The use of smartphone applications (apps) to acquire real time and readily available journey planning information is becoming instinctive behavior by public transport (PT) users. Through the apps, a passenger not only seeks a path from origin to destination, but a satisfactory path that caters to the passenger’s preferences at the desired time of travel. Essentially, apps attempt to provide a means of personalized PT service. As the implications of the Covid-19 pandemic take form and infiltrate human and environmental interactions, passenger preference personalization will likely include avoiding risks of infection or contagious contact. The personal preferences are enabled by multiple attributes associated with alternative PT routes. For instance, preferences can be connected to attributes of time, cost, and convenience.

This work establishes a personalized PT service, as an adjustment to current design frameworks, by integrating user app experience with operators’ data sources and operations modeling. The work proceeds to focus on its key component: the personalized route guidance methodology. In addition to using the existing shortest path or k-weighted shortest path method, this study develops a novel, lexicographical shortest path method, considering a just noticeable difference (JND). The method adopts lexicographical ordering to capture passenger preferences for different PT attributes following Ernst Weber’s law of human perception threshold. However, a direct application of Weber’s law violates the axiom of transitivity required for an implementable algorithm, and thus, a revised method is developed with proven algorithms for ranking different paths. The differences between the three route-guidance methods and the effects of the JND perception threshold on the order of the alternative PT routes are demonstrated with an example.

The developments were examined in a case study by simulation on the Copenhagen PT network. The results show that using the JND method reduces the value/cost of the most important attributes. Identical robust results are attained when JND parameters are not specified and default values are used. The latter may apply for the future with a mixture of specified and default preference input values. Finally, the computation time indicates a favorable potential for real-life applications. It is believed that the consideration of human threshold perception will encourage decision makers to establish new criteria to comply with this.

公共交通（PT）用户使用智能手机应用程序（app）来获取实时和随时可用的行程计划信息正成为一种本能行为。通过这些应用程序，乘客不仅可以找到从起点到目的地的路径，还可以找到一条令人满意的路径，以在期望的旅行时间满足乘客的喜好。从本质上讲，应用程序试图提供一种个性化的PT服务。随着Covid-19大流行的影响逐渐形成并渗透到人与环境的相互作用中，旅客的个性化偏好将可能包括避免感染或传染性接触的风险。通过与替代PT路由关联的多个属性启用个人首选项。例如，首选项可以与时间，成本和便利性相关。

这项工作通过将用户应用程序体验与运营商的数据源和运营建模相集成，建立了个性化的PT服务，作为对当前设计框架的调整。这项工作着重于其关键组成部分：个性化路线导航方法。除了使用现有的最短路径或k-加权最短路径方法，这项研究考虑了一个明显的差异（JND），提出了一种新颖的词典最短路径方法。该方法采用字典顺序，以遵循恩斯特·韦伯的人类感知阈值定律来捕获乘客对不同PT属性的偏好。然而，韦伯定律的直接应用违反了可实现算法所要求的传递性公理，因此，开发了一种经过修改的方法，其中使用了行之有效的算法来对不同路径进行排名。通过示例演示了三种路线引导方法之间的差异以及JND感知阈值对替代PT路线顺序的影响。

通过在哥本哈根PT网络上进行的模拟案例研究，对开发情况进行了检查。结果表明，使用JND方法可以降低最重要属性的价值/成本。如果未指定JND参数并且使用默认值，则可获得相同的鲁棒结果。后者可能会结合指定的和默认的首选项输入值来应用于将来。最后，计算时间表明了在现实生活中的应用潜力。人们认为，对人类阈值感知的考虑将鼓励决策者建立新的标准来遵守这一标准。