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Reviewing the transport domain: an evolutionary bibliometrics and network analysis


Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography.

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  2. It should be mentioned that for this analysis the journal of “Transportation Research Record” is excluded because no keyword is reported for articles published in that journal.


  • Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.

    Article  Google Scholar 

  • Abbasi, A., Hossain, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(1), 403–412.

    Article  Google Scholar 

  • Abbasi, A., & Jaafari, A. (2013). Research impact and scholars’ geographical diversity. Journal of Informetrics, 7(3), 683–692.

    Article  Google Scholar 

  • Abbasi, A., Wigand, R., & Hossain, L. (2014). Measuring social capital through network analysis and its influence on individual performance. Library and Information Science Research, 36(1), 66–73.

    Article  Google Scholar 

  • Autry, C. W., & Griffis, S. E. (2005). A social anthropology of logistics research: Exploring productivity and collaboration in an emerging science. Transportation Journal, 44(4), 27–43.

    Google Scholar 

  • Bailón-Moreno, R., Jurado-Alameda, E., & Ruíz-Baños, R. (2006). The scientific network of surfactants: Structural analysis. Journal of the American Society for Information Science and Technology, 57(7), 949–960.

    Article  Google Scholar 

  • Bailón-Moreno, R., Jurado-Alameda, E., Ruíz-Baños, R., & Courtial, J. P. (2005). Analysis of the scientific field of physical chemistry of surfactants with the unified scientometric model. Fit of relational and activity indicators. Scientometrics, 63(2), 259–276.

    Article  Google Scholar 

  • Bontekoning, Y. M., Macharis, C., & Trip, J. J. (2004). Is a new applied transportation research field emerging? A review of intermodal rail-truck freight transport literature. Transportation Research Part A: Policy and Practice, 38(1), 1–34.

    Article  Google Scholar 

  • Bravo, J. J., & Vidal, C. J. (2013). Freight transportation function in supply chain optimization models: A critical review of recent trends. Expert Systems with Applications, 40(17), 6742–6757. doi:10.1016/j.eswa.2013.06.015.

    Article  Google Scholar 

  • Chen, C. (2004). CiteSpace: Visualizing patterns and trends in scientific literature. Obtenido de

  • Chen, C. (2014). The CiteSpace manual.

  • Chen, K., & Guan, J. (2011). A bibliometric investigation of research performance in emerging Nano biopharmaceuticals. Journal of Informetrics, 5, 233–247.

    Article  Google Scholar 

  • Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinion on Biological Therapy, 12(5), 593–608.

    Article  Google Scholar 

  • Chen, C., Song, I. L., Yuan, X., & Zhang, J. (2008). The thematic and citation landscape of Data and Knowledge Engineering (1985–2007). Data and Knowledge Engineering, 67, 234–259.

    Article  Google Scholar 

  • Cobo, M. J., Chiclana, F., & Collop, A. (2014). A bibliometric analysis of the intelligent transportation systems research based on science mapping. IEEE Transactions on Intelligent Transportation Systems, 15(2), 901–908.

    Article  Google Scholar 

  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science Mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.

    Article  MATH  Google Scholar 

  • Cruz, S. C. S., & Teixeira, A. A. C. (2010). The evolution of the cluster literature: Shedding light on the regional studies–regional science debate. Regional Studies, 44(9), 1263–1288.

    Article  Google Scholar 

  • Demir, E., Bektas, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237, 775–793.

    Article  MATH  Google Scholar 

  • Edler, D., Rosvall, M. (2015). The map equation software package. Available online at

  • Farahani, R., Miandoabchi, E., Szeto, W., & Rashidi, H. (2013). A review of urban transportation network design problems. European Journal of Operational Research, 229, 281–302.

    Article  MathSciNet  MATH  Google Scholar 

  • Feng, F., Zhang, L., Du, Y., & Wang, W. (2015). Visualization and quantitative study in bibliographic databases: a case in the field of university–industry cooperation. Journal of Informetrics, 9, 118–134.

    Article  Google Scholar 

  • Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social networks, 1(3), 215–239.

    Article  MathSciNet  Google Scholar 

  • Guan, J., & Liu, N. (2015). Invention profiles and uneven growth in the field of emerging nano-energy. Energy Policy, 76, 146–157.

    Article  Google Scholar 

  • He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.

    Google Scholar 

  • Hood, W. W., & Wilson, W. S. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291–314.

    Article  Google Scholar 

  • Karner, A., & Niemeier, D. (2013). Civil rights guidance and equity analysis methods for regional transportation plans: A critical review of literature and practice. Journal of Transport Geography, 33, 126–134.

    Article  Google Scholar 

  • Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.

    Article  Google Scholar 

  • Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In Proceedings of the 8th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (pp. 91–101), Edmonton, Alberta, Canada: ACM Press.

  • Lee, P. C., & Su, H. N. (2010). Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innovation: Management Policy and Practice, 12(1), 26–40.

    Article  Google Scholar 

  • Mansouri, S. A., Lee, H., & Aluko, O. (2015). Multi-objective decision support to enhance environmental sustainability in maritime shipping: A review and future directions. Transportation Research Part E: Logistics and Transportation Review, 78, 3–18.

    Article  Google Scholar 

  • Moral-Muñoz, J. A., Cobo, M. J., Chiclana, F., Collop, A., & Herrera-Viedma, E. (2016). Analyzing highly cited papers in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 17(4), 993–1001.

    Article  Google Scholar 

  • Niu, B. B., Loáiciga, H. A., Wang, Z., Zhan, F. B., & Hong, S. (2014). Twenty years of global groundwater research: A science citation index expanded-based bibliometric survey (1993–2012). Journal of Hydrology, 519, 966–975.

    Article  Google Scholar 

  • Pallis, A. A., Vitsounis, T. K., & De Langen, P. W. (2010). Port economics, policy and management: review of an emerging research field. Transport Reviews, 30(1), 115–161.

    Article  Google Scholar 

  • Persson, O., Danell, R., & WiborgSchneider, J. (2009). How to use Bibexcel for various types of bibliometric analysis. In F. Åström, R. Danell, B. Larsen, & J. WiborgSchneider (Eds.), Celebrating scholarly communication studies: A festschrift for Olle Persson at his 60th birthday (Vol. 5, pp. 9–24). Leuven, Belgium: International Society for Scientometrics and Informetrics.

    Google Scholar 

  • Porter, A. L., & Cunningham, S. W. (2004). Tech mining: exploiting new technologies for competitive advantage. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Rosvall, M., & Bergstrom, C. T. (2010). Mapping change in large networks. PLoS ONE, 5(1), e8694. doi:10.1371/journal.pone.0008694.

    Article  Google Scholar 

  • Sci2 Team (2009). Science of Science (Sci 2 ) Tool. Indiana University and SciTech Strategies. Retrieved from

  • Silva, E. G., & Teixeira, A. A. C. (2008). Surveying structural change: Seminal contributions and a bibliometric account. Structural Change and Economic Dynamics, 19(4), 273–300.

    Article  Google Scholar 

  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467.

    Article  Google Scholar 

  • SteadieSeifi, M., Dellaert, N., Nuijten, W., Woensel, T. V., & Raoufi, R. (2014). Multimodal freight transportation planning: a literature review. European Journal of Operational Research, 233, 1–15.

    Article  MATH  Google Scholar 

  • Van Eck, N., & Waltman, L. (2010). Software survey: Vosviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  • Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice (pp. 285–320). Berlin: Springer.

    Google Scholar 

  • Wang, H., He, Q., Liu, X., Zhuang, Y., & Hong, S. (2012). Global urbanization research from 1991 to 2009: a systematic research review. Landscape and Urban Planning, 104(34), 299–309.

    Article  Google Scholar 

  • Woo, S. H., Kang, D. J., & Martin, S. (2013). Seaport research: An analysis of research collaboration using social network analysis. Transport Reviews, 33(4), 460–475. doi:10.1080/01441647.2013.786766.

    Article  Google Scholar 

  • Zhou, J., & Dai, S. (2012). Urban and metropolitan freight transportation: A quick review of existing models. Journal of Transportation Systems Engineering and Information Technology, 12(4), 106–114.

    Article  Google Scholar 

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Correspondence to Ali Najmi.

Appendix 1

Appendix 1

Mapping and analysis on cited-authors

Table 3 categorises top 30 (out of 181,355 references of publications included in the dataset) cited authors. The number of most influential scholars in each university in addition to their discipline are shown in the first and third columns of the Table 20 (out of top 30 cited scholars) of these authors are from institutes located in the United States of America. Further, scholars at the University of California at Berkley have been more recognized in the field of transport. Moreover, Massachusetts Institute of Technology, the University of California at Irvine, and the University of California at Davis are jointly ranked second in the list having 2 authors among the top 30 highly cited authors. By categorising these 30 authors into two disciplines of “Transport and urban planning” and “Economics” based on their main field of research, it can be seen that 6 of them are economists which represents the inextricable relationship between transport and economic studies.

Table 3 An overview on the affiliations of top 30 cited authors

It is also interesting to know that almost all of these 30 authors published their highly cited works in the 1990s which might be just simply because of longer time they have had to collect citations.

Countries and institutes network analysis

As shown in Fig. 9, the USA with 14,534 articles outperform all other countries in term of the number of publications which confirms the finding of top 30 highly successful scholars of “Mapping and analysis on cited-authors” section. Among other countries, researchers of England, Canada, Republic of China, Australia, and Netherlands highly contributed to the area of transport planning operations and management with 1950, 1837, 1517, 1403, and 1263 papers, respectively. It should be noted that, compared to other countries in the list, using the colour rule mentioned in “Tools evaluation and selection” section, the number of publications by Australia has recently surged.

Fig. 9
figure 9

Distribution of authors according to the total number of publications by the countries in the dataset during 1991–2015. Note Slice length is 1 and publications are selected per slice

At the institute level, top institutes with regard to the number of publications are visualized in Fig. 10. A total of 6966 institutes were included in the dataset for the period of 1991 to 2015. The top 15 institutes in terms of frequency are also listed in Table 4. In the list, the University of California at Berkeley stands at the top followed by the University of Texas at Austin and the Delft University of Technology. From the number of publications point of view, the top ranked institutes in the area of transport are from USA, Australia, United Kingdoms, Netherlands and Canada. Canadian, Australian and British institutes are relatively more isolated in terms of research collaborations compared to American, Chinese and Dutch institutes.

Fig. 10
figure 10

Network of institutes and the strongest collaborations among them during 1991–2015. Note Slice length is 1 and top 15% are selected per slice

Table 4 Top 15 institutes based on frequency (numbers of publications from 1991)

Based on the strongest co-authorship relationships, institutes can be visually distinguished to be in clusters with at least one institute at the core. The biggest cluster is situated in the middle of the figure including University of Illinois, Purdue University, the University of California at Davis, Georgia Institute of Technology, University of Minnesota, University of British Columbia, the University of California at Irvine, University of Florida, North Carolina University, Rutgers State University among others.

The University of California at Berkley and Delft University are the core institutes of the second major cluster. Although these universities outrank the others in terms of the number of publications, they do not play a central role in making strong connections among other universities. The university of California at Irvine as the core of the third cluster has a high centrality and as a result has strong collaboration with some other institutes.

Some institutes have had strong collaboration with other institutes. To name a few, University of Florida has had some significant collaboration with Federal Highway Administration, Minnesota Department of Transportation, Florida Department of Transportation, Tsinghua University, and CUNY City College; Rutgers State University has had Strong collaboration with New York University, Virginia Tech, New Jersey Institute of Technology, and CUNY City College; The university of British Columbia has had some remarkable collaboration with University of Alberta, Carleton University, Ain Shams University, University of Chili, Bucknell University, City University of Hong Kong, and CUNY City College. On the contrary, some universities such as the University of Sydney, University of Leeds, Pennsylvania State University and the University of Waterloo, despite the significant number of publications, have no strong relationship with other institutes. A number of universities such as Monash University, Queensland University of Technology, Tongji University, Technical University of Denmark, and University of New South Wales have surged in the number of publications in recent years.

Same as the previous definition we provided for keyword burst, burst detection of institutes indicates the speed with which the numbers of publication of institutes are taken up. This assists us finding the institutes whose publications have increased abruptly over time. Figure 11 illustrate the top 40 institutes with strongest citation bursts. The top ranked items by bursts are the University of California at Berkley and the University of Texas at Austin with the bursts strength of 19.73 and 16, respectively. In term of recently emerging institutes with a burst in their number of publications, Tongi University, KTH Royal Institute of Technology, McGill University, Technical University of Denmark and Virginia Center for Transportation Innovation and Research, in an order, have the first through fifth ranks among others.

Fig. 11
figure 11

A summary list of institutes with burstness

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Najmi, A., Rashidi, T.H., Abbasi, A. et al. Reviewing the transport domain: an evolutionary bibliometrics and network analysis. Scientometrics 110, 843–865 (2017).

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  • Transport
  • Bibliometric
  • Scientific visualization