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Human factors of digitalized mobility forms and services
European Transport Research Review ( IF 5.1 ) Pub Date : 2020-07-06 , DOI: 10.1186/s12544-020-00435-5
Alexandra Millonig , Sonja Haustein

In the light of pressing challenges like climate change, urban congestion, air and noise pollution and traffic safety there are numerous efforts aiming at reducing car traffic and shifting road users to more sustainable forms of mobility. In this respect, digitalization and technology developments offer new opportunities to support this development. The envisioned solutions range from increasing the energy efficiency of vehicles as well as their usage through pooling and sharing options, the introduction of a growing variety of micro vehicles for bridging last mile gaps (e.g. electric scooters, monowheels), or multimodal information and ticketing services to encourage changing monomodal car use. These solutions are based on a high-tech vision of future mobility composed of highly efficient automated vehicles of different shapes and sizes running on green and renewable energy, where people take responsibility and informed transport decisions resulting in a sustainable and just modal split, and where all road users share public space equitably and in harmony. Needless to say, that in this vision every individual is provided with at least the same or even higher “anywhere, anytime” transport guarantee as well as more personal comfort, all at a reasonable price.

Of course, most experts agree that this perfect future will most probably be hard if not impossible to reach, as scenario studies comparing less perfect and partly conflicting scenarios show [1], but in general it is wise to aim high for reaching at least the closest possible scenario near this vision. However, experiences with technology-boosted increase of vehicle efficiency and newly introduced sustainable sharing and pooling services show that technologies and digitalized services achieve significantly less impact than assumed, and it becomes more and more apparent that technological development will not suffice in reaching a sustainable and fair mobility system [2]. One of the main reasons for this frequent underperformance is that in many cases users of new technologies and services do not behave as expected, which results in rebound effects [3, 4]. Rebound effects occur because humans tend to assign costs of any kind (e.g. money or time) to fairly stable mental accounts [5]. That means that savings in a specific category get reinvested, resulting in an increase of demand compensating the savings. As an example, the transport system has been seeing increasing amounts of distances travelled per person during the last decades, but at the same time travel time budgets remain more or less on the same level, indicating that saved time is simply reinvested in longer distances [6]. Another example is the fact that increasing energy efficiency in cars leads to consumers buying larger cars and hence compensating the gain [7] or the indication that consumers buying “green” vehicles start driving more as they perceive it as less harmful [8]. For more recent digitalization developments, similar effects become suspected. While at present Mobility as a Service (MaaS) is seen by many as significant chance to shift people from private cars to other, more sustainable modes, some studies already hint to a plausible oppositional impact: multicar-households are less interested in MaaS than households with only one or no car, suggesting that MaaS may hardly be used as replacement of a second or third car in the household, but rather put more people from public transport into the more flexible and comfortable shared car, thus increasing the amount of vehicles on the streets [9]. Similarly, automated cars that seem to promise positive environmental effects may actually increase travel demand by encouraging people to move further away from the city center or work place due to a better utilization of travel time [10].

Examples like these suggest that it is worth to take a closer look on seemingly irrational mobility behaviours to avoid fallacies in the assumed effect of new services. Especially the increasing digitalization and incremental automation of vehicles and services, bearing highly promising opportunities to pave the way for a more sustainable and equitable mobility future, should be thoroughly rechecked from the perspective of behavioural phenomena. Such an approach complementing product and service development is vital for minimizing undesired or even backfiring effects of innovations that jeopardize the aspired vision for the overall mobility system, and it should not be limited to mere questions of “user acceptance”.

However, what makes the consideration of Human Factors during the development and implementation of new digitalized solutions difficult, is that individuals often find it hard to imagine a hypothetical situation and their most probable reaction to it, limiting the insights that can be expected from stated preference (SP) designs. On the other hand, there are only few studies having the chance to observe behavioural effects and collect revealed preferences (RP) regarding already implemented services or long-term experiments. Consequently, the assessment of Human Factor effects requires different approaches at different stages of developing a new digitalized service. This Topical Collection presents a wide range of approaches to grasp the Human Factors affecting the impact of technological improvements and digitalized services, spanning from analysing field experiences and psychosocial perspectives from actual users of a system or experimental users within a Living Lab, to more hypothetical explorations of personal dispositions and expressed needs in view of unfamiliar or future services.

The first paper, authored by Adelé and Dionisio [11] provides hands-on experience from a “smart” carpooling app that turns out to be less smart than expected. Based on the analysis of chat protocols, trip refusal information and qualitative interviews with users, practical limitations and psychosocial barriers of the app are identified. Part of the limitations are caused by the “smart matching” function of the app. The app uses mobility habits to predict future trips and to propose relevant matches. This procedure causes misunderstandings as it often remains unclear to the user whether proposals are made by users or the system. Additionally, the high number of bad matches leads to discomfort both for the one who refuses a trip and the one whose offer is refused. Due to the complexity of the matching task and the limitation of the system, the authors suggest giving a smaller role to the system and a stronger role to the end-user. Another conclusion of the study is that intelligent systems cannot replace human relations in the process of building trust. This is concluded from the extensive use of the chat function before a ride and the content analysis of the chats. The study also reveals that carpooling is socially and emotionally demanding as there is high insecurity about the appropriate behaviour at different stages of the journey (e.g. What to do after a refusal? Is it better to talk or not to talk in the car? Who should initiate the next shared ride?), which could be addressed in a best practice guide. Despite the outlined difficulties, users perceive carpooling as a good solution and positive experience, which should motivate to implement improvements as suggested in the paper.

In the second paper, Sjöman et al. [12] report experiences from a Living Lab in which economic information and incentives were tested with regard to their ability to motivate sustainable mobility transitions. The three tested interventions include: 1) making costs of participants’ car use transparent; 2) providing cheaper access to public transport during off-peak hours; 3) economic rewards for cycling. Nine car-owning participants who varied in socio-demographics, access to public transport and commuting mode were included in the study. During the 6-month study period, participants used a GPS-tracker app that collected individual travel information. Additionally, in-depth interviews with participants were conducted before, during, and after the Living Lab. During these interviews, participants’ travel behaviour and related attitudes, needs, and perceptions were explored. Participants were confronted with the yearly total costs from driving their own car compared with the hypothetical costs of a car sharing service. Generally, the differences were perceived as much too low to motivate a shift from private car ownership to car sharing. The results support findings from previous research showing that the perceived freedom, autonomy and convenience of car use are difficult to meet with alternative modes and services. The reason that alternative transport modes are often perceived as expensive could partly be explained by “unfair” comparisons people make, not taking the car’s full costs into account. The tested incentives for off-peak public transport use and cycling showed rather minor effects but the idea of rewarding cycling was mostly seen positive and could be effective in case of higher rewards as has already been demonstrated in an e-bike commuting study in the Netherlands [13].

The third paper by Chee et al. [14] examined the potential use of different automated vehicle (AV) services, or more specifically, which factors affect the willingness to pay for their use. Apart from a first/last mile automated bus service that was already in operation as part of an AV trial in Sweden, on-demand personalized AV services and demand responsive shared AV services were considered. Study participants were potential users of the service who lived, worked or studied in the area of the trial and about half of them had already taken at least one automated bus ride. A survey collected data on socio-demographics and commuter mode choice, perceptions about different AV attributes (e.g. safety, comfort, travel time) as well as the amount of money people were willing to pay for the three services on top of a regular monthly public transport pass. The survey showed that people who had tried the AV service perceived it as safe and comfortable and were more willing to pay for future on-demand personalized AV services. Results of separate structural equation models moreover showed that expectations towards each type of AV service differed. Apart from service quality expectations, AV rider experience, and income, willingness to pay differed by commuter mode choice and knowledge about AV technology: People who walked for daily trips perceived a negative ride comfort of AV services; people with greater knowledge about AV technology were more sceptical about AV safety.

While the first three studies were at least partly based on actual user experience, the two remaining papers deal with the acceptance and potential use of future mobility solutions based on descriptions of these services.

König and Grippenkoven [15] focus on the relevance of different service attributes for the adoption of a ridepooling service. Based on discrete choice experiments, they find that all considered attributes (fare, walking distance to the pick-up point, time of booking in advance, shift of departure time, travel time, information) significantly affected the choice of the service but that the appraisal of them differed depending on the trip purpose: In case of a doctor’s appointment, people were, for instance, more sensitive to a shift of departure time, an increase of travel time and walking distance to the pick-up point than were people for a shopping trip. Correspondingly, also the respondents’ willingness to pay for an improvement in the service attributes differed depending on the trip purpose. Based on the results, the authors suggest concrete recommendations for service providers, for example to avoid shifts of departure time shortly before the trip by freezing the time window for bookings. They expect further insights from considering additional trip purposes and differences in the assessment of service attributes based on sociodemographic variables.

The last paper by Winter et al. [16] identifies potential user groups of shared and automated mobility services. It is based on stated choice experiments, in which participants can choose between free-floating car-sharing, shared automated vehicles, private vehicles, busses, or taxis under a systematic variation of different time and cost related parameters. Based on latent class modelling, three user classes were identified: “Brisk Sharers” - mainly young adults who prefer shared modes over private modes and are very sensitive towards increases of travel time. “Public Transport Enthusiasts” - typically public transport commuters who are less time but more price sensitive. They are typically older but prefer shared modes to the same extent as brisk sharers. In contrast to that, “Car Captives” are current car commuters who dislike shared modes, tend to be older and less educated. The paper moreover shows that current car commuters are open for shared services but not for automation. While intermodal commuters, combining PT and the car, are most open to shared (automated) services, people commuting solely with PT are least open. In line with the results by Chee et al. [14], the study thus shows that current commuter mode choice is a relevant predictor of the acceptance of new car-based mobility services.

The examples given in this Topical Collection provide valuable knowledge for an improved consideration of the Human Factor perspective in digitalized mobility developments. Several learnings can be drawn from the contributions, which can help to achieve the originally intended impact of digitalized services.

One important insight is that individuals have to deal with a lot of insecurities when using a new or unfamiliar system, which can limit or even hinder the success of the service. Many of these insecurities concern the appropriate interaction with other users, others concern the system itself if it appears like a black box to the user and it is not clear what it actually does. Service developments should therefore ensure sufficient transparency of the system to help people understand its actions, and to clearly define the contribution of the system to the user’s decision basis. Similarly, systems supporting interactions between users should consider providing simple guidelines to suggest social rules and facilitate the exchange.

In many cases, the potential success of a service is estimated along its ability to help users in comparing different mobility options based on factual attributes like travel time, costs, potential incentives, and other measurable attributes. Findings within this Topical Collection however show, that personal motives and subconscious values people attribute to a specific mode or a trip purpose can easily override a “rational” decision. For future developments, it might therefore be advisable to pay more attention to symbolic and affective motives of mode choice than focusing solely on functional aspects. Approaches to decipher the emotional meaning behind reported preferences can help to shape a sustainable mobility alternative along such subconscious qualities and raise its acceptability.

From the methodological viewpoint, the contributions in this Topical Collection provide valuable experiences in exploring Human Factors, which is very useful for choosing the appropriate approach for specific questions. One take-away is that the more time people have to get familiar with a new service, the more accurate assessments of its applicability in the personal environment of the respondents can be achieved. The long-term approach of Living Labs is a beneficial experimental environment to observe changes in attitudes and behaviours. Also, systematic sampling can be helpful, as individuals who had at least some experience with the subject matter of the research can provide more reliable responses than a best guess from an unexperienced respondent. Moreover, methods identifying different behaviour profiles instead of targeting a heterogeneous mass of users can better explain differences in behaviour responses and related impact, and help to adjust services to the needs of potential user groups [17, 18].

Finally, we should also be aware of the fact that Human Factor phenomena are not limited to users. Humans operate at every level of the mobility system – researchers, developers, providers, decision makers – and although they may have more factual insights into the complex nature of mobility than the average citizen, no one is safe from interfering misperceptions and personal motives. But by widening our horizon beyond technological progress through understanding ourselves and others, we are likely to achieve more power in shaping the mobility future instead of having to deal with dead ends and sunken costs.

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AIT Austrian Institute of Technology, Vienna, Austria
Alexandra Millonig
Technical University of Denmark, Kgs. Lyngby, Denmark
Sonja Haustein

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Millonig, A., Haustein, S. Human factors of digitalized mobility forms and services. Eur. Transp. Res. Rev. 12, 46 (2020). https://doi.org/10.1186/s12544-020-00435-5

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Published: 06 July 2020
DOI: https://doi.org/10.1186/s12544-020-00435-5

中文翻译：

数字化出行方式和服务的人为因素

面对气候变化，城市交通拥挤，空气和噪声污染以及交通安全等紧迫挑战，人们进行了许多努力，旨在减少汽车交通并将道路使用者转变为更具可持续性的出行方式。在这方面，数字化和技术发展提供了支持这一发展的新机会。设想的解决方案包括通过共享和共享选项来提高车辆的能源效率以及车辆的能效，引入越来越多的微型车辆以弥合最后一英里的差距（例如，电动踏板车，单轮车）或多模式信息和票务服务鼓励改变单峰汽车的使用方式。这些解决方案基于对未来机动性的高科技愿景，该愿景由运行于绿色和可再生能源上的不同形状和尺寸的高效自动化车辆组成，人们在其中承担责任并做出明智的运输决策，从而实现可持续的，公正的模式划分，以及所有道路使用者均公平，和谐地共享公共空间。不用说，在这个愿景中，每个人都至少以合理的价格获得了至少相同甚至更高的“随时随地”运输保证以及更多的个人舒适感。

当然，大多数专家都认为，如对不太理想和部分矛盾的情景进行的情景研究显示[1]，即使不是不可能，也很难实现这个理想的未来，但总的来说，明智的做法是至少达到此愿景附近最接近的情况。但是，通过技术提高车辆效率和新引入的可持续共享与共享服务的经验表明，技术和数字化服务所产生的影响要比设想的要小得多，而且越来越明显的是，技术发展不足以实现可持续发展和可持续发展。公平流动系统[2]。经常出现性能不佳的主要原因之一是，在许多情况下，新技术和服务的用户的行为均不符合预期，导致反弹效果[3，4]。发生回弹效应是因为人类倾向于将任何种类的成本（例如金钱或时间）分配给相当稳定的心理账户[5]。这意味着可以将特定类别的储蓄重新投资，从而导致需求增加，从而弥补了储蓄。例如，在过去的几十年中，运输系统的人均出行距离不断增加，但与此同时，出行时间预算或多或少都保持在同一水平，这表明节省的时间只是简单地重新投资了更长的距离[ 6]。另一个例子是，提高汽车的能源效率会导致消费者购买更大的汽车，从而补偿收益[7]，或者表明购买“绿色”汽车的消费者开始驾驶更多，因为他们认为这种汽车危害较小[8]。对于最近的数字化发展，类似的效果也令人怀疑。虽然目前许多人认为移动即服务（MaaS）是将人们从私家车转移到其他更可持续的模式的重要机会，但一些研究已经暗示了可能的对立影响：与家庭相比，多车家庭对MaaS的兴趣较小仅使用一辆汽车或没有一辆汽车，这表明MaaS可能几乎不能替代家庭中的第二辆或第三辆汽车，而是将更多的公共交通人员置于更加灵活和舒适的共享汽车中，从而增加了街道[9]。同样，似乎可以带来积极环境影响的自动驾驶汽车实际上可能会通过鼓励人们由于更好地利用旅行时间而远离市中心或工作地点而实际上增加了旅行需求[10]。虽然目前许多人认为移动即服务（MaaS）是将人们从私家车转移到其他更可持续的模式的重要机会，但一些研究已经暗示了可能的对立影响：与家庭相比，多车家庭对MaaS的兴趣降低仅使用一辆汽车或没有一辆汽车，这表明MaaS可能几乎不能替代家庭中的第二辆或第三辆汽车，而是将更多的公共交通人员置于更加灵活和舒适的共享汽车中，从而增加了街道[9]。同样，似乎可以带来积极环境影响的自动驾驶汽车实际上可能会通过鼓励人们由于更好地利用旅行时间而远离市中心或工作地点而实际上增加了旅行需求[10]。虽然目前许多人认为移动即服务（MaaS）是将人们从私家车转移到其他更可持续的模式的重要机会，但一些研究已经暗示了可能的对立影响：与家庭相比，多车家庭对MaaS的兴趣降低仅使用一辆汽车或没有一辆汽车，这表明MaaS可能几乎不能替代家庭中的第二辆或第三辆汽车，而是将更多的公共交通人员置于更加灵活和舒适的共享汽车中，从而增加了街道[9]。同样，似乎可以带来积极环境影响的自动驾驶汽车实际上可能会通过鼓励人们由于更好地利用旅行时间而远离市中心或工作地点而实际上增加了旅行需求[10]。

像这样的例子表明，有必要仔细研究看似不合理的流动性行为，以免对新服务的假定效果产生谬误。尤其是，从行为现象的角度，应该彻底地重新检查日益增加的车辆和服务的数字化和自动化程度，这些机遇和机遇充满希望，为更可持续和公平的机动性未来铺平道路。这种对产品和服务开发进行补充的方法对于最大程度地减少不利于整体移动系统的理想愿景的创新的不良影响甚至是反击效果至关重要，它不应仅限于“用户接受度”问题。

但是，使得在开发和实施新的数字化解决方案时难以考虑人为因素的原因是，人们常常很难想象一种假设的情况及其对这种情况的最可能的反应，这限制了从陈述的偏好中可以预期的洞察力（SP）设计。另一方面，只有很少的研究有机会观察行为影响并收集有关已经实施的服务或长期实验的偏好（RP）的机会。因此，对人为因素影响的评估需要在开发新的数字化服务的不同阶段采用不同的方法。本专题收藏提供了多种方法来掌握影响技术进步和数字化服务影响的人为因素，

由Adelé和Dionisio [11]撰写的第一篇论文提供了“智能”拼车应用程序的动手经验，事实证明它比预期的要聪明。基于对聊天协议的分析，拒绝旅行的信息以及对用户的定性采访，确定了该应用的实际限制和心理障碍。部分限制是由应用程序的“智能匹配”功能引起的。该应用程序使用移动习惯来预测未来出行并提出相关匹配。此过程会引起误解，因为对于用户来说，提案是由用户还是由系统提出的，通常是不清楚的。另外，大量的不匹配比赛导致拒绝旅行的人和拒绝提供旅行的人都感到不舒服。由于匹配任务的复杂性和系统的局限性，作者建议赋予系统较小的角色，并赋予最终用户更大的角色。该研究的另一个结论是，智能系统无法在建立信任的过程中取代人际关系。这可以从乘车前广泛使用聊天功能以及对聊天内容进行分析得出结论。该研究还表明，拼车在社交和情感上都要求很高，因为在旅途的不同阶段对适当的行为存在高度的不安全感（例如，拒绝后该怎么办？在车里说话还是不说话更好？发起下一个共享旅程？），可以在最佳做法指南中解决。尽管遇到了上述困难，但用户仍将拼车视为很好的解决方案和积极的体验，

在第二篇论文中，Sjöman等人。[12]报告了来自生活实验室的经验，该实验在经济信息和激励措施方面进行了测试，以探讨其促进可持续交通转型的能力。经过测试的三个干预措施包括：1）使参与者的汽车使用成本透明化；2）在非高峰时段更便宜地使用公共交通工具；3）骑自行车的经济奖励。这项研究包括了九名拥有汽车的参与者，这些参与者在社会人口统计学，公共交通和通勤方式上各不相同。在为期6个月的研究期内，参与者使用了GPS追踪器应用，该应用收集了个人旅行信息。此外，在“生活实验室”之前，期间和之后都对参与者进行了深入访谈。在这些访谈中，参与者的旅行行为以及相关的态度，需求，和知觉被探索。与假设的共享汽车费用相比，与会人员面临着自己开车所产生的年度总成本。通常，人们认为差异太小，不足以促使人们从私人拥有汽车转向共享汽车。这些结果支持以前的研究结果，这些结果表明，使用替代方式和服务很难满足人们对汽车使用的自由，自主和便利的要求。人们通常认为替代运输方式很昂贵的原因可以部分由人们进行的“不公平”比较来解释，而不是将汽车的全部成本考虑在内。

Chee等人的第三篇论文。[14]研究了不同自动驾驶汽车（AV）服务的潜在用途，或更具体地讲，哪些因素影响了使用它们的付费意愿。除了已经在瑞典进行的自动驾驶汽车试验的一部分中已开始运行的第一英里/最后一英里自动公交服务之外，还考虑了按需个性化自动驾驶汽车服务和需求响应的共享自动驾驶汽车服务。研究参与者是在试验地区生活，工作或学习的服务的潜在用户，其中大约一半已经乘坐了至少一辆自动公交车。一项调查收集了有关社会人口统计资料和通勤方式选择，对不同AV属性（例如安全性，舒适性，出行时间）以及人们愿意在每月定期的公共交通通行证之上支付这三项服务的金额。调查显示，尝试过AV服务的人们认为它安全又舒适，并且更愿意为将来的按需个性化AV服务付费。此外，单独的结构方程模型的结果表明，对每种AV服务的期望都不同。除了对服务质量的期望，AV乘坐者的经验和收入，支付意愿因通勤方式选择和对AV技术的了解而有所不同：每天出行的人都对AV服务的乘坐舒适度感到失望；对视音频技术了解更多的人对视音频安全性更加怀疑。调查显示，尝试过AV服务的人们认为它安全又舒适，并且更愿意为将来的按需个性化AV服务付费。此外，单独的结构方程模型的结果表明，对每种AV服务的期望都不同。除了对服务质量的期望，AV乘坐者的经验和收入，支付意愿因通勤方式选择和对AV技术的了解而有所不同：每天出行的人都对AV服务的乘坐舒适度感到失望；对视音频技术了解更多的人对视音频安全性更加怀疑。调查显示，尝试过AV服务的人们认为它安全又舒适，并且更愿意为将来的按需个性化AV服务付费。此外，单独的结构方程模型的结果表明，对每种AV服务的期望都不同。除了对服务质量的期望，AV乘坐者的经验和收入，支付意愿因通勤方式选择和对AV技术的了解而有所不同：每天出行的人都对AV服务的乘坐舒适度感到失望；对视音频技术了解更多的人对视音频安全性更加怀疑。除了对服务质量的期望，AV乘坐者的经验和收入，支付意愿因通勤方式选择和对AV技术的了解而有所不同：每天出行的人都对AV服务的乘坐舒适度感到失望；对视音频技术了解更多的人对视音频安全性更加怀疑。除了对服务质量的期望，AV乘坐者的经验和收入，支付意愿因通勤方式选择和对AV技术的了解而有所不同：每天出行的人都对AV服务的乘坐舒适度感到失望；对视音频技术了解更多的人对视音频安全性更加怀疑。

前三项研究至少部分基于实际用户体验，而其余两篇论文则基于对这些服务的描述，讨论了对未来移动解决方案的接受和潜在使用。

König和Grippenkoven [15]专注于采用搭便车服务的不同服务属性的相关性。基于离散选择实验，他们发现所有考虑到的属性（票价，到接载点的步行距离，提前预订时间，出发时间的偏移，旅行时间，信息）都显着影响了服务的选择，但是对他们的评估因旅行目的而异：例如，在预约医生的情况下，人们对出发时间的变化，旅行时间和到达接载点的步行距离的敏感性比对人的敏感性高。购物之旅。相应地，受访者为改善服务属性付费的意愿也因旅行目的而异。根据结果，作者为服务提供商提出了具体建议，例如，通过冻结预订时间窗口来避免在出发前不久改变出发时间。他们希望通过考虑其他旅行目的以及基于社会人口统计学变量的服务属性评估中的差异来获得进一步的见解。

Winter等人的最后一篇论文。[16]确定了共享和自动移动服务的潜在用户组。它基于既定的选择实验，参与者可以在不同时间和成本相关参数的系统变化下，在自由浮动的汽车共享，共享的自动车辆，私家车，公共汽车或出租车之间进行选择。根据潜在类别建模，确定了三个用户类别：“轻快共享者”-主要是年轻人，他们更喜欢共享模式而不是私人模式，并且对旅行时间的增加非常敏感。“公共交通爱好者”-通常是公共交通通勤者，他们时间较少但对价格更敏感。他们通常年龄较大，但在某种程度上喜欢共享模式，就像活跃的共享者一样。与此相反，““汽车俘虏”是目前的汽车通勤者，他们不喜欢共享模式，年龄较大，教育程度较低。该文件还显示，当前的通勤者对共享服务开放，但对自动化不开放。虽然将PT和汽车相结合的联运通勤者对共享（自动）服务最开放，但仅使用PT上下班的人则开放度最低。与Chee等人的结果一致。[14]因此，研究表明，当前的通勤方式选择是接受新的基于汽车的出行服务的相关预测因子。

本专题收藏中的示例提供了宝贵的知识，可以更好地考虑数字化移动发展中对人为因素的看法。可以从这些贡献中吸取一些教训，这可以帮助实现数字化服务的最初预期影响。

一个重要的见解是，在使用新的或不熟悉的系统时，个人必须应对很多不安全感，这可能会限制甚至阻碍服务的成功。这些不安全因素中的许多都与与其他用户的适当交互有关，而其他方面则与系统本身有关，如果该系统对用户来说像黑匣子，并且不清楚其实际作用。因此，服务开发应确保系统具有足够的透明度，以帮助人们理解其操作，并清楚地定义系统对用户决策基础的贡献。同样，支持用户之间交互的系统应考虑提供简单的指南，以建议社交规则并促进交流。

在许多情况下，服务的潜在成功是根据其帮助用户根据实际属性（例如旅行时间，成本，潜在激励因素和其他可衡量的属性）比较不同的移动性选项的能力来估计的。然而，在该专题收集中的发现表明，人们归因于特定模式或出行目的的个人动机和潜意识价值观可以轻易地超越“理性”的决定。因此，对于未来的发展，建议将注意力更多地放在模式选择的象征性和情感动机上，而不是仅仅关注功能方面。解读所报告的偏好背后的情感含义的方法可以帮助塑造具有这种潜意识特质的可持续性出行方式，并提高其可接受性。

从方法论的角度来看，本主题集的贡献为探索人为因素提供了宝贵的经验，这对于选择针对特定问题的适当方法非常有用。一个收获是，人们必须花更多的时间来熟悉一项新服务，才能更准确地评估其在受访者个人环境中的适用性。Living Labs的长期方法是观察态度和行为变化的有益实验环境。此外，系统采样可能会有所帮助，因为至少对研究主题有一定经验的个人可以提供比没有经验的受访者的最佳猜测更可靠的答案。此外，

最后，我们还应该意识到人为因素现象不仅限于用户。人类在交通系统的各个层面（研究人员，开发人员，提供者，决策者）进行操作，尽管他们比一般公民对交通的复杂性有更多的事实见解，但没有人能够避免误解和个人动机。但是，通过了解自己和他人，将视野开阔到技术进步之外，我们可能会在塑造出行方式的未来方面获得更大的力量，而不必面对死胡同和沉没的成本。

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下载参考

隶属关系

AIT奥地利技术学院，奥地利维也纳
亚历山德拉·米洛尼格（Alexandra Millonig）
丹麦工业大学，KGS。丹麦林比
松雅·豪斯坦

亚历山德拉·米洛尼格（Alexandra Millonig）查看作者出版物
您也可以在PubMed Google学术搜索中搜索该作者
Sonja Haustein查看作者出版物
您也可以在PubMed Google学术搜索中搜索该作者

会费

作者阅读并批准了最终手稿。

通讯作者

对应于Sonja Haustein。

利益争夺

作者宣称他们没有竞争利益。

发行人须知

对于已发布地图和机构隶属关系中的管辖权主张，Springer Nature保持中立。

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