用POI和时间特征增强BERT，提升人流预测准确率
Classical Feature Embeddings Help in BERT-Based Human Mobility Prediction
- 将POI和时间信息融合进BERT，构建语义丰富的移动表示
- 单城预测GEO-BLEU达0.75，多城达0.56，显著提升
- 适合城市规划、公共健康等需精准人流预测的场景
人流预测对灾害救援、城市规划和公共卫生至关重要。现有模型通常仅建模位置序列，或仅将时间作为辅助输入，未能充分利用兴趣点（POIs）提供的丰富语义上下文。为此，我们通过引入派生的时间描述符和POI嵌入，增强基于BERT的移动性模型。提出STaBERT（语义-时间感知BERT），在每个位置融合POI与时间信息，构建统一的语义丰富移动表征。实验表明，STaBERT显著提升预测精度：单城预测中GEO-BLEU从0.34提升至0.75；多城预测从0.34提升至0.56。
原文摘要 · Abstract (English)
Human mobility forecasting is crucial for disaster relief, city planning, and public health. However, existing models either only model location sequences or include time information merely as auxiliary input, thereby failing to leverage the rich semantic context provided by points of interest (POIs). To address this, we enrich a BERT-based mobility model with derived temporal descriptors and POI embeddings to better capture the semantics underlying human movement. We propose STaBERT (Semantic-Temporal aware BERT), which integrates both POI and temporal information at each location to construct a unified, semantically enriched representation of mobility. Experimental results show that STaBERT significantly improves prediction accuracy: for single-city prediction, the GEO-BLEU score improved from 0.34 to 0.75; for multi-city prediction, from 0.34 to 0.56.
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