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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

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

ISBN

978-3-031-40724-6

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

11

Pages from-to

589-599

Publisher name

Springer Nature Switzerland AG

Place of publication

Cham

Event location

Salamanca

Event date

Sep 5, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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