用专家混合+BERT预测跨城市长期人流，准确率提升8.29%。
ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction
- 将跨城人流预测转为时空分类任务，结合专家混合与BERT建模
- 在GEO-BLEU和DTW指标上平均提升8.29%，优于主流方法
- 适合解决数据稀缺的跨城市人流预测场景
由于不同城市环境中的复杂多变时空动态，跨城市人类移动预测面临巨大挑战。本文提出一种名为ST-MoE-BERT的鲁棒方法，将预测任务定义为时空分类问题。该方法将专家混合（Mixture-of-Experts）架构与BERT模型结合，以捕捉复杂的移动动态并完成下游预测任务。同时，引入迁移学习缓解跨城市预测中的数据稀缺问题。我们在GEO-BLEU和DTW两个指标上验证了该模型的有效性，结果表明其相比多个先进方法平均提升8.29%。
原文摘要 · Abstract (English)
Predicting human mobility across multiple cities presents significant challenges due to the complex and diverse spatial-temporal dynamics inherent in different urban environments. In this study, we propose a robust approach to predict human mobility patterns called ST-MoE-BERT. Compared to existing methods, our approach frames the prediction task as a spatial-temporal classification problem. Our methodology integrates the Mixture-of-Experts architecture with BERT model to capture complex mobility dynamics and perform the downstream human mobility prediction task. Additionally, transfer learning is integrated to solve the challenge of data scarcity in cross-city prediction. We demonstrate the effectiveness of the proposed model on GEO-BLEU and DTW, comparing it to several state-of-the-art methods. Notably, ST-MoE-BERT achieves an average improvement of 8.29%.
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