Exploring Dissatisfaction in Bus Route Reduction through LLM-Calibrated Agent-Based Modeling

TL;DR

This study uses a novel LLM-calibrated agent-based model to analyze how bus route reductions impact passenger dissatisfaction and network resilience, revealing nonlinear effects and critical thresholds.

Contribution

It introduces a LLM-calibrated ABM approach to assess the effects of bus route cutbacks on passenger dissatisfaction and system stability.

Findings

Elimination of high-connectivity routes causes exponential dissatisfaction increase.

Passenger dissatisfaction shows three phases: stable, transitional, critical.

Crossing certain thresholds leads to significant passenger flow loss.

Abstract

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.