World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
55
Citations
15906
World Ranking
2936
National Ranking
885

Dominique Lord 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 Dominique Lord 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: 240 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Dominique Lord 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 Dominique Lord 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: 55 D-Index — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Overview

Dominique Lord is affiliated with Texas A&M University in the United States. Their research focuses primarily on engineering, with significant contributions to safety, risk, reliability, and quality, as well as transportation and related subfields.

The scientist has published extensively in fields intersecting traffic safety, urban transport, and traffic management. Their work addresses various aspects such as traffic and road safety, urban transport and accessibility, traffic prediction and management techniques, injury epidemiology and prevention, and automotive and human injury biomechanics.

Frequent coauthors include Srinivas Reddy Geedipally, Subasish Das, Bahar Dadashova, Xiao Qin, and Richard Dzinyela, illustrating a collaborative approach to research across multiple related domains.

Dominique Lord has contributed to several notable publication venues, with many papers appearing in:

  • Accident Analysis & Prevention
  • SSRN Electronic Journal
  • arXiv (Cornell University)
  • Transportmetrica A Transport Science
  • Transportation Research Record Journal of the Transportation Research Board

Recent papers authored or coauthored by the scientist include:

  • "Quantifying the automated vehicle safety performance: A scoping review of the literature, evaluation of methods, and directions for future research" (2021), published in Accident Analysis & Prevention
  • "Impacts of Autonomous Vehicles on Public Health: A Conceptual Model and Policy Recommendations" (2020), published in Sustainable Cities and Society
  • "Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial" (2021), published in Accident Analysis & Prevention
  • "A multi-year statistical analysis of driver injury severities in single-vehicle freeway crashes with and without airbags deployed" (2024), published in Analytic Methods in Accident Research
  • "Examining driver distraction in the context of driving speed: An observational study using disruptive technology and naturalistic data" (2021), published in Accident Analysis & Prevention

Dominique Lord has also published several books through the Transportation Research Board eBooks, including:

  • Safety Prediction Methodology and Analysis Tool for Freeways and Interchanges (2021)
  • Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 (2020)
  • Safety Prediction Models for Six-Lane and One-Way Urban and Suburban Arterials (2022)
  • Safety Effects of Raising Speed Limits to 75 mph and Higher (2022)
  • Guide to Understanding Effects of Raising Speed Limits (2022)

Best Publications

  • The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

    Dominique Lord;Fred L. Mannering

  • The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives.

    Peter T. Savolainen;Fred L. Mannering;Dominique Lord;Mohammed Abdul Quddus

  • Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

    Dominique Lord;Simon P. Washington;John N. Ivan

  • MODELING TRAFFIC CRASH-FLOW RELATIONSHIPS FOR INTERSECTIONS: DISPERSION PARAMETER, FUNCTIONAL FORM, AND BAYES VERSUS EMPIRICAL BAYES METHODS

    Shaw-Pin Miaou;Dominique Lord

  • Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

    Dominique Lord

  • Comparing Three Commonly Used Crash Severity Models on Sample Size Requirements: Multinomial Logit, Ordered Probit, and Mixed Logit Models

    Fan Ye;Dominique Lord

  • Safety Effect of Roundabout Conversions in the United States: Empirical Bayes Observational Before-After Study

    Bhagwant N. Persaud;Richard A. Retting;Per E. Garder;Dominique Lord

  • Predicting motor vehicle crashes using Support Vector Machine models.

    Xiansheng Li;Dominique Lord;Yunlong Zhang;Yuanchang Xie

  • Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity

    Eun Sug Park;Dominique Lord

  • Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure

    Dominique Lord;Bhagwant N. Persaud

  • Further notes on the application of zero-inflated models in highway safety

    Dominique Lord;Simon P. Washington;John N. Ivan

  • Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis

    Yuanchang Xie;Dominique Lord;Yunlong Zhang

  • Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments.

    Dominique Lord;Abdelaziz Manar;Anna Vizioli

  • Application of the Conway–Maxwell–Poisson generalized linear model for analyzing motor vehicle crashes

    Dominique Lord;Seth D. Guikema;Srinivas Reddy Geedipally

  • Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective

    Dominique Lord;Luis F. Miranda-Moreno

  • Application of finite mixture models for vehicle crash data analysis

    Byung-Jung Park;Dominique Lord

  • The negative binomial-Lindley generalized linear model: Characteristics and application using crash data

    Srinivas Reddy Geedipally;Dominique Lord;Soma Sekhar Dhavala

  • CRASH AND INJURY REDUCTION FOLLOWING INSTALLATION OF ROUNDABOUTS IN THE UNITED STATES

    Richard A. Retting;Bhagwant N. Persaud;Per E. Garder;Dominique Lord

  • Investigation of Effects of Underreporting Crash Data on Three Commonly Used Traffic Crash Severity Models: Multinomial Logit, Ordered Probit, and Mixed Logit

    Fan Ye;Dominique Lord

  • Extension of the application of conway-maxwell-poisson models: analyzing traffic crash data exhibiting underdispersion.

    Dominique Lord;Srinivas Reddy Geedipally;Seth D. Guikema

  • Calibration and Transferability of Accident Prediction Models for Urban Intersections

    Bhagwant N. Persaud;Dominique Lord;Joseph Palmisano

  • Modeling Motor Vehicle Crashes Using Poisson-Gamma Models: Examining Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter

    Dominique Lord

  • Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: Empirical Analysis

    Yuanchang Xie;Dominique Lord;Yunlong Zhang

Frequent Co-Authors

Yunlong Zhang
Yunlong Zhang Texas A&M University
Kay Fitzpatrick
Kay Fitzpatrick Texas A&M University
Bhagwant Persaud
Bhagwant Persaud Toronto Metropolitan University
Simon Washington
Simon Washington Queensland University of Technology
Liping Fu
Liping Fu University of Waterloo
Luis F Miranda-Moreno
Luis F Miranda-Moreno McGill University
Seth D. Guikema
Seth D. Guikema University of Michigan–Ann Arbor
Georges Dionne
Georges Dionne HEC Montréal
Bani K. Mallick
Bani K. Mallick Texas A&M University
Fred L. Mannering
Fred L. Mannering University of South Florida

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