D.C. Chin

29 papers receiving 533 citations

Peers

D.C. Chin
Comparison fields: 5 of 60
  • Transportation 132
  • Management Science and Operations Research 170
  • Control and Systems Engineering 296
  • Building and Construction 127
  • Computational Theory and Mathematics 117
Replace L. A. Prashanth with:
L. A. Prashanth India
Yo Ishizuka Japan
Marc S. Meketon United States
Cihan H. Tuncbilek United States
Alain B. Zemkoho United Kingdom
Dingju Zhu China
Benjamin Jansen Netherlands
S.D. Hill United States
Leonardo Amaral Mozelli Brazil
László Gerencsér Hungary
D.C. Chin relative to L. A. Prashanth India L. A. Prashanth's profile →
Citations per field
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L. A. Prashanth · 1×
Citations per year

Countries citing papers authored by D.C. Chin

Since Specialization
Citations

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

Fields of papers citing papers by D.C. Chin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D.C. Chin. 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 D.C. Chin. The network helps show where D.C. Chin may publish in the future.

Co-authors

The 6 scholars most cited alongside D.C. Chin, 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 D.C. Chin Line = papers co-authored together D.C. Chin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997120
2 1997102
3 200854
4 199449
5 200137
6 200232
7 200126
8 199124
9 199923
10 199916
11 200215
12
NETWORKWIDE APPROACH TO OPTIMAL SIGNAL TIMING FOR INTEGRATED TRANSIT VEHICLE AND TRAFFIC OPERATIONS
199711
13 200210
14 19979
15 19958
16 19997
17 19905
18 20024
19 20003
20
A TRAFFIC FLOW SIMULATOR FOR TRAFFIC SIGNAL CONTROL
19972

About D.C. Chin

D.C. Chin is a scholar working on Control and Systems Engineering, Management Science and Operations Research, Artificial Intelligence, Building and Construction and Transportation, having authored 31 papers that have together received 568 indexed citations. Recurring topics across this work include Simulation Techniques and Applications (11 papers), Traffic control and management (11 papers), Traffic Prediction and Management Techniques (9 papers), Transportation Planning and Optimization (8 papers), Advanced Multi-Objective Optimization Algorithms (7 papers), Stochastic processes and financial applications (6 papers), Geophysical and Geoelectrical Methods (3 papers) and Control Systems and Identification (3 papers). The work is most often cited by research in Transportation (132 citations), Management Science and Operations Research (170 citations), Control and Systems Engineering (296 citations), Building and Construction (127 citations) and Computational Theory and Mathematics (117 citations). D.C. Chin has collaborated with scholars based in United States and Singapore. Frequent co-authors include James C. Spall, J.L. Maryak, C.C. Hang, R. Srinivasan, Robert E. Ball and Raghavan Srinivasan. Their work appears in journals such as IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Automatic Control, Transportation Research Part C Emerging Technologies, Neural Networks and Computational Statistics & Data Analysis.

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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