Using clustering algorithms to identify recreational trips within a bike-sharing system
Authors:
- Vitalii Naumov,
- Krystian Banet
Abstract
Bike-sharing systems became one of the most popular means of individual transport in many European cities. With the growth of popularity of bicycle-sharing systems, the estimation of demand for its services becomes a very significant task for transportation planners. The data from GPS tracking systems installed on bicycles can be used as a reliable source of information in this case. The paper presents an approach to identify the segment of recreational trips, implemented within the bike-sharing system, based on poplar clusterisation algorithms. The authors developed the subroutines for cleaning the raw data obtained from GPS-trackers. By using the purified data on the numeric parameters of trips in a bicycle sharing system, the clustering model identifies such a cluster that represent recreational trips. The k-means, mini-batch k-means, birch algorithms, and agglomerative clustering are examined in this study in order to allocate recreational trips. The use of the proposed approach is demonstrated on the example of data obtained from Wavelo bike-sharing system in the city of Krakow, Poland.
- Record ID
- CUT31f874b99c214c668148d616c11ae3fa
- Publication categories
- ; ;
- Author
- Pages
- 130-138
- Other elements of collation
- rys.; tab.; wykr.; Bibliografia (na s.) - 138; Bibliografia (liczba pozycji) - 12; Oznaczenie streszczenia - Abstr.
- Book
- Kabashkin Igor, Igor Kabashkin Yatskiv Irina, Irina Yatskiv Prentkovskis Olegas Olegas Prentkovskis (eds.): Reliability and Statistics in Transportation and Communication : selected papers from the 19th International Conference on Reliability and Statistics in Transportation and Communication, RelStat’19, 16-19 October 2019, Riga, Latvia, Lecture Notes in Networks and Systems, no. 117, 2020, Cham, Springer, Springer, ISBN 978-3-030-44610-9 (eBook)
- Keywords in English
- travel demand, bike-sharing system, recreational trips, data cleansing, clustering algorithms
- DOI
- DOI:10.1007/978-3-030-44610-9_14 Opening in a new tab
- URL
- https://link.springer.com/chapter/10.1007%2F978-3-030-44610-9_14 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 20
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT31f874b99c214c668148d616c11ae3fa/
- URN
urn:pkr-prod:CUT31f874b99c214c668148d616c11ae3fa
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.