scholarly journals Exploring the Impact of Dockless Bikeshare on Docked Bikeshare—A Case Study in London

2020 ◽  
Vol 12 (15) ◽  
pp. 6110
Author(s):  
Dongdong Feng ◽  
Lin Cheng ◽  
Mingyang Du

As a green and sustainable travel mode, the bikeshare plays an important role in solving the “last-mile” problem. The new dockless bikeshare system (DBS) is widely favored by travelers, and the traditional docked bikeshare system (BS) is affected to a certain extent, but the specific circumstances of this impact are not yet known. To fill the knowledge gap, the objective of this study is to measure the impacts of DBS on London cycle hire, which is a type of BS. In this study, the travel data of 707 docking stations in two periods, i.e., March 2018 and March 2017, are included. A spatial-temporal analysis is first conducted to investigate the mobility pattern changes. A complex network analysis is then developed to explore the impact of DBS on network connectivity. The results suggest a significant decrease of 64% in the average trip amounts, with both origins and destinations in the affected area, and the trips with short and medium duration and short and medium distances are mainly replaced by DBS. DBS also has a considerable impact on the structure and properties of the mobility network. The connectivity and interaction strength between stations decrease after DBS appears. We also concluded that the observed changes are heterogeneously distributed in space, especially on weekends. The applied spatial-temporal analysis and complex network analysis provide a better understanding of the relationships between DBS and BS.

2017 ◽  
Vol 207 ◽  
pp. 477-493 ◽  
Author(s):  
Peipei Zhang ◽  
Mei Sun ◽  
Xiaoling Zhang ◽  
Cuixia Gao

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.


2020 ◽  
Vol 67 (6) ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhongke Gao ◽  
Hongtao Wang ◽  
Weidong Dang ◽  
Yongqiang Li ◽  
Xiaolin Hong ◽  
...  

Author(s):  
Emerson Luiz Chiesse da Silva ◽  
Marcelo De Oliveira Rosa ◽  
Keiko Veronica Ono Fonseca ◽  
Ricardo Luders ◽  
Nadia Puchaslki Kozievitch

2018 ◽  
Vol 55 ◽  
pp. 133-142 ◽  
Author(s):  
Wenyu Hou ◽  
Huifang Liu ◽  
Hui Wang ◽  
Fengyang Wu

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