Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles
Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles
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DOI：
10.1016/s0191-2615(99)00031-4
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发表时间：
2000-06-01
影响因子：
6.8
通讯作者：
Train, K
中科院分区：
文献类型：
--
作者：
Brownstone, D;Bunch, DS;Train, K
我们比较了多项logit和混合logit模型的数据加州家庭的汽车显示和陈述的喜好。陈述偏好（SP）数据引发了家庭对汽油，电动，甲醇和压缩天然气汽车的各种属性的偏好。混合logit模型提供了比logit更好的拟合，具有高度显著性，并显示出受访者对替代燃料车辆偏好的巨大异质性。包括这种异质性的影响表现在预测练习。这里介绍的替代燃料汽车模型还强调了合并SP和显示偏好（RP）数据的优势。RP数据似乎是获得现实的车身类型的选择和缩放信息的关键，但他们受到多重共线性和测量车辆属性的困难。SP数据对于获得市场上不可用的属性信息至关重要，但使用这些数据的纯SP模型给出的预测令人难以置信。(C)2000爱思唯尔科技有限公司版权所有。
We compare multinomial logit and mixed logit models for data on California households' revealed and stated preferences for automobiles. The stated preference (SP) data elicited households' preferences among gasoline, electric, methanol, and compressed natural gas vehicles with various attributes. The mixed logit models provide improved fits over logit that are highly significant, and show large heterogeneity in respondents' preferences for alternative-fuel vehicles. The effects of including this heterogeneity are demonstrated in forecasting exercises. The alternative-fuel vehicle models presented here also highlight the advantages of merging SP and revealed preference (RP) data. RP data appear to be critical for obtaining realistic body-type choice and scaling information, but they are plagued by multicollinearity and difficulties with measuring vehicle attributes. SP data are critical for obtaining information about attributes not available in the marketplace, but pure SP models with these data give implausible forecasts. (C) 2000 Elsevier Science Ltd. All rights reserved.