FM
F.A.J.C. Muis
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This research investigated how parametric modelling can support early-stage airport landside planning by enabling the systematic generation and evaluation of adaptable terminal configurations. Traditional landside design relies on static forecasts and rules of thumb, which provide baseline estimates but limit exploration of alternative layouts and integration of public transport. To address these limitations, a parametric model framework was developed, translating demand, spatial, and performance parameters into a structured, data-driven approach. The model uses Excel inputs for passenger demand, modal splits, and basic geometry, generating landside layouts in Grasshopper through optimisation and scenario evaluation. Passenger experience is represented by walking distance, providing a quantifiable performance metric, while multiple scenarios illustrate how layouts respond to changes in demand and design constraints. Results demonstrate that the parametric model enables rapid iteration, visualisation, and comparison of alternative designs, improving decision-making transparency and adaptability. While simplifications such as 2D representation, partial passenger experience metrics, and solver stochasticity limit full applicability, the framework provides a foundation for more data-driven, flexible, and collaborative landside planning. Recommendations for future research include 3D modelling, enhanced passenger experience metrics, and alternative optimisation methods.
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This research investigated how parametric modelling can support early-stage airport landside planning by enabling the systematic generation and evaluation of adaptable terminal configurations. Traditional landside design relies on static forecasts and rules of thumb, which provide baseline estimates but limit exploration of alternative layouts and integration of public transport. To address these limitations, a parametric model framework was developed, translating demand, spatial, and performance parameters into a structured, data-driven approach. The model uses Excel inputs for passenger demand, modal splits, and basic geometry, generating landside layouts in Grasshopper through optimisation and scenario evaluation. Passenger experience is represented by walking distance, providing a quantifiable performance metric, while multiple scenarios illustrate how layouts respond to changes in demand and design constraints. Results demonstrate that the parametric model enables rapid iteration, visualisation, and comparison of alternative designs, improving decision-making transparency and adaptability. While simplifications such as 2D representation, partial passenger experience metrics, and solver stochasticity limit full applicability, the framework provides a foundation for more data-driven, flexible, and collaborative landside planning. Recommendations for future research include 3D modelling, enhanced passenger experience metrics, and alternative optimisation methods.