This paper presents an algorithm to find multimodal shortest hyperpaths in a transport system where transit arrival is random (i.e. random-arrival transit system), while real-time information on vehicle arrivals is only available for some lines and modes. When the algorithm is queried, there is a short horizon where real-time information is accurate, and past this the most reliable information for estimating the arrivals is to use a random distribution specific to the lines. This problem occurs frequently in emerging cities where the transit schedules are not maintained. A Combined Real-Time Hypergraph is constructed to model the multimodal transport system where all the public transport modes have random arrivals and real-time information on arrivals is available for some lines of some modes. In order to model the period of time when some lines have real-time information, the composition of the head nodes of hyperarcs changes over time. The algorithm is tested on a real-life transport system where we change the number of lines with available real-time information to assess the performance of the algorithm in different scenarios. We found that incrementing the number of lines with real-time information does not impact the performance.

本文提出了一种算法，用于在运输到达为随机的运输系统（即随机到达运输系统）中找到多模式最短超路径，而有关车辆到达的实时信息仅适用于某些线路和模式。当查询该算法时，实时信息很准确，因此在很短的时间内，估计到达的最可靠信息是使用特定于线路的随机分布。在没有维护公交时间表的新兴城市中，经常会出现此问题。构建组合实时超图以对多式联运系统建模，在该系统中，所有公共运输模式均具有随机到达，并且某些模式的某些线路可提供有关到达的实时信息。为了建模某些线具有实时信息的时间段，超弧的头节点的组成会随时间而变化。在实际运输系统上测试了该算法，在该系统中，我们使用可用的实时信息更改了行数，以评估该算法在不同情况下的性能。我们发现使用实时信息增加行数不会影响性能。