World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
65
Citations
15858
World Ranking
1529
National Ranking
500

Bin Ran publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Bin Ran sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 134 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 117 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 59 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 495 publications — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Bin Ran D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Bin Ran sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 128 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 349 scientists 41 D-Index: 362 scientists 42 D-Index: 425 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 94 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 24 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 65 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Bin Ran is affiliated with the University of Wisconsin-Madison in the United States. Their work spans multiple aspects of transportation engineering and control systems, with a strong focus on traffic management, autonomous vehicle technology, and transportation planning.

Bin Ran's recent publications include the following papers:

  • Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment, 2021, Transportation Research Part C Emerging Technologies
  • Exploring AIS data for intelligent maritime routes extraction, 2020, Applied Ocean Research
  • Integrated Schedule and Trajectory Optimization for Connected Automated Vehicles in a Conflict Zone, 2020, IEEE Transactions on Intelligent Transportation Systems
  • Automated traffic incident detection with a smaller dataset based on generative adversarial networks, 2020, Accident Analysis & Prevention
  • Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm, 2020, Transportation Research Part C Emerging Technologies

Frequent co-authors who have collaborated with Bin Ran include:

  • Haotian Shi
  • Yang Zhou
  • Keshu Wu
  • Xu Qu
  • Linheng Li

Bin Ran has contributed extensively to several publication venues, with the following being the most frequent:

  • IEEE Transactions on Intelligent Transportation Systems
  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Transportation Research Part C Emerging Technologies
  • Physica A Statistical Mechanics and its Applications

The main fields of study for Bin Ran's research are:

  • Engineering
  • Social Sciences

Subfields within their research portfolio include:

  • Control and Systems Engineering
  • Transportation
  • Building and Construction
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality

Key topics covered by Bin Ran span across:

  • Traffic control and management
  • Transportation Planning and Optimization
  • Traffic Prediction and Management Techniques
  • Autonomous Vehicle Technology and Safety
  • Traffic and Road Safety
  • Human Mobility and Location-Based Analysis
  • Vehicular Ad Hoc Networks (VANETs)

Best Publications

  • A hybrid deep learning based traffic flow prediction method and its understanding

    Yuankai Wu;Huachun Tan;Lingqiao Qin;Bin Ran

  • Dynamic Urban Transportation Network Models: Theory and Implications for Intelligent Vehicle-Highway Systems

    Bin Ran;David E. Boyce

  • A method of providing travel time predictions

    Bin Ran

  • MODELING DYNAMIC TRANSPORTATION NETWORKS

    Bin Ran;David Boyce

  • Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches

    Jiateng Yin;Lixing Yang;Tao Tang;Ziyou Gao

  • A new class of instantaneous dynamic user-optimal traffic assignment models

    Bin Ran;David E. Boyce;Larry J. LeBlanc

  • Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

    Jiateng Yin;Tao Tang;Lixing Yang;Ziyou Gao

  • Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

    Unknown

  • Modeling Dynamic Transportation Networks: An Intelligent Transportation System Oriented Approach

    Bin Ran;David E. Boyce

  • Short-Term Traffic Prediction Based on Dynamic Tensor Completion

    Huachun Tan;Yuankai Wu;Bin Shen;Peter J. Jin

  • A dynamic lane-changing trajectory planning model for automated vehicles

    Da Yang;Da Yang;Shiyu Zheng;Cheng Wen;Peter J. Jin

  • Use of Local Linear Regression Model for Short-Term Traffic Forecasting

    Hongyu Sun;Henry X. Liu;Heng Xiao;Rachel R. He

  • Central processing and combined central and local processing of personalized real-time traveler information over internet/intranet

    Bin Ran;Jing Li

  • Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm

    Linchao Li;Linchao Li;Lingqiao Qin;Xu Qu;Jian Zhang

  • Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method

    Linchao Li;Jian Zhang;Yonggang Wang;Bin Ran

  • Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

    Linheng Li;Jing Gan;Xinkai Ji;Xu Qu

  • Short Term Traffic Forecasting Using the Local Linear Regression Model

    Hongyu Sun;Henry X. Liu;Heng Xiao;Bin Ran

  • A link-based variational inequality model for dynamic departure time/route choice

    Bin Ran;Randolph W Hall;David E Boyce

  • Developing a Dynamic Traffic Management Modeling Framework for Hurricane Evacuation

    Bridget Barrett;Bin Ran;Rekha Pillai

  • Autonomous Vehicle-Intersection Coordination Method in a Connected Vehicle Environment

    Peiqun Lin;Jiahui Liu;Peter J. Jin;Bin Ran

  • Tensor based missing traffic data completion with spatial–temporal correlation

    Bin Ran;Huachun Tan;Yuankai Wu;Peter J. Jin

  • Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data

    Yang Cheng;Xiao Qin;Jing Jin;Bin Ran

  • An Exploratory Shockwave Approach to Estimating Queue Length Using Probe Trajectories

    Yang Cheng;Xiao Qin;Jing Jin;Bin Ran

Frequent Co-Authors

David E. Boyce
David E. Boyce Northwestern University
Henry X. Liu
Henry X. Liu University of Michigan–Ann Arbor
Jeffrey S. Russell
Jeffrey S. Russell University of Wisconsin–Madison
Michael C. Ferris
Michael C. Ferris University of Wisconsin–Madison
Lixing Yang
Lixing Yang Beijing Jiaotong University
Tao Tang
Tao Tang Beijing Jiaotong University
Der-Horng Lee
Der-Horng Lee Zhejiang University
Pitu B. Mirchandani
Pitu B. Mirchandani Arizona State University
Limin Jia
Limin Jia Beijing Jiaotong University
Helai Huang
Helai Huang Central South University

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