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D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Engineering and Technology 67 1330 1261 258 257 433 14918

Shuaian Wang publications per year

The chart shows the history of publications by Shuaian Wang between 2007 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Shuaian Wang published across 20 years, from 2007 to 2026, averaging 26.4 papers a year. Output peaked at 73 publications in 2023. 73 of the 528 publications appeared in the last two years.

No. of publications
20 40 60
Bar chart. Horizontal axis: year, 2007 to 2026. Vertical axis: number of publications, 0 to 73. Peak 73 publications in 2023. 2007: 1 publication 2008: 0 publications 2009: 0 publications 2010: 0 publications 2011: 6 publications 2012: 14 publications 2013: 17 publications 2014: 21 publications 2015: 20 publications 2016: 12 publications 2017: 34 publications 2018: 34 publications 2019: 28 publications 2020: 20 publications 2021: 43 publications 2022: 62 publications 2023: 73 publications 2024: 70 publications 2025: 68 publications 2026: 5 publications
2007 2026

528 publications in total across all disciplines

View publications per year as a table
Shuaian Wang: publications per year, 2007 to 2026
Year Publications
2007 1
2008 0
2009 0
2010 0
2011 6
2012 14
2013 17
2014 21
2015 20
2016 12
2017 34
2018 34
2019 28
2020 20
2021 43
2022 62
2023 73
2024 70
2025 68
2026 5
Total 528
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Shuaian Wang 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 Shuaian Wang sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: publications, 38–47 to 804+. Vertical axis: number of scientists, 0 to 457. Most scientists, 457, have 148–157 publications. The last bar groups every scientist with 804 publications or more. The highlighted bar, 428–437 publications, is where this scientist sits. 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
38–47 publications 804+

This scientist: 433 publications — 91st percentile

91% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Engineering and Technology scientists by publication count, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
Publications Scientists This scientist
38–47 20
48–57 35
58–67 96
68–77 135
78–87 190
88–97 259
98–107 283
108–117 369
118–127 341
128–137 386
138–147 372
148–157 457
158–167 415
168–177 407
178–187 421
188–197 378
198–207 403
208–217 317
218–227 346
228–237 321
238–247 260
248–257 280
258–267 240
268–277 214
278–287 242
288–297 203
298–307 166
308–317 154
318–327 175
328–337 159
338–347 99
348–357 131
358–367 106
368–377 118
378–387 97
388–397 108
398–407 82
408–417 71
418–427 64
428–437 55 433
438–447 54
448–457 60
458–467 47
468–477 40
478–487 30
488–497 29
498–507 38
508–517 40
518–527 32
528–537 23
538–547 28
548–557 23
558–567 19
568–577 16
578–587 17
588–597 18
598–607 22
608–617 15
618–627 9
628–637 11
638–647 21
648–657 12
658–667 9
668–677 11
678–687 9
688–697 6
698–707 14
708–717 7
718–727 8
728–737 10
738–747 9
748–757 5
758–767 5
768–777 11
778–787 7
788–797 2
798–803 4
804+ 100
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Shuaian Wang 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 Shuaian Wang sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: D-Index, 30 to 107+. Vertical axis: number of scientists, 0 to 426. Most scientists, 426, have 42 D-Index. The last bar groups every scientist with 107 D-Index or more. The highlighted bar, 67 D-Index, is where this scientist sits. 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: 67 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Engineering and Technology scientists by D-index, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
D-Index Scientists This scientist
30 59
31 114
32 129
33 189
34 200
35 262
36 311
37 312
38 350
39 385
40 348
41 362
42 426
43 380
44 310
45 341
46 301
47 306
48 271
49 246
50 210
51 253
52 213
53 221
54 195
55 186
56 170
57 167
58 166
59 144
60 152
61 141
62 138
63 131
64 118
65 114
66 119
67 95 67
68 87
69 77
70 89
71 69
72 54
73 46
74 55
75 54
76 49
77 53
78 46
79 28
80 39
81 36
82 24
83 26
84 36
85 18
86 25
87 19
88 26
89 27
90 23
91 15
92 12
93 9
94 15
95 10
96 13
97 13
98 9
99 7
100 7
101 8
102 7
103 7
104 9
105 6
106 9
107+ 99
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Overview

Shuaian Wang is affiliated with Hong Kong Polytechnic University in China and primarily works in the field of Engineering. Their research spans various subfields including Industrial and Manufacturing Engineering, Environmental Engineering, Ocean Engineering, Automotive Engineering, and Transportation.

Their recent papers cover several dimensions of transportation and maritime studies, addressing issues like fuel consumption, electric vehicle infrastructure, and fleet management. Notable publications include:

  • Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship (2020), published in Transportation Research Part E Logistics and Transportation Review
  • Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile (2021), published in Transportation Research Part E Logistics and Transportation Review
  • Mitigate the range anxiety: Siting battery charging stations for electric vehicle drivers (2020), published in Transportation Research Part C Emerging Technologies
  • Green technology adoption for fleet deployment in a shipping network (2020), published in Transportation Research Part B Methodological
  • Data analytics for fuel consumption management in maritime transportation: Status and perspectives (2021), published in Transportation Research Part E Logistics and Transportation Review

The main topics of Shuaian Wang's work exhibit a focus on maritime and transportation challenges, including:

  • Maritime Transport Emissions and Efficiency
  • Maritime Ports and Logistics
  • Maritime Navigation and Safety
  • Vehicle Routing Optimization Methods
  • Transportation Planning and Optimization
  • Transportation and Mobility Innovations
  • Vehicle emissions and performance

Collaboration is a significant aspect of their research, with frequent co-authors including Ran Yan, Lu Zhen, Xuecheng Tian, Min Xu, and Dan Zhuge. These partnerships span multiple papers and contribute to the breadth of their scientific output.

Key venues for their published work include:

  • SSRN Electronic Journal
  • Transportation Research Part B Methodological
  • Mathematics
  • Transportation Research Part E Logistics and Transportation Review
  • Transportation Research Part C Emerging Technologies

In addition to journal articles, Shuaian Wang has contributed to academic book publications. One example is the book titled Applications of Machine Learning and Data Analytics Models in Maritime Transportation, published in 2022 by the Institution of Engineering and Technology.

Best Publications

  • Sailing speed optimization for container ships in a liner shipping network

    Shuaian Wang;Qiang Meng

  • Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions

    Qiang Meng;Shuaian Wang;Henrik Andersson;Kristian Thun

  • How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications

    Dong Yang;Lingxiao Wu;Shuaian Wang;Haiying Jia

  • Liner shipping service network design with empty container repositioning

    Qiang Meng;Shuaian Wang

  • Liner ship route schedule design with sea contingency time and port time uncertainty

    Shuaian Wang;Qiang Meng

  • Integrated internal truck, yard crane and quay crane scheduling in a container terminal considering energy consumption

    Junliang He;Youfang Huang;Wei Yan;Shuaian Wang

  • Global optimization methods for the discrete network design problem

    Shuaian Wang;Qiang Meng;Hai Yang

  • On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models

    Xiaobo Qu;Shuaian Wang;Jin Zhang

  • A two-phase optimization model for the demand-responsive customized bus network design

    Di Huang;Yu Gu;Shuaian Wang;Zhiyuan Liu

  • Liner Ship Fleet Deployment with Container Transshipment Operations

    Shuaian Wang;Qiang Meng

  • Bunker consumption optimization methods in shipping: A critical review and extensions

    Shuaian Wang;Qiang Meng;Zhiyuan Liu

  • Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand

    Qiang Meng;Tingsong Wang;Shuaian Wang

  • Robust schedule design for liner shipping services

    Shuaian Wang;Qiang Meng

  • Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship

    Ran Yan;Shuaian Wang;Yuquan Du

  • On the Stochastic Fundamental Diagram for Freeway Traffic: Model Development, Analytical Properties, Validation, and Extensive Applications

    Xiaobo Qu;Jin Zhang;Shuaian Wang

  • Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile

    Le Zhang;Le Zhang;Shuaian Wang;Xiaobo Qu

  • A tree-structured crash surrogate measure for freeways

    Yan Kuang;Xiaobo Qu;Shuaian Wang

  • Robust optimization model of schedule design for a fixed bus route

    Yadan Yan;Yadan Yan;Qiang Meng;Shuaian Wang;Xiucheng Guo

  • Two-phase optimal solutions for ship speed and trim optimization over a voyage using voyage report data

    Yuquan Du;Qiang Meng;Shuaian Wang;Haibo Kuang

  • Mitigate the range anxiety: Siting battery charging stations for electric vehicle drivers

    Min Xu;Hai Yang;Shuaian Wang

  • Optimal Distance Tolls under Congestion Pricing and Continuously Distributed Value of Time

    Qiang Meng;Zhiyuan Liu;Shuaian Wang

  • Speed-based toll design for cordon-based congestion pricing scheme

    Zhiyuan Liu;Qiang Meng;Shuaian Wang

  • Optimal operating strategy for a long-haul liner service route

    Qiang Meng;Shuaian Wang

Frequent Co-Authors

Qiang Meng
Qiang Meng National University of Singapore
Zhiyuan Liu
Zhiyuan Liu Southeast University
Xiaobo Qu
Xiaobo Qu Xiamen University
Lu Zhen
Lu Zhen Shanghai University
Gilbert Laporte
Gilbert Laporte HEC Montréal
Hai Yang
Hai Yang Hong Kong University of Science and Technology
Chung Yee Lee
Chung Yee Lee Hong Kong University of Science and Technology
Michael G.H. Bell
Michael G.H. Bell University of Sydney
Harilaos N. Psaraftis
Harilaos N. Psaraftis Technical University of Denmark
Kjetil Fagerholt
Kjetil Fagerholt Norwegian University of Science and Technology

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