A data-driven agent-based simulation of the public bicycle-sharing system in Sejong city

In this study, we propose a data-driven agent-based model for simulating Eoulling, the public bicycle-sharing systems (PBSSs) in Sejong City, the administrative capital of South Korea. Most existing models for PBSSs based on top-down approaches have limitations in reflecting Eoulling users' behavioral characteristics and analyzing their convenience. Unlike these, the proposed model is based on a bottom-up approach of agent-based simulation. We model each user as an agent to capture their bicycle rental and return behaviors, and analyze user convenience through interactions with bicycle station agent models. To improve model fidelity, multiple parameters for determining agent behaviors are extracted from the actual operations data of Eoulling, along with the population and geographic information of Sejong City. The validation results showed that the proposed model accurately describes the behavioral characteristics. We provide a workable solution addressing multiple concerns with Eoulling by evaluating its utilization and user convenience in virtual scenarios via model simulations.