The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles befo...The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles before and after the moment when flashing green started was compared using paired-samples T-test. The critical distances between go and stop decisions was defined through cumulative percentage curve. The boundary of dilemma zone was determined by comparing stop distance and travel distance.Amber-running violation was analyzed on the basis of the travel time to the stop line. And finally, a logistic model for stop and go decisions was constructed. The results shows that the stopping ratios of the first vehicles of west-bound and east-bound approaches are 41.3% and 39.8%, respectively; the amber-light running violation ratios of two approaches are 31.6% and 25.4%, respectively;the operating speed growth ratios of first vehicles selecting to cross intersection after the moment when flashing green started are26.7% and 17.7%, respectively; and the critical distances are 48 m and 46 m, respectively, which are close to 44 m, the boundary of dilemma zone. The developed decision models demonstrate that the probability of go decision is higher when the distance from the stop line is shorter or operating speed is higher. This indicates that flashing green is an effective way to enhance intersection safety,but it should work together with a strict enforcement. In addition, traffic signs near critical distance and reasonable speed limitation are also beneficial to the safety of intersections.展开更多
The construction industry is one of the major producers of municipal solid waste.Although there are many studies in municipal solid waste management,the research on the recovery of recyclable building material from co...The construction industry is one of the major producers of municipal solid waste.Although there are many studies in municipal solid waste management,the research on the recovery of recyclable building material from construction sites remains limited.This paper addresses the optimal design issue of the construction and demolition(C&D)waste logistics network based on the features of the construction industry from the contractors’perspective.The purpose of this paper is to provide an optimal C&D waste recycling network decision(RND)model considering the change of construction sites location over time.A multi-period and multi-objective mixed-integer linear programming model was developed to minimize the cost of C&D waste disposal for contractors,and to minimize the carbon emissions from C&D waste transportation.An application study was conducted to assess the performance of the RND model.Through some sensitivity analysis experiments based on an immune genetic algorithm,the influences of environmental policies and carbon tax policy on improving the recycling rate of C&D waste and reduce the carbon emission were explored.The findings of this research suggest that:(1)a RND model with the feature of the construction industry developed in this paper can effectively optimize the C&D waste logistics network;(2)government policies and laws are valid political instruments to improve the recycling rate of C&D waste;(3)the carbon-tax analyses demonstrate that a carbon tax policy can effectively reduce carbon emissions.展开更多
The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics h...The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.展开更多
基金Project(51208451)supported by the National Natural Science Foundation of ChinaProject(10KJB580004)supported by the Natural Science Foundation for Colleges and Universities of Jiangsu Province,ChinaProject supported by the New Century Talents Project of Yangzhou University,China
文摘The primary objective of this work is to explore how drivers react to flashing green at signalized intersections. Through video taping and data procession based on photogrammetry, the operating speeds of vehicles before and after the moment when flashing green started was compared using paired-samples T-test. The critical distances between go and stop decisions was defined through cumulative percentage curve. The boundary of dilemma zone was determined by comparing stop distance and travel distance.Amber-running violation was analyzed on the basis of the travel time to the stop line. And finally, a logistic model for stop and go decisions was constructed. The results shows that the stopping ratios of the first vehicles of west-bound and east-bound approaches are 41.3% and 39.8%, respectively; the amber-light running violation ratios of two approaches are 31.6% and 25.4%, respectively;the operating speed growth ratios of first vehicles selecting to cross intersection after the moment when flashing green started are26.7% and 17.7%, respectively; and the critical distances are 48 m and 46 m, respectively, which are close to 44 m, the boundary of dilemma zone. The developed decision models demonstrate that the probability of go decision is higher when the distance from the stop line is shorter or operating speed is higher. This indicates that flashing green is an effective way to enhance intersection safety,but it should work together with a strict enforcement. In addition, traffic signs near critical distance and reasonable speed limitation are also beneficial to the safety of intersections.
基金financial support provided by Fundamental Research Funds for the Central Universities-China(No.2018CDJSK03XK15)project support by the National Planning Office of Philosophy and Social Science Foundation of China-China(No.18BJY06).
文摘The construction industry is one of the major producers of municipal solid waste.Although there are many studies in municipal solid waste management,the research on the recovery of recyclable building material from construction sites remains limited.This paper addresses the optimal design issue of the construction and demolition(C&D)waste logistics network based on the features of the construction industry from the contractors’perspective.The purpose of this paper is to provide an optimal C&D waste recycling network decision(RND)model considering the change of construction sites location over time.A multi-period and multi-objective mixed-integer linear programming model was developed to minimize the cost of C&D waste disposal for contractors,and to minimize the carbon emissions from C&D waste transportation.An application study was conducted to assess the performance of the RND model.Through some sensitivity analysis experiments based on an immune genetic algorithm,the influences of environmental policies and carbon tax policy on improving the recycling rate of C&D waste and reduce the carbon emission were explored.The findings of this research suggest that:(1)a RND model with the feature of the construction industry developed in this paper can effectively optimize the C&D waste logistics network;(2)government policies and laws are valid political instruments to improve the recycling rate of C&D waste;(3)the carbon-tax analyses demonstrate that a carbon tax policy can effectively reduce carbon emissions.
基金supported by the National Natural Science Foundation of China Key Project under Grant No.70933003the National Natural Science Foundation of China under Grant Nos.70871109 and 71203247
文摘The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
