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

• URL: http://arxiv.org/abs/2509.18181v1
• Date: Thu, 18 Sep 2025 01:52:27 GMT
• Title: Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling
• Authors: Mustafa Sameen, Xiaojian Zhang, Xilei Zhao,
• Abstract summary: Existing models for predicting ridesourcing mode choices often suffer from limited predictive accuracy due to their inability to capture key psychological factors.<n>This paper introduces the Synthesizing Attitudes, Predicting Actions (SAPA) framework to synthesize theory-grounded latent attitudes.<n> Experiments on a large-scale, multi-year travel survey show that SAPA significantly outperforms state-of-the-art baselines.
• Score: 4.901179513819295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Did Models Sufficient Learn? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation [61.248535801314375]
  Subset-Selected Counterfactual Augmentation (SS-CA)<n>We develop Counterfactual LIMA to identify minimal spatial region sets whose removal can selectively alter model predictions.<n>Experiments show that SS-CA improves generalization on in-distribution (ID) test data and achieves superior performance on out-of-distribution (OOD) benchmarks.
  arXiv  Detail & Related papers  (2025-11-15T08:39:22Z)
• Towards Locally Deployable Fine-Tuned Causal Large Language Models for Mode Choice Behaviour [4.378407481656902]
  This study investigates the adoption of open-access, locally deployable causal large language models (LLMs) for travel mode choice prediction.<n>We benchmark eleven LLMs across three stated and revealed preference datasets, testing 396 configurations and generating over 79,000 synthetic commuter predictions.<n>LiTransMC, fine-tuned using parameter efficient and loss masking strategy, achieved a weighted F1 score of 0.6845 and a Jensen-Shannon Divergence of 0.000245.
  arXiv  Detail & Related papers  (2025-07-29T02:03:37Z)
• Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction [2.2974830861901414]
  This study uses data on travel satisfaction from a household survey in shanghai to identify the existence and source of misalignment between Large Language Models and human behavior.<n>We find that the zero-shot LLM exhibits behavioral misalignment, resulting in relatively low prediction accuracy.<n>We propose an LLM-based modeling approach that can be applied to model travel behavior using samples of small sizes.
  arXiv  Detail & Related papers  (2025-05-29T09:11:58Z)
• Aligning LLM with human travel choices: a persona-based embedding learning approach [15.11130742093296]
  This paper introduces a novel framework for aligning large language models with human travel choice behavior.<n>Our framework uses a persona inference and loading process to condition LLMs with suitable prompts to enhance alignment.
  arXiv  Detail & Related papers  (2025-05-25T06:54:01Z)
• Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning [4.345382237366071]
  This study introduces SILIC, short for Sociodemographic Inference with LLM-guided Inverse Reinforcement Learning (IRL) and Cognitive Chain Reasoning ( CCR)<n> CCR infers sociodemographic attributes from observed mobility patterns by capturing latent behavioral intentions and reasoning through psychological constructs.<n>Our method substantially outperforms state-of-the-art baselines and shows great promise for enriching big trajectory data to support behaviorally grounded applications in transportation planning and beyond.
  arXiv  Detail & Related papers  (2025-05-22T19:56:03Z)
• Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling [87.17041933863041]
  Reinforcement Learning from Human Feedback (RLHF) has achieved considerable success in aligning large language models (LLMs)<n>We introduce a $textbfR$esponse-$textbfc$onditioned $textbfB$radley-$textbfT$erry (Rc-BT) model that enhances the model's capability in length bias mitigating and length instruction following.<n>We also propose the Rc-RM and Rc-DPO algorithm to leverage the Rc-BT model for reward modeling and direct policy optimization
  arXiv  Detail & Related papers  (2025-02-02T14:50:25Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach [6.913791588789051]
  We introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions. We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland. The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices.
  arXiv  Detail & Related papers  (2024-06-19T13:46:08Z)
• Deep Bayesian Active Learning for Preference Modeling in Large Language Models [84.817400962262]
  We propose the Bayesian Active Learner for Preference Modeling (BAL-PM) for Preference Modeling. BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous Bayesian acquisition policies. Our experiments demonstrate that BAL-PM requires 33% to 68% fewer preference labels in two popular human preference datasets and exceeds previous Bayesian acquisition policies.
  arXiv  Detail & Related papers  (2024-06-14T13:32:43Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago [0.0]
  This study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone travel demand for ridesourcing services. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago.
  arXiv  Detail & Related papers  (2021-09-08T04:58:29Z)
• Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network [5.000272778136268]
  This study shows that the predictive coding (PC) and active inference (AIF) frameworks can develop better generalization by learning a prior distribution in a low dimensional latent state space. In our proposed model, learning is carried out by inferring optimal latent variables as well as synaptic weights for maximizing the evidence lower bound. Our proposed model was evaluated with both simple and complex robotic tasks in simulation, which demonstrated sufficient generalization in learning with limited training data.
  arXiv  Detail & Related papers  (2020-05-27T06:43:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.