A spatiotemporal dynamic analyses approach for dockless bike-share system-Reference-Cited by-同舟云学术

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A spatiotemporal dynamic analyses approach for dockless bike-share system

Published:2021-01 Issue: Volume:85 Page:101566
ISSN:0198-9715
Container-title:Computers, Environment and Urban Systems
language:en
Short-container-title:Computers, Environment and Urban Systems

Author:

Song Jie,Zhang Liye,Qin Zheng,Ramli Muhamad Azfar

Funder

National Research Foundation

Urban Mobility Grand Challenge Programme

Publisher

Elsevier BV

Subject

Urban Studies,General Environmental Science,Ecological Modelling,Geography, Planning and Development

Reference38 articles.

1. Evaluating clustering methods within the artificial ecosystem algorithm and their application to bike redistribution in london;Adham;Biosystems,2016

2. The structure of spatial networks and communities in bicycle sharing systems;Austwick;PLoS One,2013

3. Studying commuting behaviours using collaborative visual analytics;Beecham;Computers, Environment and Urban Systems,2014

4. Fast unfolding of communities in large networks;Blondel;Journal of Statistical Mechanics: Theory and Experiment,2008

5. Shared bicycles in a city: A signal processing and data analysis perspective;Borgnat,2011

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding multimodal travel mobilities of dockless bike-sharing and metro: A multilayer network analysis;Physica A: Statistical Mechanics and its Applications;2024-08

2. Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation;Socio-Economic Planning Sciences;2024-08

3. Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks;Cities;2024-07

4. Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clustering;Journal of Transport Geography;2024-06

5. FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction;PLOS ONE;2024-03-07

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