Pin Wang

39 papers receiving 923 citations

Peers

Pin Wang
Comparison fields: 5 of 90
  • Automotive Engineering 593
  • Control and Systems Engineering 444
  • Safety, Risk, Reliability and Quality 137
  • Building and Construction 167
  • Transportation 71
Replace Anup Doshi with:
Anup Doshi United States
Zhongcheng Wu China
Glen Berseth Canada
Andrew Best United States
Akshay Rangesh United States
Katharina Muelling United States
Dražen Brščić Japan
Lisheng Jin China
Fabio Tango Italy
Pin Wang relative to Anup Doshi United States Anup Doshi's profile →
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Countries citing papers authored by Pin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Pin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Pin Wang, 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 Pin Wang Line = papers co-authored together Pin Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018197
2 2017103
3 202082
4 201959
5 202158
6 201153
7 201953
8 202150
9 202148
10 202134
11 201633
12 201730
13 202419
14 202017
15 201016
16 202115
17 202010
18 20217
19 20117
20 20176

About Pin Wang

Pin Wang is a scholar working on Automotive Engineering, Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition and Building and Construction, having authored 42 papers that have together received 946 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (17 papers), Traffic control and management (9 papers), Reinforcement Learning in Robotics (8 papers), Video Surveillance and Tracking Methods (6 papers), Traffic Prediction and Management Techniques (4 papers), Anomaly Detection Techniques and Applications (3 papers), Action Observation and Synchronization (3 papers) and Sport Psychology and Performance (3 papers). The work is most often cited by research in Automotive Engineering (593 citations), Control and Systems Engineering (444 citations), Safety, Risk, Reliability and Quality (137 citations), Building and Construction (167 citations) and Transportation (71 citations). Pin Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Ching‐Yao Chan, Arnaud de La Fortelle, Xuxin Cheng, Fei Ye, Biao Yang, Jiucai Zhang, Mengyu Guo, Ding‐Hsiang Huang, Shen Zhang and Tianyu Shi. Their work appears in journals such as IET Intelligent Transport Systems, Frontiers in Psychology, IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Intelligent Transportation Systems and Dyes and Pigments.

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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