StationNet: An Algorithm for the Extraction and Visualization of Top-n Correlated Bike Stations in Bike Sharing Systems Big Datasets
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Bike sharing systems (BSS) have emerged as an alternative and environmentally friendlytransportation tool that provides short-term bike rental to city residents for their close proximitytransportation purposes or sports activities. With the emergence and widespread usage of BSS,BSS operators started collecting bike user-related datasets to benefit from it and to increaseservice quality. Many application areas are present which use BSS big datasets, such asbehavioral analyses, urban pattern discovery, and network analysis of bike stations. A bikestation network can be defined as a network where bike stations are nodes and the bike trips ofusers from a station to another station as edges. The extraction of bike station network providesinformation about which stations are central, which stations have more in- or out-flows, andwhich regions of the cities are actively used by bike users. However, the extraction of bikestation networks is challenging due to the complexity and different characteristics of bikestations, the requirement of new algorithms and new visualization techniques, and the issuesrelated to efficient handling BSS big datasets. In this study, the concept of bike station networkextraction in terms of top-n correlated stations is proposed. In particular, the extraction of a bikestation network from BSS big datasets are defined and a new algorithm is proposed forextraction of bike station network, and also a new visualization approach that uses commonvisualization tools is utilized to represent bike station network on a map which would providemore information than a network without a background information. The proposed algorithmand visualization technique are evaluated using one year BSS big dataset. Experimental resultsshow that the proposed algorithm could successfully extract top-n correlated bike stationnetworks and utilized visualization technique is beneficial.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Bilgisayar Bilimleri, Sibernitik, Bilgisayar Bilimleri, Bilgi Sistemleri, Bilgisayar Bilimleri, Donanım ve Mimari, Bilgisayar Bilimleri, Teori ve Metotlar, Bilgisayar Bilimleri, Yapay Zeka
Kaynak
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
25
Sayı
1
