Large language models (LLMs) offer new opportunities for simulating human decision making at scale, yet prompting-based approaches often fail to recover realistic population-level response distributions, particularly for heterogeneous demographic groups and high-entropy questions. This paper introduces context modeling with agentic AI, a structured simulation framework that explicitly constructs latent, individual-level decision contexts prior to response generation. By separating persona construction from decision making and introducing structured stochasticity through context sampling, the method captures within-group heterogeneity and mitigates mode collapse inherent in single-prompt simulations. Extensive empirical evaluation across population-level and group-conditioned settings shows that context modeling consistently improves simulation fidelity, with especially strong gains on high-entropy questions and demographic subpopulations, while preserving performance on low-entropy tasks. Detailed tail analysis further reveals that the primary improvement arises from recovering minority and low-probability response options that are systematically underrepresented by direct prompting. Overall, the results demonstrate that faithful behavioral simulation requires explicit modeling of contextual heterogeneity and structured uncertainty, establishing context modeling as a robust foundation for LLM-based behavioral simulation.

GPS trajectories encode rich behavioral information about how people move, organize activities, and form daily routines. Recent advances in large language models (LLMs) raise a natural question: can such models infer and summarize travel behavior directly from mobility traces? This paper introduces TravelBehaviorQA, a large-scale benchmark dataset that reframes trajectory analysis as a language-based behavioral understanding task. The dataset links raw GPS trajectories with human-grounded question-answering (QA) pairs that capture travel intensity, temporal structure, activity patterns, mode usage, and behavioral routines. Unlike prior mobility datasets focused on prediction or classification, TravelBehaviorQA emphasizes semantic interpretation through a unified mix of deterministic and open-ended questions. In this benchmark, we construct over 143k QA instances spanning users and years, and evaluate a broad range of state-of-the-art LLMs under controlled settings. Our results reveal substantial gaps between factual extraction and genuine behavioral reasoning, showing that model scale alone is insufficient and that trajectory representation is a primary bottleneck. TravelBehaviorQA exposes critical limitations of current models and establishes a rigorous benchmark for advancing language-based understanding of human mobility behavior.