In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model e...In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.展开更多
This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alter...This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alternative to the mixed logit model.This was done to better account for unobserved heterogeneity in the crash data.As per the results,the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity.The advanced model showed that traffic violations,crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming’s interstate highways.With regards to traffic violations,driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity.Among other useful insights,the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks(vehicles not classified as trucks)and driving too fast for conditions.This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming’s interstates.This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions.Results from the study will help enforcement agencies in the state to better identify appropriate countermeasures to mitigate the impact of violations on crash severity.展开更多
为更全面研究高速公路车辆冲突风险的影响因素,笔者考虑微观与宏观因素影响,分析其可能存在的异质性,建立了随机参数Logit模型对其进行分析。使用高分辨率车辆轨迹数据,以碰撞时间(time to collision,TTC)为风险识别标准,提取风险发生前...为更全面研究高速公路车辆冲突风险的影响因素,笔者考虑微观与宏观因素影响,分析其可能存在的异质性,建立了随机参数Logit模型对其进行分析。使用高分辨率车辆轨迹数据,以碰撞时间(time to collision,TTC)为风险识别标准,提取风险发生前1 s内数据,处理得到车辆自身运动状态、与周围车辆的微观交互以及路段宏观交通流状态三类特征,并基于皮尔逊相关系数和嵌入法进行特征筛选;以筛选后的特征作为模型自变量,以车辆是否存在冲突风险为因变量,分别构建随机参数Logit模型以及考虑均值异质性的随机参数模型并进行对比。研究结果表明:考虑均值异质性的随机参数Logit模型拟合效果最好,且三类变量均对冲突风险有显著影响,其中车辆自身运动状态的波动以及车辆与前方车辆的速度差与风险呈正相关,相邻车道的大车比例与风险呈负相关;车辆自身运动状态以及与前方车辆的微观交互两类特征对冲突风险影响较大,而宏观交通特征的影响相对较小。展开更多
Despite the number of studies focusing on the financial analysis of production activities, conducting on technical solutions, and improving water quality, no study has been conducted on the application of economic ins...Despite the number of studies focusing on the financial analysis of production activities, conducting on technical solutions, and improving water quality, no study has been conducted on the application of economic instruments that apply to water quality management in craft villages, and several studies of WTP also. This study aimed to estimate the households’ willingness-to-pay for wastewater treatment in selected traditional agro-food processing villages in Nhue-Day River Basin, Vietnam. A pilot Choice Experiment (CE) technique in Choice Modelling (CM) approach was applied for this study with 267 selected agro-food processing households by using the conditional logit (CL) and random parameter logit (RPL) models. The results showed that total annual environmental fee for wastewater treatment from agro-food processing households is estimated as 1089 million VND (equal to US$47,868 per year) for the total of 902 agro-food processing households in three research sites in Nhue-Day River Basin. This estimated budget for wastewater treatment accounted for 55.85% of total annual operation and maintenance costs only. In addition, the technology is improved to enable 90% of treated wastewater. Overall, the results of this study suggest the new wastewater treatment plant construction and improved wastewater collection system by increasing the investment in order to improve the water quality in Nhue-Day River Basin that brings about the reducing environmental degradation, biodiversity loss and human health risks.展开更多
文摘In this paper, we explore the properties of a positive-part Stein-like estimator which is a stochastically weighted convex combination of a fully correlated parameter model estimator and uncorrelated parameter model estimator in the Random Parameters Logit (RPL) model. The results of our Monte Carlo experiments show that the positive-part Stein-like estimator provides smaller MSE than the pretest estimator in the fully correlated RPL model. Both of them outperform the fully correlated RPL model estimator and provide more accurate information on the share of population putting a positive or negative value on the alternative attributes than the fully correlated RPL model estimates. The Monte Carlo mean estimates of direct elasticity with pretest and positive-part Stein-like estimators are closer to the true value and have smaller standard errors than those with fully correlated RPL model estimator.
基金funded by the Wyoming Department of Transportation
文摘This study investigated the impact of traffic violations on crash injury severity on Wyoming’s interstate highways.A random parameters multinomial logit(MNL)model with heterogeneity in means was estimated as an alternative to the mixed logit model.This was done to better account for unobserved heterogeneity in the crash data.As per the results,the random parameters model with heterogeneity in means not only exhibited a better fit but also uncovered more insights regarding the factors influencing crash injury severity.The advanced model showed that traffic violations,crash characteristics and environmental characteristics among other factors impact crash injury severity on Wyoming’s interstate highways.With regards to traffic violations,driving too fast for prevailing conditions and driving under the influence of alcohol and drugs were identified as the main violations that significantly influenced crash severity.Among other useful insights,the heterogeneity in mean specification indicated that the likelihood of severe injury crashes is increased by the interactive effect between non-trucks(vehicles not classified as trucks)and driving too fast for conditions.This is a significant implication that high speed behavior by non-truck drivers in adverse weather conditions is ranked as one of the hazardous traffic violations on Wyoming’s interstates.This study provided for the first time important information on the impact of traffic violations on crash severity of crashes that occurred on challenging roadways that characterized by mountainous terrain and severe weather conditions.Results from the study will help enforcement agencies in the state to better identify appropriate countermeasures to mitigate the impact of violations on crash severity.
文摘为更全面研究高速公路车辆冲突风险的影响因素,笔者考虑微观与宏观因素影响,分析其可能存在的异质性,建立了随机参数Logit模型对其进行分析。使用高分辨率车辆轨迹数据,以碰撞时间(time to collision,TTC)为风险识别标准,提取风险发生前1 s内数据,处理得到车辆自身运动状态、与周围车辆的微观交互以及路段宏观交通流状态三类特征,并基于皮尔逊相关系数和嵌入法进行特征筛选;以筛选后的特征作为模型自变量,以车辆是否存在冲突风险为因变量,分别构建随机参数Logit模型以及考虑均值异质性的随机参数模型并进行对比。研究结果表明:考虑均值异质性的随机参数Logit模型拟合效果最好,且三类变量均对冲突风险有显著影响,其中车辆自身运动状态的波动以及车辆与前方车辆的速度差与风险呈正相关,相邻车道的大车比例与风险呈负相关;车辆自身运动状态以及与前方车辆的微观交互两类特征对冲突风险影响较大,而宏观交通特征的影响相对较小。
基金Southeast Asia Regional Center for Graduate Study and Research Agriculture(SEARCA)provide me the financial support to conduct this research.
文摘Despite the number of studies focusing on the financial analysis of production activities, conducting on technical solutions, and improving water quality, no study has been conducted on the application of economic instruments that apply to water quality management in craft villages, and several studies of WTP also. This study aimed to estimate the households’ willingness-to-pay for wastewater treatment in selected traditional agro-food processing villages in Nhue-Day River Basin, Vietnam. A pilot Choice Experiment (CE) technique in Choice Modelling (CM) approach was applied for this study with 267 selected agro-food processing households by using the conditional logit (CL) and random parameter logit (RPL) models. The results showed that total annual environmental fee for wastewater treatment from agro-food processing households is estimated as 1089 million VND (equal to US$47,868 per year) for the total of 902 agro-food processing households in three research sites in Nhue-Day River Basin. This estimated budget for wastewater treatment accounted for 55.85% of total annual operation and maintenance costs only. In addition, the technology is improved to enable 90% of treated wastewater. Overall, the results of this study suggest the new wastewater treatment plant construction and improved wastewater collection system by increasing the investment in order to improve the water quality in Nhue-Day River Basin that brings about the reducing environmental degradation, biodiversity loss and human health risks.
