FOMOsim: An open-source simulator for rigorous analysis of micromobility planning problems

Existing simulation models for micromobility systems often face significant limitations: they are typically custom-built for specific contexts, lack generalizability, are not open-source, and undergo limited testing, which again question their reliability and applicability in broader settings. To address these challenges, this paper presents FOMOsim, an open-source simulator developed to analyze micromobility systems. The simulator’s modular design allows for better scalability and usability, facilitating detailed and realistic simulations of shared transport operations. New benchmark instances, utilizing data from bike-sharing systems (BSSs) in major American and Norwegian cities, are created and embedded within the simulator. Additionally, we introduce a new greedy heuristic for the dynamic bike rebalancing problem, integrated within FOMOsim. Rigorous testing demonstrates that the proposed heuristic, when combined with FOMOsim, outperforms state-of-the-art methods, significantly improving BSS performance by reducing bike shortages and surpluses. Furthermore, a comprehensive experimental study is designed to assess the impact of key strategic and operational decisions on BSS performance. The findings underscore the simulator’s adaptability in addressing various planning challenges and its potential to improve BSS management through informed decision-making. Consequently, FOMOsim provides the research community with a robust platform for replicating experiments, generating new instances, and exploring innovative solutions in BSS management, thereby substantially enriching the field’s body of knowledge. • FOMOsim: Open-source simulator with benchmark instances for bike-sharing systems. • Validated across various BSSs, demonstrating scalability and versatility. • Developed and tested a new greedy heuristic for dynamic rebalancing problems. • Extensive experiments compare the heuristic with state-of-the-art methods. • Findings provide insights into the optimal design and management of BSS.