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首页> 外文期刊>Transportation research >Will commute drivers switch to park-and-ride under the influence of multimodal traveler information? A stated preference investigation
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Will commute drivers switch to park-and-ride under the influence of multimodal traveler information? A stated preference investigation

机译:通勤驾驶员会在多式联运旅行者信息的影响下转换为停车乘车吗?明确的偏好调查

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摘要

The knowledge about en-trip mode switching behavior with presence of multimodal traveler information is very limited so far. This study investigated the impacts on commute drivers' en-trip mode switch decisions of smartphone multimodal traveler information systems (SMTIS) which integrate dynamic information of auto-drive and subway park and-ride (P&R). This is based on data collected from a stated preference survey in Shanghai, China. A panel mixed probit model which accounts for potential correlations of observations among a same driver and heterogeneity in preferences for travel time savings and comfort level of subway car was developed. The panel model has a much better goodness of fit than a model without consideration of panel effect and heterogeneity. The results show that SMTIS have significant impacts on commuter drivers' decision about switching from auto drive to P & R; the impacts depend on personal attributes including gender, age, education level, income, and PEW use experience; the sensitivity to time savings in the case non-incident induced delays, and the sensitivity to comfort level of subway, both vary significantly among the driver sample. (C) 2018 Elsevier Ltd. All rights reserved.
机译:到目前为止,关于存在多模式旅行者信息的乘车模式切换行为的知识非常有限。这项研究调查了智能手机多式联运旅行者信息系统(SMTIS)对通勤驾驶员出行模式切换决策的影响,该信息系统集成了自动驾驶和地铁停车与乘车(P&R)的动态信息。这是基于从在中国上海进行的既定偏好调查中收集的数据得出的。开发了一种面板混合概率模型,该模型考虑了同一驾驶员之间观察的潜在相关性和异质性,从而优先考虑节省旅行时间和地铁车厢的舒适度。面板模型比不考虑面板效应和异质性的模型具有更好的拟合优度。结果表明,SMTIS对通勤驾驶员从自动驾驶切换到P&R的决定有重大影响。影响取决于个人属性,包括性别,年龄,受教育程度,收入和PEW使用经验;在驾驶员意外样本中,非事故引起的延误对节省时间的敏感性以及对地铁舒适度的敏感性都显着不同。 (C)2018 Elsevier Ltd.保留所有权利。

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