用注意力机制建模司机闲置行为，提升平台留存预测精度。
Frailty-Aware Transformer for Recurrent Survival Modeling of Driver Retention in Ride-Hailing Platforms
- 基于因果掩码的Transformer捕捉司机行为长期依赖。
- 在多伦多数据上时间依赖C指数最高，贝叶斯分数最低。
- 适合平台优化司机留存策略与个性化干预。
网约车平台具有高频、行为驱动的特点。尽管生存分析已用于其他领域的复发事件建模，但在网约车司机行为研究中仍鲜有应用。本研究将司机闲置行为建模为复发生存过程，利用大规模平台数据，提出一种基于Transformer的框架，通过因果掩码捕捉长期时序依赖，并引入司机特定嵌入以建模潜在异质性。在多伦多网约车数据上的实验表明，所提出的韧性感知柯克斯Transformer（FACT）在时间依赖C指数和贝叶斯评分上均优于经典及深度学习生存模型。该方法可实现更精准的风险评估，支持平台留存策略制定，并提供政策相关洞见。
原文摘要 · Abstract (English)
Ride-hailing platforms are characterized by high-frequency, behavior-driven environments. Although survival analysis has been applied to recurrent events in other domains, its use in modeling ride-hailing driver behavior remains largely unexplored. This study formulates idle behavior as a recurrent survival process using large-scale platform data and proposes a Transformer-based framework that captures long-term temporal dependencies with causal masking and incorporates driver-specific embeddings to model latent heterogeneity. Results on Toronto ride-hailing data demonstrate that the proposed Frailty-Aware Cox Transformer (FACT) achieves the highest time-dependent C-indices and lowest Brier Scores, outperforming classical and deep learning survival models. This approach enables more accurate risk estimation, supports platform retention strategies, and provides policy-relevant insights.
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