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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:

Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning
Aliasghar Mehdizadeh Dastjerdi, Catherine Morency
Sensors (2022) Vol. 22, Iss. 3, pp. 1060-1060
Open Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities
Rusul Abduljabbar, Sohani Liyanage, Hussein Dia
Cities (2022) Vol. 127, pp. 103770-103770
Open Access | Times Cited: 31
Forecasting Bike Sharing Demand Using Quantum Bayesian Network
Ramkumar Harikrishnakumar, Saideep Nannapaneni
Expert Systems with Applications (2023) Vol. 221, pp. 119749-119749
Open Access | Times Cited: 16
A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations
Z D Wang, Dexin Yu, Xiaoyu Zheng, et al.
Sustainability (2025) Vol. 17, Iss. 3, pp. 1032-1032
Open Access
Comparison of Xgboost regression to predict the demand for bike rental system with decision tree regression
C. Punith, S. Magesh Kumar
AIP conference proceedings (2025) Vol. 3252, pp. 020155-020155
Closed Access
A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system
Ximing Chang, Jianjun Wu, Huijun Sun, et al.
Transportation Research Part C Emerging Technologies (2023) Vol. 153, pp. 104220-104220
Closed Access | Times Cited: 15
Spatiotemporal assessment of carbon emission reduction by shared bikes in Shenzhen, China
Geyu Lv, Sheng Zheng, H. L. Chen
Sustainable Cities and Society (2023) Vol. 100, pp. 105011-105011
Closed Access | Times Cited: 14
Exploring year-to-year spatiotemporal changes in cycling patterns for bike-sharing system in the pre-, during and post-pandemic periods
Xiaoying Shi, Junjie Zhao, Jiaming He, et al.
Sustainable Cities and Society (2023) Vol. 98, pp. 104814-104814
Closed Access | Times Cited: 11
Exploring Travel Mobility in Integrated Usage of Dockless Bike-Sharing and the Metro Based on Multisource Data
Hui Zhang, Yu Cui, Yanjun Liu, et al.
ISPRS International Journal of Geo-Information (2024) Vol. 13, Iss. 4, pp. 108-108
Open Access | Times Cited: 3
Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations
Yu Liang, Tao Feng, Tie Li, et al.
Urban Rail Transit (2022) Vol. 9, Iss. 1, pp. 57-71
Open Access | Times Cited: 17
A Short-Term Hybrid TCN-GRU Prediction Model of Bike-Sharing Demand Based on Travel Characteristics Mining
Shenghan Zhou, Chaofei Song, Tianhuai Wang, et al.
Entropy (2022) Vol. 24, Iss. 9, pp. 1193-1193
Open Access | Times Cited: 15
Exploring the spatiotemporal factors affecting bicycle-sharing demand during the COVID-19 pandemic
Sanjana Hossain, Patrick Loa, Felita Ong, et al.
Transportation (2023) Vol. 51, Iss. 5, pp. 1575-1610
Open Access | Times Cited: 8
Enhancing multistep-ahead bike-sharing demand prediction with a two-stage online learning-based time-series model: insight from Seoul
Subeen Leem, Jisong Oh, Jihoon Moon, et al.
The Journal of Supercomputing (2023) Vol. 80, Iss. 3, pp. 4049-4082
Closed Access | Times Cited: 7
Interpretable software estimation with graph neural networks and orthogonal array tunning method
Nevena Ranković, Dragica Ranković, Mirjana Ivanović, et al.
Information Processing & Management (2024) Vol. 61, Iss. 5, pp. 103778-103778
Open Access | Times Cited: 2
Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather
Yang Yang, Xin Shao, Yuting Zhu, et al.
Journal of Advanced Transportation (2023) Vol. 2023, pp. 1-13
Open Access | Times Cited: 5
Addressing COVID-induced changes in spatiotemporal travel mobility and community structure utilizing trip data: An innovative graph-based deep learning approach
Ximing Chang, Jianjun Wu, Jiarui Yu, et al.
Transportation Research Part A Policy and Practice (2024) Vol. 180, pp. 103973-103973
Closed Access | Times Cited: 1
Effects of COVID-19 on Residential Planning and Design: A Scientometric Analysis
Qingchang Chen, Zhuoyang Sun, Wenjing Li
Sustainability (2023) Vol. 15, Iss. 3, pp. 2823-2823
Open Access | Times Cited: 4
Analyzing Factors Affecting Micro-Mobility and Predicting Micro-Mobility Demand Using Ensemble Voting Regressor
Jiyoung Ko, Yung-Cheol Byun
Electronics (2023) Vol. 12, Iss. 21, pp. 4410-4410
Open Access | Times Cited: 4
Could free-floating bikeshare weed out station-based bikeshare? Analyzing the relationship between two bikeshare systems from bivariate flow clustering
Xize Liu, Wendong Chen, Xuewu Chen, et al.
Journal of Transport Geography (2024) Vol. 118, pp. 103941-103941
Closed Access | Times Cited: 1
A Demand-Centric Repositioning Strategy for Bike-Sharing Systems
Ying-Chih Lin
Sensors (2022) Vol. 22, Iss. 15, pp. 5580-5580
Open Access | Times Cited: 7
Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach
Heejong Lim, Kwanghun Chung, Sangbok Lee
Sustainability (2022) Vol. 14, Iss. 23, pp. 15889-15889
Open Access | Times Cited: 7
Prediction bike-sharing demand with gradient boosting methods
Zeliha Ergül Aydın, Banu İçmen Erdem, Zeynep İdil Erzurum Çiçek
Pamukkale University Journal of Engineering Sciences (2023) Vol. 29, Iss. 8, pp. 824-832
Open Access | Times Cited: 3
A Machine Learning approach for shared bicycle demand forecasting
Margarida Mergulhao, Myke Palma, Carlos J. Costa
2022 17th Iberian Conference on Information Systems and Technologies (CISTI) (2022), pp. 1-6
Closed Access | Times Cited: 4
Investigation on changes in the usage patterns of Seoul Bike usage patterns owing to COVID-19 according to pass type
Juhyeon Jung, Kyoungok Kim
Heliyon (2023) Vol. 9, Iss. 5, pp. e16077-e16077
Open Access | Times Cited: 2
Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities
Malliga Subramanian, Jaehyuk Cho, V E Sathishkumar, et al.
Sustainability (2023) Vol. 15, Iss. 18, pp. 13840-13840
Open Access | Times Cited: 2
Peak Hour Demand Prediction for Sharing Bikes: A Comparative Analysis of Performances of Machine Learning Models
Somay Verma, Lekha Rani, Pradeepta Kumar Sarangi, et al.
Lecture notes in networks and systems (2024), pp. 47-64
Closed Access
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