Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

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: 380

Showing 1-25 of 380 citing articles:

Graph neural network for traffic forecasting: A survey
Weiwei Jiang, Jiayun Luo
Expert Systems with Applications (2022) Vol. 207, pp. 117921-117921
Open Access | Times Cited: 741
Deep Learning for Spatio-Temporal Data Mining: A Survey
Senzhang Wang, Jiannong Cao, Philip S. Yu
IEEE Transactions on Knowledge and Data Engineering (2020) Vol. 34, Iss. 8, pp. 3681-3700
Open Access | Times Cited: 542
Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C.
Grant McKenzie
Journal of Transport Geography (2019) Vol. 78, pp. 19-28
Open Access | Times Cited: 383
A Multi-Stream Feature Fusion Approach for Traffic Prediction
Zhishuai Li, Gang Xiong, Yonglin Tian, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 23, Iss. 2, pp. 1456-1466
Open Access | Times Cited: 312
A data-driven lane-changing model based on deep learning
Dongfan Xie, Zhe-Zhe Fang, Bin Jia, et al.
Transportation Research Part C Emerging Technologies (2019) Vol. 106, pp. 41-60
Closed Access | Times Cited: 244
An effective spatial-temporal attention based neural network for traffic flow prediction
Loan N.N., Hai L. Vu, Vo Nguyen Quoc Bao, et al.
Transportation Research Part C Emerging Technologies (2019) Vol. 108, pp. 12-28
Closed Access | Times Cited: 237
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
Jiexia Ye, Juanjuan Zhao, Kejiang Ye, et al.
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 23, Iss. 5, pp. 3904-3924
Open Access | Times Cited: 210
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
Lei Lin, Weizi Li, Huikun Bi, et al.
IEEE Intelligent Transportation Systems Magazine (2021) Vol. 14, Iss. 2, pp. 197-208
Closed Access | Times Cited: 171
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
Jintao Ke, Xiaoran Qin, Hai Yang, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 122, pp. 102858-102858
Open Access | Times Cited: 155
Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network
Jinjun Tang, Jian Liang, Fang Liu, et al.
Transportation Research Part C Emerging Technologies (2021) Vol. 124, pp. 102951-102951
Closed Access | Times Cited: 103
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
Yang Liu, Fanyou Wu, Cheng Lyu, et al.
Transportation Research Part E Logistics and Transportation Review (2022) Vol. 161, pp. 102694-102694
Closed Access | Times Cited: 90
A dynamical spatial-temporal graph neural network for traffic demand prediction
Feihu Huang, Peiyu Yi, Jince Wang, et al.
Information Sciences (2022) Vol. 594, pp. 286-304
Closed Access | Times Cited: 77
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 8, pp. 8846-8885
Open Access | Times Cited: 77
A Hybrid Visualization Model for Knowledge Mapping: Scientometrics, SAOM, and SAO
Guangnian Xiao, Liu Chen, Xinqiang Chen, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 3, pp. 2208-2221
Closed Access | Times Cited: 51
Human-centered GeoAI foundation models: where GeoAI meets human dynamics
Xinyue Ye, Jiaxin Du, Xinyu Li, et al.
Urban Informatics (2025) Vol. 4, Iss. 1
Open Access | Times Cited: 2
Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach
Shuyun Ren, Tsan‐Ming Choi, Ka-Man Lee, et al.
Transportation Research Part E Logistics and Transportation Review (2020) Vol. 134, pp. 101834-101834
Closed Access | Times Cited: 130
Using data mining techniques for bike sharing demand prediction in metropolitan city
Sathishkumar Veerappampalayam Easwaramoorthy, Jang‐Woo Park, Yongyun Cho
Computer Communications (2020) Vol. 153, pp. 353-366
Closed Access | Times Cited: 127
Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City
Kailai Wang, Gulsah Akar
Journal of Transport Geography (2019) Vol. 76, pp. 1-9
Closed Access | Times Cited: 126
Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network
Guangyin Jin, Yan Cui, Liang Zeng, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 117, pp. 102665-102665
Closed Access | Times Cited: 108
Using machine learning for direct demand modeling of ridesourcing services in Chicago
Xiang Yan, Xinyu Liu, Xilei Zhao
Journal of Transport Geography (2020) Vol. 83, pp. 102661-102661
Closed Access | Times Cited: 103
Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction
Zhiyong Cui, Ruimin Ke, Ziyuan Pu, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 115, pp. 102620-102620
Closed Access | Times Cited: 103
Applications of Deep Learning in Intelligent Transportation Systems
Arya Haghighat, Varsha Ravichandra-Mouli, Pranamesh Chakraborty, et al.
Journal of Big Data Analytics in Transportation (2020) Vol. 2, Iss. 2, pp. 115-145
Closed Access | Times Cited: 101
Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks
Yong Han, Shukang Wang, Yibin Ren, et al.
ISPRS International Journal of Geo-Information (2019) Vol. 8, Iss. 6, pp. 243-243
Open Access | Times Cited: 100
DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting
Kyung‐Eun Lee, Wonjong Rhee
Transportation Research Part C Emerging Technologies (2021) Vol. 134, pp. 103466-103466
Open Access | Times Cited: 99
Evaluation and prediction of transportation resilience under extreme weather events: A diffusion graph convolutional approach
Hongwei Wang, Zhong‐Ren Peng, Dongsheng Wang, et al.
Transportation Research Part C Emerging Technologies (2020) Vol. 115, pp. 102619-102619
Closed Access | Times Cited: 99
Page 1 - Next Page

Scroll to top