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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Dynamic cluster-based over-demand prediction in bike sharing systems
Longbiao Chen, Daqing Zhang, Leye Wang, et al.
(2016)
Open Access | Times Cited: 172

Showing 1-25 of 172 citing articles:

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
Lei Lin, Zhengbing He, Srinivas Peeta
Transportation Research Part C Emerging Technologies (2018) Vol. 97, pp. 258-276
Open Access | Times Cited: 385
Bike flow prediction with multi-graph convolutional networks
Di Chai, Leye Wang, Qiang Yang
(2018)
Open Access | Times Cited: 273
Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks
Xian Zhou, Yanyan Shen, Yanmin Zhu, et al.
(2018), pp. 736-744
Closed Access | Times Cited: 152
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
Yuanxuan Yang, Alison Heppenstall, Andy Turner, et al.
Computers Environment and Urban Systems (2019) Vol. 77, pp. 101361-101361
Open Access | Times Cited: 141
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Ling Pan, Qingpeng Cai, Zhixuan Fang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2019) Vol. 33, Iss. 01, pp. 1393-1400
Open Access | Times Cited: 118
MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting
Chao Huang, Chuxu Zhang, Jiashu Zhao, et al.
(2019)
Closed Access | Times Cited: 93
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
Chuanpan Zheng, Xiaoliang Fan, Chenglu Wen, et al.
IEEE Transactions on Intelligent Transportation Systems (2019) Vol. 21, Iss. 9, pp. 3744-3755
Closed Access | Times Cited: 93
Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems
Pierre Hulot, Daniel Aloise, Sanjay Dominik Jena
(2018)
Open Access | Times Cited: 84
Citywide Bike Usage Prediction in a Bike-Sharing System
Yexin Li, Yu Zheng
IEEE Transactions on Knowledge and Data Engineering (2019) Vol. 32, Iss. 6, pp. 1079-1091
Closed Access | Times Cited: 83
Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems
Yuanxuan Yang, Alison Heppenstall, Andy Turner, et al.
Computers Environment and Urban Systems (2020) Vol. 83, pp. 101521-101521
Open Access | Times Cited: 82
Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks
Guangnian Xiao, Ruinan Wang, Chunqin Zhang, et al.
Multimedia Tools and Applications (2020) Vol. 80, Iss. 15, pp. 22907-22925
Closed Access | Times Cited: 79
Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields
Jingyuan Wang, Jiahao Ji, Zhe Jiang, et al.
IEEE Transactions on Knowledge and Data Engineering (2022) Vol. 35, Iss. 9, pp. 9073-9087
Closed Access | Times Cited: 39
Multi-task supply-demand prediction and reliability analysis for docked bike-sharing systems via transformer-encoder-based neural processes
Meng Xu, Yining Di, Hai Yang, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 147, pp. 104015-104015
Closed Access | Times Cited: 26
Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization
Longbiao Chen, Dingqi Yang, Daqing Zhang, et al.
Journal of Network and Computer Applications (2018) Vol. 121, pp. 59-69
Open Access | Times Cited: 82
Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction
Youru Li, Zhenfeng Zhu, Deqiang Kong, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2019) Vol. 33, Iss. 01, pp. 1004-1011
Open Access | Times Cited: 72
A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things
Tiantian Xu, Guangjie Han, Xingyue Qi, et al.
IEEE Internet of Things Journal (2020) Vol. 7, Iss. 8, pp. 7345-7356
Closed Access | Times Cited: 61
Spatial-Temporal Convolutional Graph Attention Networks for Citywide Traffic Flow Forecasting
Xiyue Zhang, Chao Huang, Yong Xu, et al.
(2020)
Closed Access | Times Cited: 60
Spatiotemporal Demand Prediction Model for E-Scooter Sharing Services with Latent Feature and Deep Learning
Seung Woo Ham, Jung-Hoon Cho, Sangwoo Park, et al.
Transportation Research Record Journal of the Transportation Research Board (2021) Vol. 2675, Iss. 11, pp. 34-43
Open Access | Times Cited: 49
Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review
Vitória Albuquerque, Miguel Sales Dias, Fernando Bação
ISPRS International Journal of Geo-Information (2021) Vol. 10, Iss. 2, pp. 62-62
Open Access | Times Cited: 42
Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach
Xinwei Ma, Yurui Yin, Yuchuan Jin, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1161-1161
Open Access | Times Cited: 31
Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Tae San Kim, Won Kyung Lee, So Young Sohn
PLoS ONE (2019) Vol. 14, Iss. 9, pp. e0220782-e0220782
Open Access | Times Cited: 43
Analysis of spatiotemporal mobility of shared‐bike usage during COVID‐19 pandemic in Beijing
Xinwei Chai, Guo Xian, Jihua Xiao, et al.
Transactions in GIS (2021) Vol. 25, Iss. 6, pp. 2866-2887
Open Access | Times Cited: 38
An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information
Fatima Abbas, Jawad Ali, Wali Khan Mashwani, et al.
PLoS ONE (2024) Vol. 19, Iss. 5, pp. e0297462-e0297462
Open Access | Times Cited: 5
A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction
Guanyao Li, Xiaofeng Wang, Gunarto Sindoro Njoo, et al.
2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022), pp. 713-726
Open Access | Times Cited: 19
Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems
Aleix Bassolas, Jordi Grau-Escolano, Julián Vicens
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
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