Qiumeng Li

Qiumeng Li, Xinxi Yang, Suhong Zhou

As emerging mobility modes continue to expand, many cities face declining bus ridership, increasing fiscal pressure to sustain underutilized routes, and growing inefficiencies in resource allocation. This study employs an agent-based modelling (ABM) approach calibrated through a large language model (LLM) using few-shot learning to examine how progressive bus route cutbacks affect passenger dissatisfaction across demographic groups and overall network resilience. Using IC-card data from Beijing's Huairou District, the LLM-calibrated ABM estimated passenger sensitivity parameters related to travel time, waiting, transfers, and crowding. Results show that the structural configuration of the bus network exerts a stronger influence on system stability than capacity or operational factors. The elimination of high-connectivity routes led to an exponential rise in total dissatisfaction, particularly among passengers with disabilities and older adults. The evolution of dissatisfaction exhibited three distinct phases - stable, transitional, and critical. Through the analysis of each stage, this study found that the continuous bus route reduction scenario exhibits three-stage thresholds. Once these thresholds are crossed, even a small reduction in routes may lead to a significant loss of passenger flow. Research highlights the nonlinear response of user sentiment to service reductions and underscore the importance of maintaining structural critical routes and providing stable services to vulnerable groups for equitable and resilient transport planning.

Chunhou Ji, Qiumeng Li

GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (capturing temporal sequences and movement dynamics). Separate embeddings are generated for each modality and then concatenated to produce trajectory scene embeddings with rich semantic content which are further paired with a simple MLP classifier. We validate the proposed framework on Travel Mode Identification (TMI), a critical task for analyzing travel choices and understanding mobility behavior. Our experiments show that these embeddings achieve significant performance improvement, highlighting the advantage of our LLM-driven method in capturing deep spatio-temporal dependencies and reducing reliance on handcrafted features. This semantic enhancement promises significant potential for diverse downstream applications and future research in geospatial artificial intelligence. The source code and dataset are publicly available at: https://github.com/februarysea/TrajSceneLLM.

Qiumeng Li, Chunhou Ji, Xinyue Liu

Understanding and replicating human mobility requires not only spatial-temporal accuracy but also an awareness of the cognitive hierarchy underlying real-world travel decisions. Traditional agent-based or deep learning models can reproduce statistical patterns of movement but fail to capture the semantic coherence and causal logic of human behavior. Large language models (LLMs) show potential, but struggle to balance creative reasoning with strict structural compliance. This study proposes a Hierarchical LLM-Agent Framework, termed Narrative-to-Action, that integrates high-level narrative reasoning, mid-level reflective planning, and low-level behavioral execution within a unified cognitive hierarchy. At the macro level, one agent is employed as a "creative writer" to produce diary-style narratives rich in motivation and context, then uses another agent as a "structural parser" to convert narratives into machine-readable plans. A dynamic execution module further grounds agents in geographic environments and enables adaptive behavioral adjustments guided by a novel occupation-aware metric, Mobility Entropy by Occupation (MEO), which captures heterogeneous schedule flexibility across different occupational personalities. At the micro level, the agent executes concrete actions-selecting locations, transportation modes, and time intervals-through interaction with an environmental simulation. By embedding this multi-layer cognitive process, the framework produces not only synthetic trajectories that align closely with real-world patterns but also interpretable representations of human decision logic. This research advances synthetic mobility generation from a data-driven paradigm to a cognition-driven simulation, providing a scalable pathway for understanding, predicting, and synthesizing complex urban mobility behaviors through hierarchical LLM agents.