Delivery time prediction using large-scale graph structure learning based on quantile regression

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A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects

H Wen, Y Lin, L Wu, X Mao, T Cai, Y Hou… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Instant delivery services, such as food delivery and package delivery, have achieved
explosive growth in recent years by providing customers with daily-life convenience. An …
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Estimating package arrival time via heterogeneous hypergraph neural network

L Zhang, X Wu, Y Liu, X Zhou, Y Cao, Y Xu… - Expert Systems with …, 2024 - Elsevier
Abstract Estimated Time of Arrival (ETA) for packages plays an essential role in intelligent
logistics. As a classic ETA method, Origin–Destination-based (OD-based) ETA predicts the …
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Advancing Sustainability via Recommender Systems: A Survey

X Zhou, L Zhang, H Zhang, Y Zhang, X Zhang… - ar**
experience and improve the placement rate of products. This problem is often formalized as …
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Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing

J Wei, Z Ye, C Yang, C Chen, G Ma - Proceedings of the 33rd ACM …, 2024 - dl.acm.org
Accurate prediction of Order Fulfillment Cycle Time (OFCT) is essential for improving
customer satisfaction and operational efficiency within the domain of on-demand grocery …
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TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories

M Li, J Chen, G Jiang, F Li, R Zhang, S Gong… - arxiv preprint arxiv …, 2024 - arxiv.org
Accurately estimating time of arrival (ETA) for trucks is crucial for optimizing transportation
efficiency in logistics. GPS trajectory data offers valuable information for ETA, but challenges …
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