Urban mobility is on the cusp of transformation with the emergence of shared,
connected, and cooperative automated vehicles. Yet, for them to be accepted by
customers, trust in their punctuality is vital. Many pilot initiatives operate
without a fixed schedule, thus enhancing the importance of reliable arrival
time (AT) predictions. This study presents an AT prediction system for
autonomous shuttles, utilizing separate models for dwell and running time
predictions, validated on real-world data from five cities. Alongside
established methods such as XGBoost, we explore the benefits of integrating
spatial data using graph neural networks (GNN). To accurately handle the case
of a shuttle bypassing a stop, we propose a hierarchical model combining a
random forest classifier and a GNN. The results for the final AT prediction are
promising, showing low errors even when predicting several stops ahead. Yet, no
single model emerges as universally superior, and we provide insights into the
characteristics of pilot sites that influence the model selection process.
Finally, we identify dwell time prediction as the key determinant in overall AT
prediction accuracy when autonomous shuttles are deployed in low-traffic areas
or under regulatory speed limits. This research provides insights into the
current state of autonomous public transport prediction models and paves the
way for more data-informed decision-making as the field advances.

城市交通正在发生转变，通过共享、连接和协作的自动驾驶车辆的出现。然而，为了被顾客接受，对它们的准时性的信任至关重要。本研究提出了一种用于自主穿梭车的到达时间预测系统，利用分别针对停留时间和行驶时间的模型，并通过现实世界五个城市的数据进行验证。除了传统方法如 XGBoost 外，我们还探索了使用图神经网络（GNN）集成空间数据的好处。为了准确处理穿梭车跳过站点的情况，我们提出了一个分层模型，组合了随机森林分类器和 GNN。最终的到达时间预测结果是令人满意的，在预测多站点时也显示出较低的错误率。然而，并无一种单一模型在各方面都表现出优势，我们提供了影响模型选择过程的试点站点特征的见解。最后，我们确定停留时间预测是在低交通地区或受到监管限速时自动穿梭车整体到达时间预测准确性的关键决定因素。本研究为自主公共交通预测模型的现状提供了见解，并为领域的进一步发展铺平了道路。