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Urban mobility is on the cusp of transformation with the emergence of shared,connected, and cooperative automated vehicles. Yet, for them to be accepted bycustomers, trust in their punctuality is vital. Many pilot initiatives operatewithout a fixed schedule, thus enhancing the importance of reliable arrivaltime (AT) predictions. This study presents an AT prediction system forautonomous shuttles, utilizing separate models for dwell and running timepredictions, validated on real-world data from five cities. Alongsideestablished methods such as XGBoost, we explore the benefits of integratingspatial data using graph neural networks (GNN). To accurately handle the caseof a shuttle bypassing a stop, we propose a hierarchical model combining arandom forest classifier and a GNN. The results for the final AT prediction arepromising, showing low errors even when predicting several stops ahead. Yet, nosingle model emerges as universally superior, and we provide insights into thecharacteristics of pilot sites that influence the model selection process.Finally, we identify dwell time prediction as the key determinant in overall ATprediction accuracy when autonomous shuttles are deployed in low-traffic areasor under regulatory speed limits. This research provides insights into thecurrent state of autonomous public transport prediction models and paves theway for more data-informed decision-making as the field advances.

Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities