Spatial Meta Learning With Comprehensive Prior Knowledge Injection for Service Time Prediction
Shuliang Wang, Qianyu Yang, Sijie Ruan, Cheng Zhi Long, Ye Yuan, Qi Li, Ziqiang Yuan, Jie Bao, Yu Zheng · IEEE Transactions on Knowledge and Data Engineering · 2024
Intelligent logistics relies on accurately predicting the service time, which is a part of time cost in the last-mile delivery. However, service time prediction (STP) is non-trivial given complex delivery circumstances, location heterogeneity, and skewed observations in space, which are not well-handled by existing solutions. In our prior work, we treat STP at each location as a learning task to keep the location heterogeneity, propose a prior knowledge-enhanced meta-learning to tackle skewed observations, and introduce a Transformer-based representation module to encode complex delivery circumstances. Maintaining the design principles of prior work, in this extended paper, we propose MetaSTP+. In addition to fusing the prior knowledge after the meta-learning process, MetaSTP+also injects the prior knowledge before and during the meta-learning process to better tackle skewed observations. More specifically, MetaSTP+completes the support set of tasks with scarce samples from other tasks based on prior knowledge and is equipped with a prior knowledge-aware historical observation encoding module to achieve those purposes accordingly. Experiments show MetaSTP+outperforms the best baseline by 11.2% and 8.4% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP+is deployed in JD Logistics.