Kai-Fung Chu

555 total citations
29 papers, 383 citations indexed

About

Kai-Fung Chu is a scholar working on Electrical and Electronic Engineering, Automotive Engineering and Control and Systems Engineering. According to data from OpenAlex, Kai-Fung Chu has authored 29 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Electrical and Electronic Engineering, 11 papers in Automotive Engineering and 9 papers in Control and Systems Engineering. Recurrent topics in Kai-Fung Chu's work include Transportation and Mobility Innovations (9 papers), Vehicular Ad Hoc Networks (VANETs) (8 papers) and Transportation Planning and Optimization (6 papers). Kai-Fung Chu is often cited by papers focused on Transportation and Mobility Innovations (9 papers), Vehicular Ad Hoc Networks (VANETs) (8 papers) and Transportation Planning and Optimization (6 papers). Kai-Fung Chu collaborates with scholars based in Hong Kong, United Kingdom and China. Kai-Fung Chu's co-authors include Albert Y. S. Lam, Victor O. K. Li, Weisi Guo, Elmer R. Magsino, Ivan Wang‐Hei Ho, Chi-Kin Chau, Chenchen Fan, Becky P.Y. Loo, Chen Tian-Lun and James Lam and has published in prestigious journals such as IEEE Access, IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Cybernetics.

In The Last Decade

Kai-Fung Chu

25 papers receiving 375 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kai-Fung Chu Hong Kong 10 169 161 131 120 118 29 383
Yan Jiao China 6 92 0.5× 139 0.9× 267 2.0× 64 0.5× 80 0.7× 10 366
Marcin Seredynski Luxembourg 15 88 0.5× 164 1.0× 190 1.5× 181 1.5× 198 1.7× 51 478
Iulian Sandu Popa France 8 143 0.8× 132 0.8× 49 0.4× 117 1.0× 109 0.9× 15 415
Manish Chaturvedi India 8 134 0.8× 75 0.5× 56 0.4× 61 0.5× 119 1.0× 34 309
Fanyou Wu United States 8 95 0.6× 108 0.7× 140 1.1× 75 0.6× 47 0.4× 11 335
Roberto Sadao Yokoyama Brazil 10 206 1.2× 90 0.6× 65 0.5× 129 1.1× 222 1.9× 19 396
Eugene Vinitsky United States 10 166 1.0× 133 0.8× 328 2.5× 428 3.6× 58 0.5× 23 563
Jennifer McManis Ireland 8 101 0.6× 86 0.5× 42 0.3× 84 0.7× 98 0.8× 23 261
Djamel Khadraoui United States 11 62 0.4× 123 0.8× 119 0.9× 110 0.9× 91 0.8× 32 323
Javier Barrachina Spain 8 81 0.5× 57 0.4× 82 0.6× 97 0.8× 214 1.8× 11 343

Countries citing papers authored by Kai-Fung Chu

Since Specialization
Citations

This map shows the geographic impact of Kai-Fung Chu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kai-Fung Chu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai-Fung Chu more than expected).

Fields of papers citing papers by Kai-Fung Chu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai-Fung Chu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kai-Fung Chu. The network helps show where Kai-Fung Chu may publish in the future.

Co-authorship network of co-authors of Kai-Fung Chu

This figure shows the co-authorship network connecting the top 25 collaborators of Kai-Fung Chu. A scholar is included among the top collaborators of Kai-Fung Chu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kai-Fung Chu. Kai-Fung Chu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ye, Fan, Arsen Abdulali, Kai-Fung Chu, Xiao–Ping Zhang, & Fumiya Iida. (2025). Reservoir controllers design though robot-reservoir timescale alignment. Communications Engineering. 4(1). 81–81.
2.
Xie, Yue, Kai-Fung Chu, Albert Y. S. Lam, & Fumiya Iida. (2025). A multi-objective evolutionary algorithm with constraint-compliant initialization for energy transport and urban logistics in Electric Vehicle Routing. Applied Soft Computing. 183. 113624–113624.
3.
Chu, Kai-Fung, Fan Ye, Arsen Abdulali, & Fumiya Iida. (2025). Enhancing traffic dynamics-induced machine learning through heterogeneous driving policies. 2(1). 1 indexed citations
4.
Chu, Kai-Fung, et al.. (2025). Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks: A Multiobjective Approach. IEEE Internet of Things Journal. 12(11). 17753–17764. 1 indexed citations
5.
6.
Chu, Kai-Fung, et al.. (2024). A Survey of Artificial Intelligence-Related Cybersecurity Risks and Countermeasures in Mobility-as-a-Service. IEEE Intelligent Transportation Systems Magazine. 16(6). 37–55. 5 indexed citations
7.
Guo, Weisi, et al.. (2024). Rewiring Complex Networks to Achieve Cluster Synchronization Using Graph Convolution Networks With Reinforcement Learning. IEEE Transactions on Network Science and Engineering. 11(5). 4293–4304. 4 indexed citations
8.
Al–Rubaye, Saba, et al.. (2024). Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks. IEEE Transactions on Intelligent Transportation Systems. 26(10). 17304–17320. 5 indexed citations
9.
Fan, Chenchen, Kai-Fung Chu, Xiaomei Wang, Ka‐Wai Kwok, & Fumiya Iida. (2024). State transition learning with limited data for safe control of switched nonlinear systems. Neural Networks. 180. 106695–106695.
10.
Chu, Kai-Fung, Fan Ye, & Fumiya Iida. (2024). Embodied Intelligence in Transportation Systems: Road Network Computing. IOP Conference Series Materials Science and Engineering. 1321(1). 12010–12010. 1 indexed citations
11.
Chu, Kai-Fung & Weisi Guo. (2024). Multi-Agent Reinforcement Learning-Based Passenger Spoofing Attack on Mobility-as-a-Service. IEEE Transactions on Dependable and Secure Computing. 21(6). 5565–5581. 5 indexed citations
12.
Chu, Kai-Fung & Weisi Guo. (2023). Federated Reinforcement Learning for Consumers Privacy Protection in Mobility-as-a-Service. CERES (Cranfield University). 3 indexed citations
13.
Chu, Kai-Fung & Weisi Guo. (2023). Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness. Neural Computing and Applications. 35(27). 20221–20240. 12 indexed citations
14.
Chu, Kai-Fung & Weisi Guo. (2023). Passenger Spoofing Attack for Artificial Intelligence-based Mobility-as-a-Service. CERES (Cranfield University). 4874–4880. 4 indexed citations
15.
Fan, Chenchen, et al.. (2023). Output Reachable Set-Based Leader-Following Consensus of Positive Agents Over Switching Networks. IEEE Transactions on Cybernetics. 54(7). 3918–3930. 8 indexed citations
16.
Chu, Kai-Fung & Weisi Guo. (2023). Privacy-Preserving Federated Deep Reinforcement Learning for Mobility-as-a-Service. IEEE Transactions on Intelligent Transportation Systems. 25(2). 1882–1896. 12 indexed citations
17.
Chu, Kai-Fung, et al.. (2023). Deep Encoder Cross Network for Estimated Time of Arrival. IEEE Access. 11. 76095–76107. 5 indexed citations
18.
Chu, Kai-Fung, Chen Tian-Lun, Albert Y. S. Lam, & Yue Song. (2023). Collaborative Routing and Charging/Discharging Scheduling of Electric Autonomous Vehicles in Coupled Power-Traffic Networks. 1–6. 2 indexed citations
19.
Chu, Kai-Fung, Albert Y. S. Lam, Chenchen Fan, & Victor O. K. Li. (2020). Disturbance-Aware Neuro-Optimal System Control Using Generative Adversarial Control Networks. IEEE Transactions on Neural Networks and Learning Systems. 32(10). 4565–4576. 14 indexed citations
20.
Chu, Kai-Fung, Albert Y. S. Lam, Becky P.Y. Loo, & Victor O. K. Li. (2019). Public Transport Waiting Time Estimation Using Semi-Supervised Graph Convolutional Networks. 9. 2259–2264. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026