Kun-Feng Wu

29 papers receiving 540 citations

Peers

Kun-Feng Wu
Comparison fields: 5 of 54
  • Safety, Risk, Reliability and Quality 440
  • Transportation 156
  • Automotive Engineering 193
  • Building and Construction 134
  • Radiological and Ultrasound Technology 34
Replace Harry Lahrmann with:
Harry Lahrmann Denmark
Do‐Gyeong Kim South Korea
Ali Ghasemzadeh United States
Jinn-Tsai Wong Taiwan
Rami Harb United States
Navid Nadimi Iran
Ghulam H. Bham United States
Lu Ma China
Joshua Stipancic Canada
Kun-Feng Wu relative to Harry Lahrmann Denmark Harry Lahrmann's profile →
Citations per field
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Citations per year

Countries citing papers authored by Kun-Feng Wu

Since Specialization
Citations

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

Fields of papers citing papers by Kun-Feng Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kun-Feng Wu. 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 Kun-Feng Wu. The network helps show where Kun-Feng Wu may publish in the future.

Co-authors

The 12 scholars most cited alongside Kun-Feng Wu, 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 Kun-Feng Wu Line = papers co-authored together Kun-Feng Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201198
2 201473
3 201268
4 201565
5 201830
6 201326
7 201522
8 201819
9 202018
10 201717
11 200013
12 201212
13 200012
14 201811
15 201911
16 201210
17 20169
18
Defining, Screening, and Validating Crash Surrogate Events Using Naturalistic Driving Data
20118
19 20127
20 20226

About Kun-Feng Wu

Kun-Feng Wu is a scholar working on Safety, Risk, Reliability and Quality, Transportation, Automotive Engineering, Social Psychology and Public Health, Environmental and Occupational Health, having authored 29 papers that have together received 557 indexed citations. Recurring topics across this work include Traffic and Road Safety (25 papers), Urban Transport and Accessibility (9 papers), Human-Automation Interaction and Safety (8 papers), Injury Epidemiology and Prevention (6 papers), Vehicle emissions and performance (5 papers), Transportation Planning and Optimization (4 papers), Traffic Prediction and Management Techniques (4 papers) and Occupational Health and Safety Research (3 papers). The work is most often cited by research in Safety, Risk, Reliability and Quality (440 citations), Transportation (156 citations), Automotive Engineering (193 citations), Building and Construction (134 citations) and Radiological and Ultrasound Technology (34 citations). Kun-Feng Wu has collaborated with scholars based in Taiwan, United States and Costa Rica. Frequent co-authors include Paul P. Jovanis, Jonathan Agüero-Valverde, Eric T. Donnell, Roger K. Yeh, Lekshmi Sasidharan, Kuancheng Huang, Martin T. Pietrucha, Chen Chen, Scott Himes and Tong Lin. Their work appears in journals such as Accident Analysis & Prevention, Transportation Research Part F Traffic Psychology and Behaviour, Transportation Research Part C Emerging Technologies, Traffic Injury Prevention and Transportation Research Part E Logistics and Transportation Review.

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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