Graph2RETA: Graph Neural Networks for Pick-up and Delivery Route Prediction and Arrival Time Estimation

Wilson Sentanoe, Sreyashi Saha, Yasas Diyanananda, Aqsa Manzoor, Buddhika Dasanayake, Daniela Thyssens

Abstract: This research proposes an effective way to address the issues faced by pick-up and delivery services. The real-world variables that affect delivery routes are frequently overlooked by traditional routing technologies, resulting in differences between intended and actual trajectories. Similarly, the issue of forecasting the Estimated Time of Arrival involves unique challenges due to its high dimensionality. We suggest an integrated predictive modeling methodology that tackles routing prediction in a dynamic environment and ETA prediction at the same time to overcome these difficulties. Our method, Graph2RETA, uses a dynamic spatial-temporal graph-based model to forecast delivery workers’ future routing behaviors while integrating route inference into ETA prediction. Graph2RETA leverages rich decision context and spatial-temporal information to improve the prediction accuracy of the concurrent state-of-the-art while capturing dynamic interactions between workers and timesteps by incorporating the underlying graph structure and features.