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English

Agent-based modelling of commuting patterns in Geneva, Switzerland, towards portraying urban expansion and land cover changes

First online date2023-10-19
Abstract

Agent-based modelling has been used in many studies of urban expansion, land use and land cover change patterns. Indeed, this method represents a powerful tool for depicting and formulating predictions about the evolution of interconnected complex systems. We present an agent-based model developed in GAMA for analysing commuting patterns in the canton of Geneva, Switzerland, and fully document our modelling methodology, to provide inspiration and a materials and methodology basis for future modelling efforts. All datasets and codes related to this work are distributed in open access, and a general methodology for adapting the model to other use cases is discussed. Main findings revolve around the analysis of the impact of public transportation offer changes on journey by tram length. With our given set of initial conditions, when decreasing tram offer on the major Cornavin-Meyrin-CERN axis, journey-to-work length skyrockets for a handful of commuters. This phenomenon is correlated with an observed increase in overcrowded trams in the system. Furthermore, increasing tram offer alongside the emergence of a new large eco-district helps dampen the steady increase in journey length caused by the influx of new commuters and the saturation of the network. The model presented in this paper is early work in developing agent-based models for simulating urban expansion and land cover change dynamics.

Keywords
  • Agent-based modelling
  • Urban expansion
  • Land cover change
  • Land use
  • Commuting patterns
Funding
  • French National Research Agency (ANR) - Semantic Environmental Trajectories of Territories [ANR-21-CE23-0034]
Citation (ISO format)
CHAMBERS, Flann Sheridan, CRUZ, Christophe, DI MARZO SERUGENDO, Giovanna. Agent-based modelling of commuting patterns in Geneva, Switzerland, towards portraying urban expansion and land cover changes. 2023. doi: 10.21203/rs.3.rs-3452421/v1
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Additional URL for this publicationhttps://www.researchsquare.com/article/rs-3452421/v1
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Technical informations

Creation13/11/2025 16:21:41
First validation14/11/2025 07:22:24
Update08/01/2026 13:52:29
Status update08/01/2026 13:52:29
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