Eugene Vinitsky

1.8k citations
23 papers · 563 indexed · h-index 10
Topics
Traffic control and management (17 papers)Autonomous Vehicle Technology and Safety (12 papers)Traffic Prediction and Management Techniques (7 papers)

In The Last Decade

Eugene Vinitsky

21 papers receiving 549 citations

Peers

Eugene Vinitsky
Comparison fields: 5 of 51
  • Control and Systems Engineering 428
  • Automotive Engineering 328
  • Building and Construction 166
  • Transportation 133
  • Artificial Intelligence 99
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Countries citing papers authored by Eugene Vinitsky

Since Specialization
Citations

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

Fields of papers citing papers by Eugene Vinitsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eugene Vinitsky

This figure shows the co-authorship network connecting the top 25 collaborators of Eugene Vinitsky. A scholar is included among the top collaborators of Eugene Vinitsky 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 Eugene Vinitsky. Eugene Vinitsky 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
#WorkIndexed citations
1 0
2 1
3 8
4 6
5 4
6 38
7 0
8 23
9 112
10
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
3
11 2
12 8
13 44
14 27
15
Benchmarks for reinforcement learning in mixed-autonomy traffic
38
16
Flow: Open Source Reinforcement Learning for Traffic Control
3
17 66
18
Emergent Behaviors in Mixed-Autonomy Traffic
32
19
Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control.
101
20 7

About Eugene Vinitsky

Eugene Vinitsky is a scholar working on Automotive Engineering, Control and Systems Engineering and Building and Construction, having authored 23 papers that have together received 563 indexed citations. Recurring topics across this work include Traffic control and management (17 papers), Autonomous Vehicle Technology and Safety (12 papers) and Traffic Prediction and Management Techniques (7 papers). The work is most often cited by research in Automotive Engineering (328 citations), Transportation (133 citations) and Control and Systems Engineering (428 citations). Eugene Vinitsky has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Alexandre M. Bayen, Cathy Wu, Kanaad Parvate, Aboudy Kreidieh, Abdul Rahman Kreidieh, Zhongxia Yan, Andreas A. Malikopoulos, Behdad Chalaki, Logan E. Beaver and Ankur Mehta. Their work appears in journals such as IEEE Transactions on Robotics, IEEE Control Systems and IEEE Transactions on Automation Science and Engineering.

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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