综合态度、预测行动（SAPA）：行为理论引导的大语言模型用于网约车出行方式选择建模
Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling
AI总结：
本文提出SAPA框架，利用大语言模型从出行调查数据中合成基于行为理论的潜在态度，并结合倾向得分与出行属性预测网约车选择，实验显示其PR-AUC较最先进基线提升高达75.9%。
摘要：
对网约车出行方式选择进行准确建模，对于设计和实施有效的交通管理政策至关重要，这些政策旨在减少拥堵、改善出行效率并更高效地配置资源。现有的网约车出行方式选择预测模型往往因无法捕捉关键心理因素而预测精度有限，同时还面临严重的类别不平衡问题，因为网约车出行仅占个体日常出行的一小部分。为解决这些局限，本文提出了综合态度、预测行动（SAPA）框架，这是一种分层方法，利用大语言模型（LLMs）合成基于理论基础的潜在态度，以预测网约车选择。SAPA首先使用大语言模型从原始出行调查数据中生成定性出行者画像，然后在人口统计和行为特征上训练倾向得分模型，并以这些画像作为补充，生成个体层面的得分。接着，大语言模型为理论驱动的潜在变量（如时间敏感性和成本敏感性）赋予定量得分，最终分类器整合倾向得分、潜在变量得分（及其交互项）以及可观测的出行属性，以预测网约车出行方式选择。在大规模、多年期出行调查上的实验表明，SAPA显著优于最先进的基线方法，在留出测试集上以PR-AUC衡量，网约车选择预测性能提升高达75.9%。本研究为准确预测网约车出行方式选择提供了有力工具，并提供了一种易于迁移到各种应用场景的方法论。
英文摘要：
Accurate modeling of ridesourcing mode choices is essential for designing and implementing effective traffic management policies for reducing congestion, improving mobility, and allocating resources more efficiently. Existing models for predicting ridesourcing mode choices often suffer from limited predictive accuracy due to their inability to capture key psychological factors, and are further challenged by severe class imbalance, as ridesourcing trips comprise only a small fraction of individuals' daily travel. To address these limitations, this paper introduces the Synthesizing Attitudes, Predicting Actions (SAPA) framework, a hierarchical approach that uses Large Language Models (LLMs) to synthesize theory-grounded latent attitudes to predict ridesourcing choices. SAPA first uses an LLM to generate qualitative traveler personas from raw travel survey data and then trains a propensity-score model on demographic and behavioral features, enriched by those personas, to produce an individual-level score. Next, the LLM assigns quantitative scores to theory-driven latent variables (e.g., time and cost sensitivity), and a final classifier integrates the propensity score, latent-variable scores (with their interaction terms), and observable trip attributes to predict ridesourcing mode choice. Experiments on a large-scale, multi-year travel survey show that SAPA significantly outperforms state-of-the-art baselines, improving ridesourcing choice predictions by up to 75.9% in terms of PR-AUC on a held-out test set. This study provides a powerful tool for accurately predicting ridesourcing mode choices, and provides a methodology that is readily transferable to various applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
