首尔交通即服务中通勤者交通替代选择模型的开发

Youngseo Kim, Eui-Jin Kim, Dong‐Kyu Kim
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引用次数: 1

摘要

移动即服务(Mobility as a service, MaaS)将出行方式及其相关服务的信息集成到一个单一的平台上,由于其便利性,近年来引起了人们的广泛关注和研究兴趣。最近,韩国政府计划推出MaaS,通过一个应用程序提供预订和支付,将现有的和创新的移动服务整合在一起,为国内民众提供方便、无缝的旅行体验。尽管许多研究人员预计MaaS将带来从以汽车为中心到以交通为中心的移动模式的转变,但MaaS也有可能增加化石燃料汽车的客流量。事先了解MaaS对人们选择行为的影响,是成功实施MaaS、减少社会混乱的关键。因此,我们研究的目的是调查用户对MaaS系统中可用的运输方案的偏好。我们进行了包括模式组合在内的五种方案的陈述选择实验,获得了1556个有效样本。我们开发了两个独立的嵌套logit模型,可以减轻不相关替代问题的独立性。各模型的稳健性r平方分别为0.297、0.310,结果令人满意。最后,我们揭示了社会经济变量、工作场所变量如何影响每个嵌套结构的偏好。本文的研究结果将有助于平台运营商制定营销策略和设计定制出行模式推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing Commuters’ Transportation Alternative Choice Model in Seoul Mobility-as-a-Service
Mobility as a service (MaaS), which integrates information of travel modes and its related services into a single platform, has generated considerable recent attention and research interest owing to its convenience. Recently Korean government planned to launch the MaaS which integrates existing and innovative mobility services by providing booking and payment through a single application, thus provides convenient, seamless travel experience to domestic people. Although many researchers anticipate MaaS will bring a shift from car-centric to transit-centric mobility pattern, it is also possible that MaaS increase ridership of fossil-fuel vehicle. Understanding the impact of MaaS on people’s choice behavior beforehand is critical to successfully implement MaaS and to minimize social disorder. Thus, the objective of our research is to investigate user preference to transport alternatives available in the MaaS system. We conducted a stated choice experiment with five alternatives including mode combinations and obtained 1,556 valid samples. We developed two separate nested logit models which can assuage independence of irrelevant alternatives problems. Each model showed satisfying results in that the values of robust r-square were 0.297, 0.310, respectively. Finally, we revealed how socio-economic variables, workplace variables affect the preference of each nested structure. The results of this paper will be helpful to platform operators when building marketing strategies and designing a customized travel mode recommendation system.
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