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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20101 - Civil engineering

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Collective Dynamics

ISSN

2366-8539

e-ISSN

2366-8539

Svazek periodika

9

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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