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, enhancing the importance of reliable arrival time (AT) predictions. This study presents an AT prediction system for automated shuttles, utilizing separate models for dwell and running time predictions, validated on real-world data from six cities. Alongside established methods such as XGBoost, we explore the benefits of leveraging spatial correlations 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 and prediction performance. Finally, we identify dwell time prediction as the key determinant in overall AT prediction accuracy when automated shuttles are deployed in low-traffic areas or under regulatory speed limits. Our meta-analysis across six pilot sites in different cities 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.

翻译：随着共享化、网联化、协同化自动驾驶车辆的出现，城市交通正处于变革的边缘。然而，要让乘客接受此类服务，对其准点性的信任至关重要。许多试点项目采用非固定时刻表运营，这使得可靠的到站时间预测变得尤为重要。本研究提出了一种用于自动驾驶接驳车的到站时间预测系统，该系统采用独立的停靠时间与行驶时间预测模型，并在来自六个城市的真实世界数据上进行了验证。除了XGBoost等成熟方法外，我们还探索了利用图神经网络通过空间相关性带来的优势。为了准确处理接驳车过站不停的情况，我们提出了一种结合随机森林分类器与图神经网络的分层模型。最终到站时间预测的结果令人鼓舞，即使在预测多个站点后的到站时间时也显示出较低误差。然而，没有单一模型表现出普遍优越性，我们深入分析了试点区域的特征如何影响模型选择过程与预测性能。最后，我们发现，当自动驾驶接驳车部署在低交通流量区域或受法规限速约束时，停靠时间预测是整体到站时间预测精度的关键决定因素。我们对六个不同城市试点项目的荟萃分析，揭示了当前自动驾驶公共交通预测模型的发展现状，并为该领域发展进程中更多基于数据的决策铺平了道路。