Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles

TL;DR

This paper introduces MobAgent, an LLM-based framework that generates realistic, personalized travel diaries by understanding individual mobility patterns and contextual influences, validated on large survey data.

Contribution

The study presents a novel LLM agent framework that combines pattern extraction and recursive reasoning to produce authentic, personalized mobility trajectories at urban scale.

Findings

Effective generation of personalized travel diaries

High accuracy validated on 0.2 million survey data

Enhanced understanding of mobility behavioral factors

Abstract

Human mobility is inextricably linked to social issues such as traffic congestion, energy consumption, and public health; however, privacy concerns restrict access to mobility data. Recently, research have utilized Large Language Models (LLMs) for human mobility generation, in which the challenge is how LLMs can understand individuals' mobility behavioral differences to generate realistic trajectories conforming to real world contexts. This study handles this problem by presenting an LLM agent-based framework (MobAgent) composing two phases: understanding-based mobility pattern extraction and reasoning-based trajectory generation, which enables generate more real travel diaries at urban scale, considering different individual profiles. MobAgent extracts reasons behind specific mobility trendiness and attribute influences to provide reliable patterns; infers the relationships between contextual factors and underlying motivations of mobility; and based on the patterns and the recursive reasoning process, MobAgent finally generates more authentic and personalized mobilities that reflect both individual differences and real-world constraints. We validate our framework with 0.2 million travel survey data, demonstrating its effectiveness in producing personalized and accurate travel diaries. This study highlights the capacity of LLMs to provide detailed and sophisticated understanding of human mobility through the real-world mobility data.