Work

Reconstructing flood-induced human mobility using LLM agent with disaster context adaptation

Year: 2026

Type: article

Abstract: Understanding human mobility during extreme flood events is critical for emergency evacuation and resilient transport planning, yet flood-induced behavioral heterogeneity remains difficult to characterize. Progress has been constrained by data scarcity and the inherent complexity of emergency decision-making, which often induces abnormal travel behaviors and anomalous mobility fluxes. We propose the Disaster-aware Trajectory Generator (DTG), integrating Large Language Model (LLM) agents into an agent-based framework to generate individual-level mobility trajectories under evolving urban flood conditions. Validated against 1.32 billion mobility records from the 2021 “7.20” Zhengzhou flood in China, DTG outperforms all traditional and LLM-enabled baselines, achieving a flood-induced inflow-shift prediction accuracy of 0.713 with outflow and OD distribution JSD of 0.069 and 0.047 during the disaster period. By combining accessibility-constrained disaster context reasoning with the representation of heterogeneous socio-demographic responses, DTG reveals critical infrastructure vulnerabilities and abnormal behavioral patterns, offering actionable insights for flood-resilient transport planning.

Source: Transportation Research Part D Transport and Environment

DOI: 10.1016/j.trd.2026.105685

Authors: Zijing Zhang, Fengjue Huang, Junqing Tang, Pengjun Zhao

Institutions: Peking University, Ministry of Natural Resources

Language: English

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,450