy can significantly reduce the queue length in taxiway and taxi-out time. The total operation cost and fuel-burn cost can be curtailed by 37.2% and 52.1%,respectively, as compared to the non-enforcement of any pushback control mechanism. These results show that the proposed pushback control model can reduce fuel-burn costs and airport surface congestion effectively.展开更多
基金This research was sponsored by Natural Science Foundation of Chongqing,China(grant number CSTB2023NSCQ-MSX0742)by the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(grant number 22YJCZH143)by the National Natural Science Foundation of China(General Program)(grant number 52172341).
文摘To effectively mitigate the short-term fatigue effects of driving in extra-long tunnels,this study conducted natural driving experiments in five extra-long tunnels of varying lengths and tunnel group sections.Utilizing data obtained from natural driving fatigue experiments,this study identified perclos P80,variable coefficient of pupil diameter,and acceleration as fatigue sensitivity indicators,determined through significance tests of difference and correlation analysis.This study employed an ordered multi-class Logistic model to investigate the factors that influence driving fatigue in extra-long tunnels.The most significant variable in the model was perclos P80,which served as an indicator for classifying and identifying fatigue levels in extra-long tunnels.Following this,a dimensionless quantitative metric,the Fatigue Driving Degree,was formulated,and the Threshold of Driving Fatigue was established.Using the quantitative framework for driving fatigue,this paper standardized the definition of the fatigue arousal zone in extra-long tunnels.The study analyzed the operational principles and validated the key parameters of the fatigue arousal zone in extra-long tunnels.These parameters encompass the placement location,length,form,and traffic induction design of the fatigue arousal zone.The research findings can serve as a theoretical reference for the development of fatigue arousal technology in extra-long highway tunnels in China.
基金partially supported by the National Natural Science Foundation of China-Civil Aviation Joint Fund(Nos.U1533203,U1233124.)
文摘Long departure-taxi-out time leads to significant airport surface congestion, fuel-burn costs, and excessive emissions of greenhouse gases. To reduce these undesirable effects, a Predicted taxi-out time-based Dynamic Pushback Control(PDPC) method is proposed. The implementation of this method requires two steps: first, the taxi-out times for aircraft are predicted by the leastsquares support-vector regression approach of which the parameters are optimized by an introduced improved Firefly algorithm. Then, a dynamic pushback control model equipped with a linear gate-hold penalty function is built, along with a proposed iterative taxiway queue-threshold optimization algorithm for solving the model. A case study with data obtained from Beijing International airport(PEK) is presented. The taxi-out time prediction model achieves predictive accuracy within 3 min and 5 min by 84.71% and 95.66%, respectively. The results of the proposed pushback method show that total operation cost and fuel-burn cost achieve a 14.0% and 21.1%reduction, respectively, as compared to the traditional K-control policy.(3) From the perspective of implementation, using PDPC policy can significantly reduce the queue length in taxiway and taxi-out time. The total operation cost and fuel-burn cost can be curtailed by 37.2% and 52.1%,respectively, as compared to the non-enforcement of any pushback control mechanism. These results show that the proposed pushback control model can reduce fuel-burn costs and airport surface congestion effectively.
