ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework

TL;DR

This work constructs the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics, and proposes ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive.

Abstract

Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large-scale societal events. This limitation stems from two critical gaps: (1) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users' habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive. Extensive experiments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at https://github.com/deepkashiwa20/ELLMob.

Figures (17)

• Figure 1: Event-driven mobility generation by LLMs, which incorporates event context to capture real-world human mobility on three different events: (1) Typhoon: evacuation from seaside, (2) COVID-19 Pandemic: self-restraint, and (3) Olympics: restricted zones and traffic jam.
• Figure 2: Normalized radar charts of four mobility metrics on pre-event and during-event patterns.
• Figure 3: The ELLMob framework architecture comprising three interconnected modules: Trajectory Generation, Gist Extraction, and Reflection-based alignment.
• Figure 4: (a) Comparison of generated activity proportions (relative to the total number of activities) during the COVID-19 Pandemic. Each chart contrasts the ground truth (G.T.), distribution with: Without internal alignment (w/o I.A.), without external alignment (w/o E.A.), and the complete model (Comp.). (b) Comparison of three key activity categories distributions generated by ELLMob with various LLM-based baselines. (c) Performance comparison on the active user prediction task.
• Figure 5: A case study of ELLMob's workflow on the mobility of User No.003.