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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00382483" target="_blank" >RIV/68407700:21110/24:00382483 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.17815/CD.2024.177" target="_blank" >https://doi.org/10.17815/CD.2024.177</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.17815/CD.2024.177" target="_blank" >10.17815/CD.2024.177</a>

Result language
angličtina
Original language name
The Evaluation of Data Fitting Approaches for Speed/Flow Density Relationships
Original language description
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.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)