The Evaluation of Data Fitting Approaches for Speed/Flow Density Relationships




Data fitting, Speed/Flow Density Relationship, Pedestrian Dynamics, Traffic Dynamics


This paper presents guidance on data-fitting approaches in the context of pedestrian and evacuation dynamics research. In particular, it examines parametric and non-parametric regression techniques for analysing speed/flow density relationships. Parametric models assume predefined functional forms, while non-parametric models provide flexibility to capture complex relationships. This paper evaluates a range of traditional statistical approaches and machine-learning techniques. It emphasises the importance of weighting unbalanced datasets to enhance model accuracy. Practical applications are illustrated using traffic and pedestrian evacuation data.

This paper is intended to stimulate discussion on best practices for developing, calibrating, and testing macroscopic and microscopic evacuation models. It does not prescribe a one-size-fits-all solution for evacuation data fitting approaches, but it provides an overview of existing methods and analyses their advantages and limitations.


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How to Cite

Rohaert, A., Wahlqvist, J., Najmanová, H., Bode, N., & Ronchi, E. (2024). The Evaluation of Data Fitting Approaches for Speed/Flow Density Relationships. Collective Dynamics, 9, 1–9.



Special Issue of Pedestrian and Evacuation Dynamics 2023