Kan Guo

1.2k citations
20 papers · 858 · 1 hit paper · h-index 9

Impact in

Papers in

Kan Guo

18 papers receiving 832 citations

Kan Guo's Hit Papers

Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction 2020 · 268 citations
2680+2+4Years since publication50100150200250

Peers

Kan Guo
Comparison fields: 5 of 84
  • Transportation 355
  • Building and Construction 495
  • Computer Graphics and Computer-Aided Design 90
  • Signal Processing 120
  • Control and Systems Engineering 241
Replace Kamal C. Sarma with:
Kamal C. Sarma United States
Hang Zhao China
Yinguo Li China
Wei Shangguan China
Roland Gerærts Netherlands
Shiru Qu China
Chih‐Wei Yi Taiwan
Kan Chen Singapore
Sławomir Bąk United States
Joongheon Kim South Korea
Kan Guo relative to Kamal C. Sarma United States Kamal C. Sarma's profile →
Citations per field
00.5×10.5×
Kamal C. Sarma · 1×
Citations per year

Countries citing papers authored by Kan Guo

Since Specialization
Citations

This map shows the geographic impact of Kan Guo'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 Kan Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kan Guo more than expected).

Fields of papers citing papers by Kan Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kan Guo. 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 Kan Guo. The network helps show where Kan Guo may publish in the future.

Co-authors

The 25 scholars most cited alongside Kan Guo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kan Guo Line = papers co-authored together Kan Guo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction
Hit paper breakdown →
2020268
2 2015153
3 2021139
4 2020131
5 202264
6 201341
7 202017
8 20239
9 20248
10 20247
11 20236
12 20204
13 20183
14 20142
15 20252
16 20182
17 20151
18 20131
19 20250
20 20250

About Kan Guo

Kan Guo is a scholar working on Building and Construction, Computer Vision and Pattern Recognition, Computational Mechanics, Transportation and Computer Graphics and Computer-Aided Design, having authored 20 papers that have together received 858 indexed citations. Recurring topics across this work include Traffic Prediction and Management Techniques (9 papers), 3D Shape Modeling and Analysis (8 papers), Computer Graphics and Visualization Techniques (6 papers), Transportation Planning and Optimization (5 papers), Data Management and Algorithms (3 papers), Time Series Analysis and Forecasting (3 papers), Traffic control and management (3 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Transportation (355 citations), Building and Construction (495 citations), Computer Graphics and Computer-Aided Design (90 citations), Signal Processing (120 citations) and Control and Systems Engineering (241 citations). Kan Guo has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Yongli Hu, Junbin Gao, Baocai Yin, Yanfeng Sun, Sean Qian, Xiaowu Chen, Dongqing Zou, Hao Liu, Ke Zhang and Qiang Fu. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, IET Intelligent Transport Systems, Science China Information Sciences, IEEE Intelligent Transportation Systems Magazine and Knowledge-Based Systems.

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.

Explore authors with similar magnitude of impact