Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling

2025-09-17

AI Summary

Existing ride-pooling mode choice prediction models suffer from limited accuracy due to the neglect of critical psychological factors and severe class imbalance—stemming from the low prevalence of ride-pooling trips. To address these limitations, this paper proposes a hierarchical prediction framework integrating behavioral theory with large language models (LLMs). Specifically, it leverages LLMs for the first time to generate qualitative user profiles and quantitative latent variable scores grounded in psychological dimensions. These are combined with propensity score matching and interaction-based feature engineering to jointly model demographic attributes, historical travel behavior, and trip-specific characteristics. Evaluated on a large-scale, multi-year travel survey dataset, the framework achieves up to a 75.9% improvement in PR-AUC over state-of-the-art baselines. It significantly enhances ride-hailing mode choice prediction accuracy while offering interpretability and robustness—establishing a novel paradigm for travel behavior modeling.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Improving predictive accuracy of ridesourcing mode choice models
• Addressing class imbalance in ridesourcing trip data
• Capturing psychological factors in travel behavior modeling

Innovation

• LLMs synthesize traveler personas from survey data
• Hierarchical framework integrates propensity scores and latent variables
• Theory-guided latent attitudes improve ridesourcing choice predictions