Mode choice is important in shipping commodities efficiently. This paper develops a binary logit model and a regression model to study the cereal grains movement by truck and rail in the United States using the public...Mode choice is important in shipping commodities efficiently. This paper develops a binary logit model and a regression model to study the cereal grains movement by truck and rail in the United States using the publically available Freight Analysis Framework (FAF2.2) database and U.S. highway and networks and TransCAD, a geographic information system with strong transportation modeling capabilities. The binary logit model and the regression model both use the same set of generic variables, including mode split probability, commodity weight, value, network travel time, and fuel cost. The results show that both the binary logit and regression models perform well for cereal grains transportation in the United States, with the binary logit model yielding overall better estimates with respect to the observed truck and rail mode splits. The two models can be used to study other commodities between two modes and may produce better results if more mode specific variables are used.展开更多
聚焦集装箱托运人货运选择偏好调查,对意向选择实验(stated choice experiment,SCE)设计方法(问卷设计的核心)进行研究。基于D-error和S-error两个效率指标,提出一种均衡调查效率与调查成本的SCE设计新方法。以义乌至宁波集装箱运输为例...聚焦集装箱托运人货运选择偏好调查,对意向选择实验(stated choice experiment,SCE)设计方法(问卷设计的核心)进行研究。基于D-error和S-error两个效率指标,提出一种均衡调查效率与调查成本的SCE设计新方法。以义乌至宁波集装箱运输为例,利用试调查数据和新设计方法设计SCE。研究发现:新设计方法的D-error和S-error指标值分别为0.0443和20,小于传统正交设计法的0.0562和61;基于新设计方法、正式调查的SWAIT多元Logit模型(multinomial Logit model proposed by SWAIT,MNLS模型)在拟合优度和显著参数数目上均优于基于正交设计法、试调查的MNLS模型。结果表明,新设计方法能够以较小样本量得到拟合优度更好的模型,揭示更多的托运人货运选择偏好信息,可为高效率、低成本地采集托运人货运选择偏好数据提供一种新途径。展开更多
文摘Mode choice is important in shipping commodities efficiently. This paper develops a binary logit model and a regression model to study the cereal grains movement by truck and rail in the United States using the publically available Freight Analysis Framework (FAF2.2) database and U.S. highway and networks and TransCAD, a geographic information system with strong transportation modeling capabilities. The binary logit model and the regression model both use the same set of generic variables, including mode split probability, commodity weight, value, network travel time, and fuel cost. The results show that both the binary logit and regression models perform well for cereal grains transportation in the United States, with the binary logit model yielding overall better estimates with respect to the observed truck and rail mode splits. The two models can be used to study other commodities between two modes and may produce better results if more mode specific variables are used.
文摘聚焦集装箱托运人货运选择偏好调查,对意向选择实验(stated choice experiment,SCE)设计方法(问卷设计的核心)进行研究。基于D-error和S-error两个效率指标,提出一种均衡调查效率与调查成本的SCE设计新方法。以义乌至宁波集装箱运输为例,利用试调查数据和新设计方法设计SCE。研究发现:新设计方法的D-error和S-error指标值分别为0.0443和20,小于传统正交设计法的0.0562和61;基于新设计方法、正式调查的SWAIT多元Logit模型(multinomial Logit model proposed by SWAIT,MNLS模型)在拟合优度和显著参数数目上均优于基于正交设计法、试调查的MNLS模型。结果表明,新设计方法能够以较小样本量得到拟合优度更好的模型,揭示更多的托运人货运选择偏好信息,可为高效率、低成本地采集托运人货运选择偏好数据提供一种新途径。
