In the effort to counteract problems associated with the current carbon intensive transport system, app-based tools persuading mobility behaviour change have emerged worldwide. Most of such apps adopt a gamified approach and motivate behaviour change through external extrinsic motivational factors such as real-life prizes, that are attributed based on the distance travelled by non-car transport modes. Despite this approach might be effective in promoting additional leisure trips by sustainable mobility, it might keep car-based commuting habits unaltered, or even stimulate unfair app behaviour to gain points. In this paper, we focus on the Bellidea persuasive app, that was co-designed with interested citizens in a Swiss-based living lab experiment, and present how we addressed the shortcomings of prize-based rewarding systems, while also dealing with the constraints imposed by current levels of accuracy in automatic transport mode detection. We illustrate and discuss our design choices and the related algorithmic solutions by referring to the following dilemmas: “single transport modes versus modal split”, “trust versus control”, “dynamism versus rigidity”, and “global versus local”. We conclude by analysing real-life mobility data-sets collected by the Bellidea app and discussing our design solutions against their capacity to attract its target user group, namely car driver individuals.

为了解决与当前碳密集型交通系统相关的问题，全球范围内出现了基于应用程序的工具来劝说移动行为的改变。大多数此类应用程序采用游戏化方法，并通过外部激励因素（例如现实生活中的奖品）来激发行为改变，这些因素是根据非汽车交通方式行驶的距离而定的。尽管这种方法可能会有效地通过可持续移动性促进更多的休闲旅行，但它可能会保持以汽车为基础的通勤习惯不变，甚至会刺激不公平的应用程序行为来获得积分。在本文中，我们关注Bellidea有说服力的应用程序，它是在瑞士的生活实验室实验中与感兴趣的公民共同设计的，并展示了我们如何解决基于奖励的奖励系统的缺点，同时还处理自动运输模式中当前准确性水平所施加的限制检测。我们通过参考以下困境来说明和讨论我们的设计选择和相关的算法解决方案：“单一运输模式与模式分离”、“信任与控制”、“动态与刚性”以及“全局与本地”。最后，我们分析了Bellidea应用程序收集的现实生活中的移动数据集，并根据它们吸引目标用户群（即汽车驾驶员个人）的能力讨论了我们的设计解决方案。