PEMANT: Persona-Enriched Multi-Agent Negotiation for Travel

arXiv cs.AI / 4/14/2026

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Key Points

家庭（世帯）単位の移動生成を、需要予測・交通流推定・都市計画に活用することを目的に、従来研究の限界（古典的ML中心で予測力が限定的、LLM手法が行動理論や世帯内相互作用を十分に取り込めていない点）を指摘しています。
提案手法PEMANTは、行動理論に基づいて世帯内の態度・主観的規範・行動の認知的統制感を含む「パーソナ」表現を、静的な社会人口属性から物語的プロファイルへ変換して作ります。
そのパーソナを用い、世帯レベルの旅行計画を、構造化されたマルチエージェント会話（2フェーズ）とパーソナ整合（persona-alignment）制御メカニズムで交渉・合意形成する枠組みを採用しています。
国レベルおよび地域レベルの家庭旅行調査データセットで評価した結果、PEMANTは複数データセットにわたり既存の最先端ベンチマークを一貫して上回ると報告しています。

Abstract

Modeling household-level trip generation is fundamental to accurate demand forecasting, traffic flow estimation, and urban system planning. Existing studies were mostly based on classical machine learning models with limited predictive capability, while recent LLM-based approaches have yet to incorporate behavioral theory or intra-household interaction dynamics, both of which are critical for modeling realistic collective travel decisions. To address these limitations, we propose a novel LLM-based framework, named Persona-Enriched Multi-Agent Negotiation for Travel (PEMANT), which first integrates behavioral theory for individualized persona modeling and then conducts household-level trip planning negotiations via a structured multi-agent conversation. Specifically, PEMANT transforms static sociodemographic attributes into coherent narrative profiles that explicitly encode household-level attitudes, subjective norms, and perceived behavioral controls, following our proposed Household-Aware Chain-of-Planned-Behavior (HA-CoPB) framework. Building on these theory-grounded personas, PEMANT captures real-world household decision negotiation via a structured two-phase multi-agent conversation framework with a novel persona-alignment control mechanism. Evaluated on both national and regional household travel survey datasets, PEMANT consistently outperforms state-of-the-art benchmarks across datasets.