World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
61
Citations
21339
World Ranking
1994
National Ranking
57

Kai Nagel 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 Kai Nagel sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 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: 118 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: 60 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: 311 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Kai Nagel 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 Kai Nagel sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 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: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 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: 95 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: 25 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: 61 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Overview

Kai Nagel is affiliated with the Technical University of Berlin in Germany. Their main field of study is Engineering, with a focus on areas such as Transportation, Automotive Engineering, Modeling and Simulation, Building and Construction, and Infectious Diseases.

Their research covers a range of topics including:

  • Transportation and Mobility Innovations
  • Transportation Planning and Optimization
  • COVID-19 epidemiological studies
  • Urban and Freight Transport Logistics
  • Human Mobility and Location-Based Analysis
  • Urban Transport and Accessibility
  • SARS-CoV-2 and COVID-19 Research

Frequent collaborators include:

  • Sebastian Alexander Müller
  • Ricardo Ewert
  • Christian Rakow
  • William Charlton
  • Tim Conrad

Common publication venues for Kai Nagel's work are:

  • Deposit Once (Technische Universität Berlin)
  • Procedia Computer Science
  • Transportation research procedia
  • Zenodo (CERN European Organization for Nuclear Research)
  • bioRxiv (Cold Spring Harbor Laboratory)

Their recent papers include:

  • Mobility traces and spreading of COVID-19, 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data, 2021, PLoS ONE
  • Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles, 2020, Transportation Research Part A Policy and Practice
  • Electrification of Urban Freight Transport - a Case Study of the Food Retailing Industry, 2020, Procedia Computer Science
  • Interplay Between Risk Perception, Behavior, and COVID-19 Spread, 2022, Frontiers in Physics

Kai Nagel has also contributed to book publications, including:

  • Handbuch Einsatz automatisierter und vernetzter Fahrzeuge für den öffentlichen Verkehr, 2021, Technische Universität Berlin
  • Georg Spalatin als Übersetzer, 2022, Tectum - ein Verlag in der Nomos Verlagsgesellschaft eBooks

Best Publications

  • A cellular automaton model for freeway traffic

    Kai Nagel;Michael Schreckenberg

  • The Multi-Agent Transport Simulation MATSim

    Andreas Horni;Kai Nagel;Kay W Axhausen

  • Discrete stochastic models for traffic flow

    M. Schreckenberg;M. Schreckenberg;A. Schadschneider;A. Schadschneider;K. Nagel;K. Nagel;N. Ito;N. Ito

  • Two-lane traffic rules for cellular automata: A systematic approach

    Kai Nagel;Dietrich E. Wolf;Peter Wagner;Peter Wagner;Patrice Simon

  • Two lane traffic simulations using cellular automata

    M. Rickert;M. Rickert;K. Nagel;M. Schreckenberg;A. Latour

  • Particle Hopping Models and Traffic Flow Theory

    Kai Nagel

  • Emergent traffic jams

    Kai Nagel;Kai Nagel;Maya Paczuski;Maya Paczuski

  • TRANSIMS: TRANSPORTATION ANALYSIS AND SIMULATION SYSTEM

    L Smith;R Beckman;D Anson;K Nagel

  • Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling

    Kai Nagel;Peter Wagner;Richard Woesler

  • Generating Complete All-Day Activity Plans with Genetic Algorithms

    David Charypar;Kai Nagel

  • MATSim-T : Architecture and Simulation Times

    Michael Balmer;Marcel Rieser;Konrad Meister;David Charypar

  • Parallel implementation of the TRANSIMS micro-simulation

    Kai Nagel;Marcus Rickert

  • Realistic multi-lane traffic rules for cellular automata

    Peter Wagner;Kai Nagel;Kai Nagel;Dietrich E. Wolf

  • Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

    Michael Balmer;Kay W. Axhausen;Kai Nagel

  • Agent-based simulation of travel demand: Structure and computational performance of MATSim-T

    Michael Balmer;Konrad Meister;Marcel Rieser;Kai Nagel

  • Deterministic models for traffic jams

    Kai Nagel;Hans J. Herrmann

  • An agent-based microsimulation model of Swiss travel: First results

    Bryan Raney;Nurhan Cetin;Andreas Völlmy;Milenko Vrtic

  • The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations

    Gregor Lämmel;Dominik Grether;Kai Nagel

  • Towards Truly Agent-Based Traffic and Mobility Simulations

    Michael Balmer;Nurhan Cetin;Kai Nagel;Bryan Raney

  • 5 An improved framework for large-scale multi-agent simulations of travel behaviour

    Bryan Raney;Kai Nagel

  • A computational study of routing algorithms for realistic transportation networks

    R. Jacob;M. Marathe;K. Nagel

  • Measuring vulnerability to promote disaster resilient societies : conceptual frameworks and definitions

    Joern Birkmann;Stefan Dech;Gerd Hirzinger;Reinhard Klein

Frequent Co-Authors

Peter Wagner
Peter Wagner German Aerospace Center
Christopher L. Barrett
Christopher L. Barrett University of Virginia
Chandra R. Bhat
Chandra R. Bhat The University of Texas at Austin
Stefan Dech
Stefan Dech German Aerospace Center
Florian Siegert
Florian Siegert Ludwig-Maximilians-Universität München
Hannes Taubenböck
Hannes Taubenböck German Aerospace Center
Michael Schreckenberg
Michael Schreckenberg University of Duisburg-Essen
Christof Schütte
Christof Schütte Freie Universität Berlin
Joern Birkmann
Joern Birkmann University of Stuttgart

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