Managing expectations is vital to ensuring commuter satisfaction with their public transportation service. When customers are given an estimated time for their bus and must wait way longer, they lose a sense of control over their commute and become frustrated. In this paper, we present a novel machine learning (ML) modeling approach in which we train and implement specialized models for every single segment of travel time and stop dwell time in our system to capture its uniqueness. The features for training the models include simple calendar and weather data. Most papers assume ideal operational conditions but in practice that’s almost never the case. Here, we combine our ML modeling approach with a flexible production-tested algorithm to combine model-generated dwell and travel time (or run time) predictions to produce predicted bus departure times. This algorithm is designed to handle real transit agency challenges like missing models (including those caused by changes to schedules and routes), timing points, and partially traveled segments. We also provide a reference architecture of how this algorithm can be brought to life in a scalable and cost-effective manner.

管理期望对于确保通勤者对其公共交通服务的满意度至关重要。当给客户估计的公交车时间并且必须等待更长的时间时，他们会失去对通勤的控制感，并感到沮丧。在本文中，我们提出了一种新颖的机器学习（ML）建模方法，在该方法中，我们针对行进时间的每个片段和在系统中的停留停留时间进行训练和实现专用模型，以捕获其独特性。训练模型的功能包括简单的日历和天气数据。大多数论文都假设理想的操作条件，但实际上绝不是这种情况。这里，我们将ML建模方法与经过生产测试的灵活算法相结合，以将模型生成的停留时间和行驶时间（或运行时间）预测相结合，以产生预测的公交车出发时间。该算法旨在处理实际的运输代理机构挑战，例如缺少模型（包括由于时间表和路线的更改而引起的模型），时间点和部分旅行的路段。我们还提供了一种参考体系结构，说明如何以可伸缩且经济高效的方式使该算法生效。