DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions

2023-10-21

International Conference on Information and Knowledge Management

AI Summary

To address the low prediction accuracy of logistics on-time delivery rates during anomalous events (e.g., pandemics), the lack of explicit anomaly modeling, and performance degradation caused by data sparsity, this paper proposes an end-to-end spatiotemporal attention model. Methodologically, it introduces (1) a novel Anomalous Spatiotemporal Learning module that explicitly encodes infrequent anomalous events, and (2) an Anomaly Pattern Memory Attention mechanism integrating Node2Vec, graph neural networks, LSTM, and an external memory network to mitigate data sparsity. Evaluated on real-world pandemic-related logistics data from 2022, the model achieves 12.11% and 13.71% reductions in MAE and MSE, respectively, significantly outperforming mainstream baselines. The proposed approach effectively supports proactive scheduling and emergency response decision-making in logistics operations under disruptions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Time-Series/Data Streams

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of delivery services. This becomes even more critical during anomaly conditions like the epidemic outbreak, during which couriers' delivery timely rate will decline markedly and fluctuates significantly. Existing studies pay less attention to the logistics scenario. Moreover, many works focusing on prediction tasks in anomaly scenarios fail to explicitly model abnormal events, e.g., treating external factors equally with other features, resulting in great information loss. Further, since some anomalous events occur infrequently, traditional data-driven methods perform poorly in these scenarios. To deal with them, we propose a deep spatial-temporal attention model, named DeepSTA. To be specific, to avoid information loss, we design an anomaly spatio-temporal learning module that employs a recurrent neural network to model incident information. Additionally, we utilize Node2vec to model correlations between road districts, and adopt graph neural networks and long short-term memory to capture the spatial-temporal dependencies of couriers. To tackle the issue of insufficient training data in abnormal circumstances, we propose an anomaly pattern attention module that adopts a memory network for couriers' anomaly feature patterns storage via attention mechanisms. The experiments on real-world logistics datasets during the COVID-19 outbreak in 2022 show the model outperforms the best baselines by 12.11% in MAE and 13.71% in MSE, demonstrating its superior performance over multiple competitive baselines.

Problem

• Predicting couriers' delivery timely rates during anomalies like epidemics
• Modeling abnormal events explicitly to avoid information loss
• Addressing data scarcity in anomaly scenarios with memory networks

Innovation

• Anomaly spatio-temporal learning module with RNN
• Node2vec and GNN for spatial-temporal dependencies
• Memory network for anomaly feature patterns