A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliabili...Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.展开更多
In this paper, we consider median unbiased estimation of bivariate predictive regression models with non-normal, heavy-tailed or heteroscedastic errors. We construct confidence intervals and median unbiased estimator ...In this paper, we consider median unbiased estimation of bivariate predictive regression models with non-normal, heavy-tailed or heteroscedastic errors. We construct confidence intervals and median unbiased estimator for the parameter of interest. We show that the proposed estimator has better predictive potential than the usual least squares estimator via simulation. An empirical application to finance is given. And a possible extension of the estimation procedure to cointegration models is also described.展开更多
In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the couplin...In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.展开更多
This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇...This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.展开更多
Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility...Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility map of the affected region based on the coseismic landslides collected from remotely sensed data and fieldwork,using bivariate statistical model with different landslide causative factors.From the investigation,it is observed that most of the coseismic landslides are independent of previous landslides.Out of 3,716 mapped landslides,we used 80% of them to develop a susceptibility map and the remaining 20% were taken for validating the model.A total of 11 different landslide-influencing parameters were considered.These include slope gradient,slope aspect,plan curvature,elevation,relative relief,Peak Ground Acceleration(PGA),distance from epicenters of the mainshock and major aftershocks,lithology,distance of the landslide from the fault,fold,and drainage line.The success rate of 87.66% and the prediction rate of86.87% indicate that the model is in good agreement between the developed susceptibility map and theexisting landslides data.PGA,lithology,slope angle and elevation have played a major role in triggering the coseismic mass movements.This susceptibility map can be used for relocating the people in the affected regions as well as for future land development.展开更多
Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C a...Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C and SCL-90 to screen for PTSD and other psychological problems among students who survived the disaster and continued their school studies. The surveys were carried out 3, 6, 9, and 12 months after the earthquake. A bivariate 2-level logistic model was used to explore the different levels of sensitivity among students. The factors influencing the relationships between PTSD and depression, and between PTSD and anxiety were examined. Results We analyzed data from 1677 students, revealing that female students in higher grades were more likely to exhibit symptoms of depression, rather than PTSD, compared with the control group （males in lower grades）, and the difference was significant （P〈0.05）. In contrast, ethnic minorities were more likely to exhibit PTSD symptoms compared to the others. In addition, female students were more likely to exhibit symptoms of anxiety than PTSD. Other effects that did not reach statistical significance were suggested to have a similar influence on PTSD, depression, and anxiety. Conclusion After a natural disaster, specific aspects of depression and anxiety should be examined, avoiding an overemphasis on PTSD in social psychological crisis interventions.展开更多
With the concern for environmental quality and food safety, organic food products are becoming more important in the global market. In recent years the organic food industry has been expanding and sales of organic pro...With the concern for environmental quality and food safety, organic food products are becoming more important in the global market. In recent years the organic food industry has been expanding and sales of organic products have been increasing. Abundant studies have been done focusing on organic fruits and vegetables which focused on the shortage of organic live stocks. In this paper we focus our attention on organic pork products. Using a sample of 400 Thais consumers, this study proposes the contingent valuation （CV） technique to measure the willingness of individuals to pay a price premium for organic pork in Thailand. In order to obtain the mean ＂willingness to pay＂ （WTP）, a bivariate probit model was applied to provide information about the crucial variables that affect the WTP. The study revealed that variables that better approximate WTP are based on the lifestyle and knowledge about organic foods rather than the usual socioeconomic factors. The mean WTP on the premium price for organic pork is approximately 34.30 Bath per kg. In order to access the market potential this study shows that the suitable attributes of organic pork which is consistent with consumer preferences are composed of modernized and environmental packaging with special product details. Marketing this product to the buyer it should be set at a reasonable price. Stimulating the market should be done by doing sales promotion and public relations on a regularly basis. In addition, organic pork should be available in any places and convenient for customers to buy.展开更多
The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking trave...The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking travel frequency based on data obtained from the National Household Travel Survey California add-on survey.The study features some highlights.First,bivariate models were used to account for the common unobserved heterogeneity shared at both household and personal levels.Second,endogeneity was explicitly considered.Third,both variable importance ranking and correlation analysis are employed to determine the different features to be fed into each of the joint models.The results illustrated that the models developed with endogeneity performed better than the models without endogeneity.Four influential variables which includes mode to work by bicycle,public transit usage,count of household members,and multiple race responses,tend to have statistically significant impacts on walking and biking trips.展开更多
The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones ge...The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones generally by crash frequency or density,which is effective for roadway engineering improvements but neglects characteristics related to other improvements such as safety education.As safety education would be more effective if targeted at the residences of crash-involved road users,the traditional approach to hot zones may therefore provide biased results for such alternative countermeasures.After confirming that 77.17%of NMV crashes occurred outside the involved riders’areas of residence,this study compared the differences between the locations of crashes and the residences of NMV crash-involved riders in safety influencing factors and hot zone identification.A Poisson lognormal bivariate conditional autoregressive(PLN-BCAR)model was developed to account for potential correlations between crashes and involved riders.The model was compared with the univariate Poisson lognormal conditional autoregressive(UPLN-CAR)model and the bivariate Poisson lognormal conditional autoregressive(BPLNCAR)model;the PLN-BCAR model outperformed the other two models in its better interpretation of the influencing factors and its more efficient parameter estimation.Model results indicated that crashes were mainly associated with roadway and land use characteristics,while involved road users were mainly associated with socioeconomic and land use characteristics.The potential for safety improvement method was adopted to identify hot zones for countermeasure implementation.Results showed over 60%of the identified hot zones were inconsistent:they needed improvement in either engineering or education but not both.These findings can help target the type of improvement to better utilize resources for NMV safety.展开更多
Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak...Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak over threshold,form the core of these models.Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical use.Recently,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity aspect.To address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level.In particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level.Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of data.Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were developed.The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters simultaneously.The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model.Overall,the modeling framework presented in this study advances conflict-based safety assessment,where the severity dimension can be captured via bivariate hybrid models.展开更多
The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The prim...The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.展开更多
This paper examines how independent directors’social capital,as measured by their social network,affects corporate fraud.We find that firms with wellconnected independent directors are less likely to commit fraud,sup...This paper examines how independent directors’social capital,as measured by their social network,affects corporate fraud.We find that firms with wellconnected independent directors are less likely to commit fraud,supporting our monitoring effect hypothesis.This result is robust to a battery of tests.Further analyses show that the effect is stronger for firms with a relatively poor legal environment,for firms whose independent directors face strong reputation incentives and when independent directors are audit committee members.Moreover,we explore a potential economic mechanism of the effect and observe that well-connected independent directors are associated with less absenteeism and more dissension.Overall,our findings suggest that independent directors’social capital plays an important role in corporate governance.展开更多
COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper inv...COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper investigates the effects of COVID-19 on changes toindividuals’ travel patterns, particularly for travel purposes. An online questionnaire survey was conducted in China, which incorporatesquestions about individuals’ sociodemographic and travel characteristics in three different periods of COVID-19 (i.e. before theoutbreak, at the peak and after the peak;the peak here refers to the peak of the pandemic in China, between the end of January and1 May, 2020). The results show that trip frequency decreased sharply from the outbreak until the peak, and drastically increased afterthe peak. Nevertheless, the data fromthis study suggests that it has not fully recovered to the level before the outbreak. Subsequently,a series of random parameters bivariate Probit models for changes in travel patterns were estimated with personal characteristics.The findings demonstrate that during the peak of the pandemic, residents who did not live in more developed cities reached lowfrequencytravel patterns more quickly. For travel purposes, residents of Wuhan, China resumed travelling for work, entertainmentand buy necessities at a much higher rate than other cities. After the peak, students’ travel for work, entertainment and to buy necessitiesrecovered significantly faster than for other occupations. The findings would be helpful for establishing effective policies tocontrol individual travel and minimize disease spread in a possible future pandemic.展开更多
基金supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金the National Natural Science Foundation of China(No.11671080)the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(No.BM2017002)
文摘Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.
基金The NNSF(10571073)of china,and 985 project of Jilin University.
文摘In this paper, we consider median unbiased estimation of bivariate predictive regression models with non-normal, heavy-tailed or heteroscedastic errors. We construct confidence intervals and median unbiased estimator for the parameter of interest. We show that the proposed estimator has better predictive potential than the usual least squares estimator via simulation. An empirical application to finance is given. And a possible extension of the estimation procedure to cointegration models is also described.
基金supported by the Natural Science Foundation of Shanxi Province(Grant No.20210302124437)the Graduate Student Research and Innovation Project of Shanxi Province(Grant No.2023KY551).
文摘In this paper,the Taixin Integrated Economic Zone in Shanxi Province is taken as the research object,and the coupling coordination degree model and bivariate spatial autocorrelation model are used to judge the coupling coordination and spatial-temporal correlation between urbanization and ecosystem service,and the hotspot analysis is used to judge the spatial-temporal trend of urbanization and ecosystem service.The results show that:(1)The urbanization level from 2000 to 2020 continued to rise,the areas with relatively high urbanization were concentrated in the central part of the study area,and the relatively high terrain areas on both sides of the study area,the urbanization was relatively slow,and the hotspot areas with highly significant and significant urbanization level from 2000 to 2020 were distributed as bands in the central part of the study area and the area was rising,and there was no Cold spot area distribution;between 2000 and 2020,the ecosystem service value in the study area increased by 2.6800×10^(8) yuan.Over these two decades,it exhibited a development trend that first rose and then declined.The woodland and grassland agglomeration areas were located on the two sides of the study area,forming highly significant and significant hotspots.Conversely,the central and northeastern parts of the study area were characterized by concentrated man-made land surfaces and croplands,resulting in the formation of highly significant and significant cold spots.(2)In the central part of the study area where man-made land surface and cultivated land are concentrated,the coupling coordination between urbanization and ecosystem service is in the intermediate dislocation and mild dislocation interval;the woodland and grassland concentration areas on both sides of the study area are ecologically fragile,and the coupling coordination between the two is in the level of less than intermediate dislocation.(3)From 2000 to 2020,urbanization and the value of ecosystem services were both negatively correlated,although the correlation coefficient was low.In the central and northeastern parts,urbanization and ecosystem service exhibited patterns of high-low,high-high,and low-low clustering.Conversely,on both sides of the study area,most of the clusters showed a low-high pattern.
文摘This paper discusses the model construction and the association between the Italy and the Germany＇s stock markets. The period of study data is from January 3, 2000 to June 30, 2008. This paper also utilizes Student＇s t distribution to analyze the proposed model. The empirical results show that the two stock markets are mutually affected each other, and the dynamic conditional correlation （DCC） and the bivariate asymmetric-GARCH （1, 2） model is appropriate in evaluating the relation between them. The empirical result also indicates that Italy and Germany＇s stock markets show a positive relationship. The average value of correlation coefficient equals to 0.8424, which implies that the two stock markets return volatility have a synchronized influence on each other. In addition, the empirical result also shows that there is an asymmetrical effect between Italy and the Germany＇s stock markets, and demonstrates that the good news and bad news of the stock returns＇ volatility will produce the different variation risks for Italy and the Germany＇s stock price markets.
基金the Chinese Academy of Sciences Presidents International Fellowship Initiative(Grant No.2015PEO23)External Cooperation Program of BIC,15 Chinese Academy of Sciences(Grant No.131551KYSB20150009)hundred talents program of Chinese Academy of Sciences(Su Lijun)for supporting for this research
文摘Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility map of the affected region based on the coseismic landslides collected from remotely sensed data and fieldwork,using bivariate statistical model with different landslide causative factors.From the investigation,it is observed that most of the coseismic landslides are independent of previous landslides.Out of 3,716 mapped landslides,we used 80% of them to develop a susceptibility map and the remaining 20% were taken for validating the model.A total of 11 different landslide-influencing parameters were considered.These include slope gradient,slope aspect,plan curvature,elevation,relative relief,Peak Ground Acceleration(PGA),distance from epicenters of the mainshock and major aftershocks,lithology,distance of the landslide from the fault,fold,and drainage line.The success rate of 87.66% and the prediction rate of86.87% indicate that the model is in good agreement between the developed susceptibility map and theexisting landslides data.PGA,lithology,slope angle and elevation have played a major role in triggering the coseismic mass movements.This susceptibility map can be used for relocating the people in the affected regions as well as for future land development.
基金supported by the Research Fund from the Department of Education of Sichuan Province (NO:08SA093)
文摘Objective To analyze the sensitivity of effect factors between the PCL-C and the SCL-90, to provide evidence for social psychological crisis screening and post-trauma interventions. Methods We administered the PCL-C and SCL-90 to screen for PTSD and other psychological problems among students who survived the disaster and continued their school studies. The surveys were carried out 3, 6, 9, and 12 months after the earthquake. A bivariate 2-level logistic model was used to explore the different levels of sensitivity among students. The factors influencing the relationships between PTSD and depression, and between PTSD and anxiety were examined. Results We analyzed data from 1677 students, revealing that female students in higher grades were more likely to exhibit symptoms of depression, rather than PTSD, compared with the control group （males in lower grades）, and the difference was significant （P〈0.05）. In contrast, ethnic minorities were more likely to exhibit PTSD symptoms compared to the others. In addition, female students were more likely to exhibit symptoms of anxiety than PTSD. Other effects that did not reach statistical significance were suggested to have a similar influence on PTSD, depression, and anxiety. Conclusion After a natural disaster, specific aspects of depression and anxiety should be examined, avoiding an overemphasis on PTSD in social psychological crisis interventions.
文摘With the concern for environmental quality and food safety, organic food products are becoming more important in the global market. In recent years the organic food industry has been expanding and sales of organic products have been increasing. Abundant studies have been done focusing on organic fruits and vegetables which focused on the shortage of organic live stocks. In this paper we focus our attention on organic pork products. Using a sample of 400 Thais consumers, this study proposes the contingent valuation （CV） technique to measure the willingness of individuals to pay a price premium for organic pork in Thailand. In order to obtain the mean ＂willingness to pay＂ （WTP）, a bivariate probit model was applied to provide information about the crucial variables that affect the WTP. The study revealed that variables that better approximate WTP are based on the lifestyle and knowledge about organic foods rather than the usual socioeconomic factors. The mean WTP on the premium price for organic pork is approximately 34.30 Bath per kg. In order to access the market potential this study shows that the suitable attributes of organic pork which is consistent with consumer preferences are composed of modernized and environmental packaging with special product details. Marketing this product to the buyer it should be set at a reasonable price. Stimulating the market should be done by doing sales promotion and public relations on a regularly basis. In addition, organic pork should be available in any places and convenient for customers to buy.
文摘The enormous advantages of active transportation lead the transportation research focus towards enhancing the walking and biking trips.The present study explored the influential factors to the walking and biking travel frequency based on data obtained from the National Household Travel Survey California add-on survey.The study features some highlights.First,bivariate models were used to account for the common unobserved heterogeneity shared at both household and personal levels.Second,endogeneity was explicitly considered.Third,both variable importance ranking and correlation analysis are employed to determine the different features to be fed into each of the joint models.The results illustrated that the models developed with endogeneity performed better than the models without endogeneity.Four influential variables which includes mode to work by bicycle,public transit usage,count of household members,and multiple race responses,tend to have statistically significant impacts on walking and biking trips.
基金the International Science and Technology Cooperation Programme of China(2017YFE0134500)。
文摘The high risk of injury resulting from non-motorized vehicle(NMV)crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety.Traditional safety improvement methods identify hot zones generally by crash frequency or density,which is effective for roadway engineering improvements but neglects characteristics related to other improvements such as safety education.As safety education would be more effective if targeted at the residences of crash-involved road users,the traditional approach to hot zones may therefore provide biased results for such alternative countermeasures.After confirming that 77.17%of NMV crashes occurred outside the involved riders’areas of residence,this study compared the differences between the locations of crashes and the residences of NMV crash-involved riders in safety influencing factors and hot zone identification.A Poisson lognormal bivariate conditional autoregressive(PLN-BCAR)model was developed to account for potential correlations between crashes and involved riders.The model was compared with the univariate Poisson lognormal conditional autoregressive(UPLN-CAR)model and the bivariate Poisson lognormal conditional autoregressive(BPLNCAR)model;the PLN-BCAR model outperformed the other two models in its better interpretation of the influencing factors and its more efficient parameter estimation.Model results indicated that crashes were mainly associated with roadway and land use characteristics,while involved road users were mainly associated with socioeconomic and land use characteristics.The potential for safety improvement method was adopted to identify hot zones for countermeasure implementation.Results showed over 60%of the identified hot zones were inconsistent:they needed improvement in either engineering or education but not both.These findings can help target the type of improvement to better utilize resources for NMV safety.
基金This research is funded by the Queensland University of Technology,iMOVE CRC,and supported by the Cooperative Research Centres program,an Australian Government initiative.
文摘Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak over threshold,form the core of these models.Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical use.Recently,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity aspect.To address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level.In particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level.Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of data.Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were developed.The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters simultaneously.The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model.Overall,the modeling framework presented in this study advances conflict-based safety assessment,where the severity dimension can be captured via bivariate hybrid models.
基金supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project(21NDJC163YB,22NDJC166YB)Natural Science Foundation of China(No.52002282,52272343)Natural Science Foundation of Zhejiang Province(LY21E080010)。
文摘The rapid development of the delivery industry brings convenience to modern society.However,the high rates of crashes and the survival of electric bicycle(e-bike)riders in the delivery industry raise concerns.The primary objective of this study is to explore the factors affecting delivery e-bike riders’stressful work pressure and crash involvement in China.Data were collected by a questionnaire survey administered in the city of Ningbo,China.A bivariate ordered probit(BOP)model was developed to simultaneously examine the factors associated with both the working conditions of delivery e-bike riders and their involvement in crashes.The marginal effects for the contributory factors were calculated to quantify their impacts on the outcomes.The results showed that the BOP model can account for commonly unobserved characteristics of the working conditions and crash involvement of delivery e-bike riders.The BOP model results showed that the stressful working conditions of delivery e-bike riders were affected by the number of orders and delivery time and rider age and risky riding behaviors.Delivery rider involvement in crashes was affected by the number of orders,strength of the punishment for traffic violations,and familiarity with traffic regulations.It was also found that stressful working conditions and crash involvement were strongly and positively correlated.The findings of this study can enhance our understanding of the factors that affect the working conditions and delivery rider crash involvement.Based on the results,some suggestions regarding public policy,risky riding behaviors,safety promotion,and stronger corporate governance rules were discussed to increase the targeted safety-related interventions for delivery ebike riders in Ningbo,China.
文摘This paper examines how independent directors’social capital,as measured by their social network,affects corporate fraud.We find that firms with wellconnected independent directors are less likely to commit fraud,supporting our monitoring effect hypothesis.This result is robust to a battery of tests.Further analyses show that the effect is stronger for firms with a relatively poor legal environment,for firms whose independent directors face strong reputation incentives and when independent directors are audit committee members.Moreover,we explore a potential economic mechanism of the effect and observe that well-connected independent directors are associated with less absenteeism and more dissension.Overall,our findings suggest that independent directors’social capital plays an important role in corporate governance.
基金National Key R&D Program of China(Grant No.2020YFB1600400)Innovation-Driven Project of Central South University(Grant No.2020CX013).
文摘COVID-19 has upended the whole world. Due to travel restrictions by governments and increased perceived risks of the disease, therehave been significant changes in social activities and travel patterns. This paper investigates the effects of COVID-19 on changes toindividuals’ travel patterns, particularly for travel purposes. An online questionnaire survey was conducted in China, which incorporatesquestions about individuals’ sociodemographic and travel characteristics in three different periods of COVID-19 (i.e. before theoutbreak, at the peak and after the peak;the peak here refers to the peak of the pandemic in China, between the end of January and1 May, 2020). The results show that trip frequency decreased sharply from the outbreak until the peak, and drastically increased afterthe peak. Nevertheless, the data fromthis study suggests that it has not fully recovered to the level before the outbreak. Subsequently,a series of random parameters bivariate Probit models for changes in travel patterns were estimated with personal characteristics.The findings demonstrate that during the peak of the pandemic, residents who did not live in more developed cities reached lowfrequencytravel patterns more quickly. For travel purposes, residents of Wuhan, China resumed travelling for work, entertainmentand buy necessities at a much higher rate than other cities. After the peak, students’ travel for work, entertainment and to buy necessitiesrecovered significantly faster than for other occupations. The findings would be helpful for establishing effective policies tocontrol individual travel and minimize disease spread in a possible future pandemic.
