Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable mo...Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable model is available to precisely reflect the behavior characteristics.This paper proposed and introduced a method for innovative multi-occupant air-conditioning(AC)usage behavior modelling in a multi-occupant office,which used intuitionistic fuzzy preference relationship to describe individual behavior intention and a hierarchical structure to reflect the social relationship among multiple occupants through subjective evaluation method.The group decision-making process combined the individual behavior intention and the weights of occupants using the analytic hierarchy process.Then,the AC usage behavior of a multi-occupant office was simulated by integrating the multi-occupant model into designer’s simulation toolkit(DeST)building performance simulation software.The results of conducted analysis of a single office with multi-occupant showed that the proposed multi-occupant modelling method could quantitatively characterize the group relationships and AC usage behavior patterns.The absolute errors for the total AC operation time and frequency of the start-up periods of AC between the simulation and measurement results were only 2.7%and 2.0%,respectively.Thus,the proposed multi-occupant modelling method could realize a relatively accurate simulation of the multi-occupant behavior.展开更多
In recent years,traffic safety researchers have attempted to separate single-vehicle and multi-vehicle crashes when analyzing crash severity,considering the significant differences in the mechanism of occurrence of th...In recent years,traffic safety researchers have attempted to separate single-vehicle and multi-vehicle crashes when analyzing crash severity,considering the significant differences in the mechanism of occurrence of the two crash types.However,regardless of the number of vehicles involved in a crash,the severity of a crash is defined by the most severe injury outcome sustained by the occupants,not vehicles.Thus,this study evaluated a need for conducting a separate severity analysis for crashes involving a single occupant(SO)and multiple occupants(MO).Ten-year data(2009–2018)of crashes that involved a collision between a single vehicle and a train at the highway-rail grade crossings(HRGCs)across the United States was used as a case study.Crashes were grouped based on occupancy level;that is,crashes involving SO were separated from the ones involving MO.As expected,MO crashes had higher injury and fatality rates than SO crashes.Three Multinomial Logit(MNL)models were developed to analyze the crash severity of SO crashes,MO crashes,and total crashes.The study found several differences in associated factors when SO crashes and MO crashes were modeled separately.Overall,combining SO and MO crashes tend to either underestimate or overestimate the actual impact of the predictor variable on a specific crash type.Among the variables,train speed and vehicle speed during crash showed a great difference.The findings provide evidence that the severity analysis of the SO and MO crashes should be performed separately as they have different characteristics.展开更多
基金This study was supported by the National Natural Science Founda-tion of China(Grant no.51978481)。
文摘Occupant behavior largely influence the energy use within buildings.In the multi-occupant office,occupant behavior is affected by individual preference as well as the interaction among occupants,and yet no suitable model is available to precisely reflect the behavior characteristics.This paper proposed and introduced a method for innovative multi-occupant air-conditioning(AC)usage behavior modelling in a multi-occupant office,which used intuitionistic fuzzy preference relationship to describe individual behavior intention and a hierarchical structure to reflect the social relationship among multiple occupants through subjective evaluation method.The group decision-making process combined the individual behavior intention and the weights of occupants using the analytic hierarchy process.Then,the AC usage behavior of a multi-occupant office was simulated by integrating the multi-occupant model into designer’s simulation toolkit(DeST)building performance simulation software.The results of conducted analysis of a single office with multi-occupant showed that the proposed multi-occupant modelling method could quantitatively characterize the group relationships and AC usage behavior patterns.The absolute errors for the total AC operation time and frequency of the start-up periods of AC between the simulation and measurement results were only 2.7%and 2.0%,respectively.Thus,the proposed multi-occupant modelling method could realize a relatively accurate simulation of the multi-occupant behavior.
文摘In recent years,traffic safety researchers have attempted to separate single-vehicle and multi-vehicle crashes when analyzing crash severity,considering the significant differences in the mechanism of occurrence of the two crash types.However,regardless of the number of vehicles involved in a crash,the severity of a crash is defined by the most severe injury outcome sustained by the occupants,not vehicles.Thus,this study evaluated a need for conducting a separate severity analysis for crashes involving a single occupant(SO)and multiple occupants(MO).Ten-year data(2009–2018)of crashes that involved a collision between a single vehicle and a train at the highway-rail grade crossings(HRGCs)across the United States was used as a case study.Crashes were grouped based on occupancy level;that is,crashes involving SO were separated from the ones involving MO.As expected,MO crashes had higher injury and fatality rates than SO crashes.Three Multinomial Logit(MNL)models were developed to analyze the crash severity of SO crashes,MO crashes,and total crashes.The study found several differences in associated factors when SO crashes and MO crashes were modeled separately.Overall,combining SO and MO crashes tend to either underestimate or overestimate the actual impact of the predictor variable on a specific crash type.Among the variables,train speed and vehicle speed during crash showed a great difference.The findings provide evidence that the severity analysis of the SO and MO crashes should be performed separately as they have different characteristics.
