Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920967" target="_blank" >RIV/00216275:25530/23:39920967 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-40725-3_50" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-40725-3_50</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-40725-3_50" target="_blank" >10.1007/978-3-031-40725-3_50</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data

Popis výsledku v původním jazyce

One of the techniques for the analysis of travel patterns on a public transport network is the clustering of the users movements, in order to identify movement patterns. This paper analyses and compares two different methodologies for public transport trajectory clustering: feature clustering and geospatial trajectory clustering. The results of clustering trip features, such as origin, destination, or distance, are compared against the clustering of travelled trajectories by their geospatial characteristics. Algorithms based on density and hierarchical clustering are compared for both methodologies. In geospatial clustering, different metrics to measure distances between trajectories are included in the comparison. Results are evaluated by analysing their quality through the silhouette coefficient and graphical representations of the clusters on the map. The results show that geospatial trajectory clustering offers better quality than feature trajectory clustering. Also, in the case of long and complete trajectories, density clustering using edit distance with real penalty distance outperforms other combinations.

Název v anglickém jazyce

Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data

Popis výsledku anglicky

One of the techniques for the analysis of travel patterns on a public transport network is the clustering of the users movements, in order to identify movement patterns. This paper analyses and compares two different methodologies for public transport trajectory clustering: feature clustering and geospatial trajectory clustering. The results of clustering trip features, such as origin, destination, or distance, are compared against the clustering of travelled trajectories by their geospatial characteristics. Algorithms based on density and hierarchical clustering are compared for both methodologies. In geospatial clustering, different metrics to measure distances between trajectories are included in the comparison. Results are evaluated by analysing their quality through the silhouette coefficient and graphical representations of the clusters on the map. The results show that geospatial trajectory clustering offers better quality than feature trajectory clustering. Also, in the case of long and complete trajectories, density clustering using edit distance with real penalty distance outperforms other combinations.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Hybrid Artificial Intelligent Systems : 18th International Conference, HAIS 2023, proceedings

ISBN

978-3-031-40724-6

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

589-599

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Salamanca

Datum konání akce

5. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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