Stated Preference Design for Exploring Demand for “Mobility as a Service” Plans
Stated Preference Design for Exploring Demand for “Mobility as a Service” Plans
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探索“移动即服务”计划需求的既定偏好设计
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发表时间：
2017
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通讯作者：
M. Kamargianni
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作者：
Melinda Matyas;M. Kamargianni
最近，交通运输行业掀起了一波创新移动解决方案浪潮，以提高其效率并消除对私家车的依赖。其中一个想法是移动即服务 (MaaS) 概念。这种以用户为中心的数字化、智能化的出行分发模式旨在通过单一平台满足用户的出行需求，并通过单一出行运营商提供所有服务。 MaaS 旨在整合所有交通方式（公共交通、自行车和汽车共享、出租车等），并通过单一界面向用户提供这些方式，允许以即用即付服务或定制的每月出行计划（包括每种交通方式的固定金额）的形式进行购买。以这种方式打包或捆绑服务对于城市交通部门来说可能是新鲜事，但在电信等其他部门中已普遍使用。作为一个新概念，如何创建这些出行计划以使其能够满足所有社会人口用户群体的偏好，在理解上仍然存在差距。在此背景下，本文的目的首先是描述陈述偏好 (SP) 实验的设计，该实验捕获购买 MaaS 产品的复杂决策过程，其次，使用焦点小组验证该设计。该设计使用基于智能手机的提示召回旅行调查工具（FMS，Cottrill 等，2013），该工具通过有关 MaaS 每月出行计划选择的 SP 实验进行了扩展。在填写有关基本社会人口统计信息和出行工具选择的预调查后，将对受访者进行为期 7 天的跟踪。在跟踪期间，系统会提醒他们核实自己的旅行和非旅行活动，并回答有关其体验的其他问题（在网络界面或智能手机上完成）。由于案例研究区域是大伦敦地区，因此调查的所有要素都经过调整以适应当地环境。 7 天的跟踪完成后，显示偏好 (RP) 数据将被汇总，并向用户提供该周出行行为的摘要记录，按交通方式细分，包括有关出行成本、时间、距离和出行次数的信息。这种所谓的移动记录 (MR) 与 MaaS 通往 SP 的描述一起呈现给用户。此外，在SP旁边还显示了MR的缩短版，以便用户反思他们当前的旅行习惯，然后在实验中做出选择。 SP 有 4 种选择：三种固定计划选择和一个菜单选项，用户可以在其中确定他们想要哪种模式以及需要多少模式。这些内容并排呈现，但只能选择其中之一。因此，从选项中做出的选择的结果是三个计划之一或菜单选项中各个功能的任意组合。菜单选项的灵活性是有价格的，这意味着该计划总是（相对）比固定计划更昂贵。选择这种方法是为了分析人们为 MaaS 计划的灵活性付费的意愿。该计划的核心属性是交通方式：公共交通（有两个级别的无限制巴士、无限制公共交通，以与伦敦现有的月票选项相匹配）、自行车共享（是、否）、汽车共享（以时间为单位的级别）和出租车（以距离为单位的级别）。我们生活在大规模定制的时代，人们习惯于根据自己的需求提供个性化服务。考虑到这一点，SP 针对每个受访者量身定制，以提出适合其特定要求的计划。这些背景信息是从调查的 RP 部分、预问卷、活动日记以及流动记录中收集的。该计划通过两种方式进行定制。首先，包括或排除某些模式属性的决定基于用户的先验知识。例如，如果他们声明自己没有许可证，那么他们的所有计划中都会排除汽车共享属性。其次，某些模式属性的属性级别根据其移动性记录值进行旋转（除非该值在 MR 中为零）。这与出租车和汽车共享属性尤其相关，如果没有这些属性，可能会假设与受访者完全不相关的值。除了基于模式的核心属性外，SP 中还包含其他属性，以测试受访者对这些功能的开放程度。其中包括“10 分钟出租车保证”和“免费为汽车共享计划添加额外司机”等想法。此外，奖励属性还包含在当月免费食品或杂货配送等级别中。最后两个属性是可转移性，即有多少剩余的模式属性可以转移到下个月，以及价格属性。后者的水平围绕每个属性现有基本价格的总和波动，在可能的情况下，这些基本价格取自其现实生活价格或平均价格。尽管设计很复杂，但呈现给用户的方式却清晰简洁。各种设计和演示选项在三波焦点小组中进行了测试，其中包括来自不同社会人口背景的个人。第一和第三焦点小组规模较小（5 人），而中间焦点小组约有 20 人。这些形式采用电子邮件反馈和个人访谈（在某些情况下两者结合）的形式，讨论首选设计、呈现的信息甚至措辞。这项研究旨在为了解人们对 MaaS 计划的偏好迈出第一步。所提出的设计结合了多种方法，例如将智能手机活动日志、旋转、菜单和简单选择整合到一项实验中，使我们能够有效地检查未来 MaaS 用户可能必须做出的复杂决策。尽管该设计适用于伦敦，但它也可以用于在任何其他环境中研究 MaaS。
The transport sector has recently been swept with a wave of innovative mobility solutions to increase its efficiency and eradicate dependence on private vehicles. One of these ideas, is the Mobility as a Service (MaaS) concept. This user-centric, digital and intelligent mobility distribution model aims to meet users’ transport needs via a single platform and offer all the services through a single mobility operator. MaaS aims to integrate all transportation modes (public transport, bike and car sharing, taxi etc.) and provide them to users through a single interface allowing purchase either as a pay-as-you-go service or as tailored monthly mobility plans including fixed amounts of each transport mode. Packaging, or bundling, services in such a way may be new to the urban transport sector, but has been commonly used in other sectors, such as telecommunications. As a new concept, there is still a gap in understanding how to create these mobility plans so that they can cater for the preferences of all the socio-demographic user groups. Against this background, the purpose of this paper is to first, describe the design of a stated preference (SP) experiment that captures the complex decision making process of purchasing MaaS products and, second, to validate this design using focus groups. The design uses a prompted recall smartphone based travel survey tool (FMS, Cottrill et al., 2013), which is expanded by a SP experiment regarding MaaS monthly mobility plans choice. After filling out a pre-survey about basic sociodemographic information and mobility tool choices, respondents are tracked for a 7-day period. During the span of the tracking, they are reminded to verify their travel and non-travel activities and answer additional questions about their experiences (completed either on the web interface or their smartphones). As the case study area is Greater London, all the elements of the survey are adapted to fit the local environment. After the 7 days of tracking is complete, the revealed preference (RP) data is aggregated and users are presented with a summary record of their mobility behaviour for that week, broken down by transport mode and including information about travel-cost, time, and distance and number of trips. This, so-called Mobility Record (MR) is presented to users alongside the a description of what MaaS is to lead up to the SP. Further, a shortened version of the MR is shown alongside the SP, to allow users to reflect on their current travel habits and then make a choice in the experiment. The SP has 4 alternatives: three fixed plan alternatives and one menu option where the users can determine which and how much of each mode they want. These are presented alongside each other, but only one of them can be chosen. Thus, the outcome of a choice made from the options is either one of the three plans or any combination of the individual features in the menu option. The flexibility of the menu option is priced, meaning that this plan is always more expensive (relatively) to the fixed plans. This approach is chosen to allow for analysis of peoples’ willingness to pay for flexibility within MaaS plans. The core attributes in the plans are the transport modes: public transport (with two levels unlimited bus, unlimited PT to match with the existing monthly pass options offered in London), bike sharing (yes, no), car sharing (with levels denominated in time), and taxi (with levels denominated in distance). As we live in the era of mass customisation, individuals are used to services that are personalised to their needs. Taking this into account, the SP was tailored to each respondent to present plans that suit their specific requirements. This contextual information is gathered from the RP sections of the survey, the pre-questionnaire and the activity diary, as well as the mobility record. The plans are tailored in two ways. First, the decision to include or exclude certain mode-attributes are based on prior knowledge of the user. For example, if they stated that they do not have a licence, they the car sharing attribute was excluded from all their plans. Second, the attribute levels of some mode-attributes are pivoted off their mobility record values (except if this value is zero in the MR). This is especially relevant for the taxi and car sharing attributes, which without this, could assume values that are completely unrelatable for the respondent. Besides the core, mode based attributes, additional ones were also included in the SP to test for respondents’ openness to these features. These include ideas like ’10-minute taxi guarantee’ and ‘adding additional drivers to the car sharing plan for free’. Further, an incentive attribute was included with levels such as free food or grocery delivery for the month. The final two attributes are transferability, meaning how much of left over mode-attributes can be transferred to the next month, and the price attribute. The levels for the latter are fluctuated around the sum of the existing base prices of each attribute, which, where possible, are taken from their real life price or average price. Even though the design is complex, the way it is presented to users is clear and as concise. Various design and presentation options were tested in three waves of focus groups, including individuals from a range of sociodemographic backgrounds. The first and the third focus groups were smaller (5 individuals) while the middle one was with around 20 individuals. These took the format of both email feedback and personal interviews (in some cases the combination of both) about preferred design, presented information and even wording. This research aims to contribute the first step in the process of understanding peoples’ preferences for MaaS plans. The presented design combines several methods, e.g. smartphone activity diaries, pivoting, menu and simple choice into one experiment allowing us to efficiently examine the complex decisions potential future MaaS users will have to make. Even though the design was applied to London, it could be used to study MaaS in any other environment as well.