Research Article
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Year 2023, Volume: 12 Issue: 1, 93 - 103, 22.03.2023
https://doi.org/10.33714/masteb.1224160

Abstract

References

  • Akyuz, E. (2017). A marine accident analysing model to evaluate potential operational causes in cargo ships. Safety Science, 92, 17–25. https://doi.org/10.1016/j.ssci.2016.09.010
  • Awal, Z. I., & Hasegawa, K. (2017). A study on accident theories and application to maritime accidents. Procedia Engineering, 194, 298–306. https://doi.org/10.1016/j.proeng.2017.08.149
  • Batalden, B. M., & Sydnes, A. K. (2014). Maritime safety and the ISM code: A study of investigated casualties and incidents. WMU Journal of Maritime Affairs, 13(1), 3–25. https://doi.org/10.1007/s13437-013-0051-8
  • Bolbot, V., Kulkarni, K., Brunou, P., Banda, O. V., & Musharraf, M. (2022). Developments and research directions in maritime cybersecurity: A systematic literature review and bibliometric analysis. International Journal of Critical Infrastructure Protection, 39, 100571. https://doi.org/10.1016/j.ijcip.2022.100571
  • Büber, M., & Köseoğlu, B. (2022). A bibliometric review and science mapping research of oil spill response. Marine Science and Technology Bulletin, 11(1), 123-134. https://doi.org/10.33714/masteb.1081670
  • Bye, R. J., & Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering and System Safety, 176, 174–186. https://doi.org/10.1016/j.ress.2018.03.033
  • Chauvin, C., Lardjane, S., Morel, G., Clostermann, J. P., & Langard, B. (2013). Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS. Accident Analysis and Prevention, 59, 26–37. https://doi.org/10.1016/j.aap.2013.05.006
  • Chen, D., Liu, Z., Luo, Z., Webber, M., & Chen, J. (2016). Bibliometric and visualized analysis of emergy research. Ecological Engineering, 90, 285–293. https://doi.org/10.1016/j.ecoleng.2016.01.026
  • Chen, S. T., Wall, A., Davies, P., Yang, Z., Wang, J., & Chou, Y. H. (2013). A human and organisational factors (HOFs) analysis method for marine casualties using HFACS-Maritime Accidents (HFACS-MA). Safety Science, 60, 105–114. https://doi.org/10.1016/j.ssci.2013.06.009
  • Dominguez-Péry, C., Vuddaraju, L. N. R., Corbett-Etchevers, I., & Tassabehji, R. (2021). Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda. Journal of Shipping and Trade, 6(1), 20. https://doi.org/10.1186/s41072-021-00098-y
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Eleftheria, E., Apostolos, P., & Markos, V. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85, 282–292. https://doi.org/10.1016/j.ssci.2016.02.001
  • Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
  • EMSA. (2022). Annual overview of marine casualties and incidents. European Maritime Safety Agency. Retrieved on December 25, 2022, from https://www.emsa.europa.eu/newsroom/latest-news/download/7362/4867/23.html
  • Fan, S., Blanco-Davis, E., Yang, Z., Zhang, J., & Yan, X. (2020a). Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network. Reliability Engineering and System Safety, 203, 107070. https://doi.org/10.1016/j.ress.2020.107070
  • Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z., & Yan, X. (2020b). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS. Ocean Engineering, 210, 107544. https://doi.org/10.1016/j.oceaneng.2020.107544
  • Fowler, T. G., & Sørgård, E. (2000). Modeling ship transportation risk. Risk Analysis, 20(2), 225–244. https://doi.org/10.1111/0272-4332.202022
  • Fu, S., Goerlandt, F., & Xi, Y. (2021). Arctic shipping risk management: A bibliometric analysis and a systematic review of risk influencing factors of navigational accidents. Safety Science, 139, 105254. https://doi.org/10.1016/j.ssci.2021.105254
  • Gil, M., Wróbel, K., Montewka, J., & Goerlandt, F. (2020). A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention. Safety Science, 128, 104717. https://doi.org/10.1016/j.ssci.2020.104717
  • Graziano, A., Teixeira, A. P., & Guedes Soares, C. (2016). Classification of human errors in grounding and collision accidents using the TRACEr taxonomy. Safety Science, 86, 245–257. https://doi.org/10.1016/j.ssci.2016.02.026
  • Hänninen, M., & Kujala, P. (2012a). Influences of variables on ship collision probability in a Bayesian belief network model. Reliability Engineering and System Safety, 102, 27–40. https://doi.org/10.1016/j.ress.2012.02.008
  • Harati-Mokhtari, A., Wall, A., Brooks, P., & Wang, J. (2007). Automatic identification system (AIS): Data reliability and human error implications. Journal of Navigation, 60(3), 373–389. https://doi.org/10.1017/S0373463307004298
  • Hetherington, C., Flin, R., & Mearns, K. (2006). Safety in shipping: The human element. Journal of Safety Research, 37(4), 401–411. https://doi.org/10.1016/j.jsr.2006.04.007
  • Kececi, T., & Arslan, O. (2017). SHARE technique: A novel approach to root cause analysis of ship accidents. Safety Science, 96, 1–21. https://doi.org/10.1016/j.ssci.2017.03.002
  • Kołakowski, P., Gil, M., Wróbel, K., & Ho, Y. S. (2022). State of play in technology and legal framework of alternative marine fuels and renewable energy systems: A bibliometric analysis. Maritime Policy and Management, 49(2), 236–260. https://doi.org/10.1080/03088839.2021.1969460
  • Kulkarni, K., Goerlandt, F., Li, J., Banda, O. V., & Kujala, P. (2020). Preventing shipping accidents: Past, present, and future of waterway risk management with Baltic Sea focus. Safety Science, 129, 104798. https://doi.org/10.1016/j.ssci.2020.104798
  • Kum, S., & Sahin, B. (2015). A root cause analysis for Arctic Marine accidents from 1993 to 2011. Safety Science, 74, 206–220. https://doi.org/10.1016/j.ssci.2014.12.010
  • Lau, Y.-Y., Ducruet, C., Ng, A. K. Y., & Fu, X. (2017). Across the waves: a bibliometric analysis of container shipping research since the 1960s. Maritime Policy and Management, 44(6), 667–684. https://doi.org/10.1080/03088839.2017.1311425
  • Li, J., Jovanovic, A., Klimek, P., & Guo, X. (2015). Bibliometric analysis of fracking scientific literature. Scientometrics, 105(2), 1273–1284. https://doi.org/10.1007/s11192-015-1739-7
  • Li, K., Rollins, J., & Yan, E. (2018). Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20. https://doi.org/10.1007/s11192-017-2622-5
  • Luo, M., & Shin, S-H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis and Prevention, 123, 448–460. https://doi.org/10.1016/j.aap.2016.04.010
  • Mao, N., Wang, M. H., & Ho, Y. S. (2010). A bibliometric study of the trend in articles related to risk assessment published in science citation index. Human and Ecological Risk Assessment, 16(4), 801–824. https://doi.org/10.1080/10807039.2010.501248
  • Md Khudzari, J., Kurian, J., Tartakovsky, B., & Raghavan, G. S. V. (2018). Bibliometric analysis of global research trends on microbial fuel cells using Scopus database. Biochemical Engineering Journal, 136, 51–60. https://doi.org/10.1016/j.bej.2018.05.002
  • Meyers, S. D., Azevedo, L., & Luther, M. E. (2021). A Scopus-based bibliometric study of maritime research involving the Automatic Identification System. Transportation Research Interdisciplinary Perspectives, 10, 100387. https://doi.org/10.1016/j.trip.2021.100387
  • Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K., & Kujala, P. (2014). A framework for risk assessment for maritime transportation systems - A case study for open sea collisions involving RoPax vessels. Reliability Engineering and System Safety, 124, 142–157. https://doi.org/10.1016/j.ress.2013.11.014
  • Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions. Maritime Policy and Management, 47(5), 577–597. https://doi.org/10.1080/03088839.2020.1788731
  • Qiao, W., Liu, Y., Ma, X., & Liu, Y. (2020). A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS. Ocean Engineering, 197, 106892. https://doi.org/10.1016/j.oceaneng.2019.106892
  • Schröder-Hinrichs, J. U. (2010). Human and organizational factors in the maritime world — Are we keeping up to speed?. WMU Journal of Maritime Affairs, 9, 1–3. https://doi.org/10.1007/BF03195162
  • Shi, X., Zhuang, H., & Xu, D. (2021). Structured survey of human factor-related maritime accident research. Ocean Engineering, 237, 109561. https://doi.org/10.1016/j.oceaneng.2021.109561
  • Soner, O., Asan, U., & Celik, M. (2015). Use of HFACS-FCM in fire prevention modelling on board ships. Safety Science, 77, 25–41. https://doi.org/10.1016/j.ssci.2015.03.007
  • Uğurlu, Ö., Yıldız, S., Loughney, S., & Wang, J. (2018). Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV). Ocean Engineering, 161, 47–61. https://doi.org/10.1016/j.oceaneng.2018.04.086
  • Wong, S. L., Nyakuma, B. B., Wong, K. Y., Lee, C. T., Lee, T. H., & Lee, C. H. (2020). Microplastics and nanoplastics in global food webs: A bibliometric analysis (2009–2019). Marine Pollution Bulletin, 158, 111432. https://doi.org/10.1016/j.marpolbul.2020.111432
  • Wróbel, K., Montewka, J., & Kujala, P. (2017). Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliability Engineering and System Safety, 165, 155–169. https://doi.org/10.1016/j.ress.2017.03.029
  • Wróbel, K. (2021). Searching for the origins of the myth: 80% human error impact on maritime safety. Reliability Engineering and System Safety, 216, 107942. https://doi.org/10.1016/j.ress.2021.107942
  • Yıldırım, U., Başar, E., & Uğurlu, Ö. (2019). Assessment of collisions and grounding accidents with human factors analysis and classification system (HFACS) and statistical methods. Safety Science, 119, 412–425. https://doi.org/10.1016/j.ssci.2017.09.022
  • Zhang, W., Goerlandt, F., Kujala, P., & Wang, Y. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124, 141–156. https://doi.org/10.1016/j.oceaneng.2016.07.059
  • Zou, X., Yue, W. L., & Vu, H. le. (2018). Visualization and analysis of mapping knowledge domain of road safety studies. Accident Analysis and Prevention, 118, 131–145. https://doi.org/10.1016/j.aap.2018.06.010

The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents

Year 2023, Volume: 12 Issue: 1, 93 - 103, 22.03.2023
https://doi.org/10.33714/masteb.1224160

Abstract

The purpose of the study was to assess the output of research on maritime accidents and citations from 2000 to 2022 through a bibliometric analysis. Utilizing the visualization and mapping program VOSviewer 1.6.18, the relevant data was extracted from the Web of Science (WoS) database and analyzed. The findings indicated important study fields, country contributions, productive journals, as well as the most cited authors’ articles. The primary findings were as follows: The most influential journal was Safety Science. One of the most common topics of study for maritime accidents was the human factor. The most productive country was the People’s Republic of China. The findings of the study can assist researchers in conducting their studies more effectively by providing information about the journals they may use, the authors who contributed to it, current research trends, countries, and keywords.

References

  • Akyuz, E. (2017). A marine accident analysing model to evaluate potential operational causes in cargo ships. Safety Science, 92, 17–25. https://doi.org/10.1016/j.ssci.2016.09.010
  • Awal, Z. I., & Hasegawa, K. (2017). A study on accident theories and application to maritime accidents. Procedia Engineering, 194, 298–306. https://doi.org/10.1016/j.proeng.2017.08.149
  • Batalden, B. M., & Sydnes, A. K. (2014). Maritime safety and the ISM code: A study of investigated casualties and incidents. WMU Journal of Maritime Affairs, 13(1), 3–25. https://doi.org/10.1007/s13437-013-0051-8
  • Bolbot, V., Kulkarni, K., Brunou, P., Banda, O. V., & Musharraf, M. (2022). Developments and research directions in maritime cybersecurity: A systematic literature review and bibliometric analysis. International Journal of Critical Infrastructure Protection, 39, 100571. https://doi.org/10.1016/j.ijcip.2022.100571
  • Büber, M., & Köseoğlu, B. (2022). A bibliometric review and science mapping research of oil spill response. Marine Science and Technology Bulletin, 11(1), 123-134. https://doi.org/10.33714/masteb.1081670
  • Bye, R. J., & Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering and System Safety, 176, 174–186. https://doi.org/10.1016/j.ress.2018.03.033
  • Chauvin, C., Lardjane, S., Morel, G., Clostermann, J. P., & Langard, B. (2013). Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS. Accident Analysis and Prevention, 59, 26–37. https://doi.org/10.1016/j.aap.2013.05.006
  • Chen, D., Liu, Z., Luo, Z., Webber, M., & Chen, J. (2016). Bibliometric and visualized analysis of emergy research. Ecological Engineering, 90, 285–293. https://doi.org/10.1016/j.ecoleng.2016.01.026
  • Chen, S. T., Wall, A., Davies, P., Yang, Z., Wang, J., & Chou, Y. H. (2013). A human and organisational factors (HOFs) analysis method for marine casualties using HFACS-Maritime Accidents (HFACS-MA). Safety Science, 60, 105–114. https://doi.org/10.1016/j.ssci.2013.06.009
  • Dominguez-Péry, C., Vuddaraju, L. N. R., Corbett-Etchevers, I., & Tassabehji, R. (2021). Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda. Journal of Shipping and Trade, 6(1), 20. https://doi.org/10.1186/s41072-021-00098-y
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  • Eleftheria, E., Apostolos, P., & Markos, V. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85, 282–292. https://doi.org/10.1016/j.ssci.2016.02.001
  • Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
  • EMSA. (2022). Annual overview of marine casualties and incidents. European Maritime Safety Agency. Retrieved on December 25, 2022, from https://www.emsa.europa.eu/newsroom/latest-news/download/7362/4867/23.html
  • Fan, S., Blanco-Davis, E., Yang, Z., Zhang, J., & Yan, X. (2020a). Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network. Reliability Engineering and System Safety, 203, 107070. https://doi.org/10.1016/j.ress.2020.107070
  • Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z., & Yan, X. (2020b). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS. Ocean Engineering, 210, 107544. https://doi.org/10.1016/j.oceaneng.2020.107544
  • Fowler, T. G., & Sørgård, E. (2000). Modeling ship transportation risk. Risk Analysis, 20(2), 225–244. https://doi.org/10.1111/0272-4332.202022
  • Fu, S., Goerlandt, F., & Xi, Y. (2021). Arctic shipping risk management: A bibliometric analysis and a systematic review of risk influencing factors of navigational accidents. Safety Science, 139, 105254. https://doi.org/10.1016/j.ssci.2021.105254
  • Gil, M., Wróbel, K., Montewka, J., & Goerlandt, F. (2020). A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention. Safety Science, 128, 104717. https://doi.org/10.1016/j.ssci.2020.104717
  • Graziano, A., Teixeira, A. P., & Guedes Soares, C. (2016). Classification of human errors in grounding and collision accidents using the TRACEr taxonomy. Safety Science, 86, 245–257. https://doi.org/10.1016/j.ssci.2016.02.026
  • Hänninen, M., & Kujala, P. (2012a). Influences of variables on ship collision probability in a Bayesian belief network model. Reliability Engineering and System Safety, 102, 27–40. https://doi.org/10.1016/j.ress.2012.02.008
  • Harati-Mokhtari, A., Wall, A., Brooks, P., & Wang, J. (2007). Automatic identification system (AIS): Data reliability and human error implications. Journal of Navigation, 60(3), 373–389. https://doi.org/10.1017/S0373463307004298
  • Hetherington, C., Flin, R., & Mearns, K. (2006). Safety in shipping: The human element. Journal of Safety Research, 37(4), 401–411. https://doi.org/10.1016/j.jsr.2006.04.007
  • Kececi, T., & Arslan, O. (2017). SHARE technique: A novel approach to root cause analysis of ship accidents. Safety Science, 96, 1–21. https://doi.org/10.1016/j.ssci.2017.03.002
  • Kołakowski, P., Gil, M., Wróbel, K., & Ho, Y. S. (2022). State of play in technology and legal framework of alternative marine fuels and renewable energy systems: A bibliometric analysis. Maritime Policy and Management, 49(2), 236–260. https://doi.org/10.1080/03088839.2021.1969460
  • Kulkarni, K., Goerlandt, F., Li, J., Banda, O. V., & Kujala, P. (2020). Preventing shipping accidents: Past, present, and future of waterway risk management with Baltic Sea focus. Safety Science, 129, 104798. https://doi.org/10.1016/j.ssci.2020.104798
  • Kum, S., & Sahin, B. (2015). A root cause analysis for Arctic Marine accidents from 1993 to 2011. Safety Science, 74, 206–220. https://doi.org/10.1016/j.ssci.2014.12.010
  • Lau, Y.-Y., Ducruet, C., Ng, A. K. Y., & Fu, X. (2017). Across the waves: a bibliometric analysis of container shipping research since the 1960s. Maritime Policy and Management, 44(6), 667–684. https://doi.org/10.1080/03088839.2017.1311425
  • Li, J., Jovanovic, A., Klimek, P., & Guo, X. (2015). Bibliometric analysis of fracking scientific literature. Scientometrics, 105(2), 1273–1284. https://doi.org/10.1007/s11192-015-1739-7
  • Li, K., Rollins, J., & Yan, E. (2018). Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20. https://doi.org/10.1007/s11192-017-2622-5
  • Luo, M., & Shin, S-H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis and Prevention, 123, 448–460. https://doi.org/10.1016/j.aap.2016.04.010
  • Mao, N., Wang, M. H., & Ho, Y. S. (2010). A bibliometric study of the trend in articles related to risk assessment published in science citation index. Human and Ecological Risk Assessment, 16(4), 801–824. https://doi.org/10.1080/10807039.2010.501248
  • Md Khudzari, J., Kurian, J., Tartakovsky, B., & Raghavan, G. S. V. (2018). Bibliometric analysis of global research trends on microbial fuel cells using Scopus database. Biochemical Engineering Journal, 136, 51–60. https://doi.org/10.1016/j.bej.2018.05.002
  • Meyers, S. D., Azevedo, L., & Luther, M. E. (2021). A Scopus-based bibliometric study of maritime research involving the Automatic Identification System. Transportation Research Interdisciplinary Perspectives, 10, 100387. https://doi.org/10.1016/j.trip.2021.100387
  • Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K., & Kujala, P. (2014). A framework for risk assessment for maritime transportation systems - A case study for open sea collisions involving RoPax vessels. Reliability Engineering and System Safety, 124, 142–157. https://doi.org/10.1016/j.ress.2013.11.014
  • Munim, Z. H., Dushenko, M., Jimenez, V. J., Shakil, M. H., & Imset, M. (2020). Big data and artificial intelligence in the maritime industry: A bibliometric review and future research directions. Maritime Policy and Management, 47(5), 577–597. https://doi.org/10.1080/03088839.2020.1788731
  • Qiao, W., Liu, Y., Ma, X., & Liu, Y. (2020). A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS. Ocean Engineering, 197, 106892. https://doi.org/10.1016/j.oceaneng.2019.106892
  • Schröder-Hinrichs, J. U. (2010). Human and organizational factors in the maritime world — Are we keeping up to speed?. WMU Journal of Maritime Affairs, 9, 1–3. https://doi.org/10.1007/BF03195162
  • Shi, X., Zhuang, H., & Xu, D. (2021). Structured survey of human factor-related maritime accident research. Ocean Engineering, 237, 109561. https://doi.org/10.1016/j.oceaneng.2021.109561
  • Soner, O., Asan, U., & Celik, M. (2015). Use of HFACS-FCM in fire prevention modelling on board ships. Safety Science, 77, 25–41. https://doi.org/10.1016/j.ssci.2015.03.007
  • Uğurlu, Ö., Yıldız, S., Loughney, S., & Wang, J. (2018). Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV). Ocean Engineering, 161, 47–61. https://doi.org/10.1016/j.oceaneng.2018.04.086
  • Wong, S. L., Nyakuma, B. B., Wong, K. Y., Lee, C. T., Lee, T. H., & Lee, C. H. (2020). Microplastics and nanoplastics in global food webs: A bibliometric analysis (2009–2019). Marine Pollution Bulletin, 158, 111432. https://doi.org/10.1016/j.marpolbul.2020.111432
  • Wróbel, K., Montewka, J., & Kujala, P. (2017). Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliability Engineering and System Safety, 165, 155–169. https://doi.org/10.1016/j.ress.2017.03.029
  • Wróbel, K. (2021). Searching for the origins of the myth: 80% human error impact on maritime safety. Reliability Engineering and System Safety, 216, 107942. https://doi.org/10.1016/j.ress.2021.107942
  • Yıldırım, U., Başar, E., & Uğurlu, Ö. (2019). Assessment of collisions and grounding accidents with human factors analysis and classification system (HFACS) and statistical methods. Safety Science, 119, 412–425. https://doi.org/10.1016/j.ssci.2017.09.022
  • Zhang, W., Goerlandt, F., Kujala, P., & Wang, Y. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124, 141–156. https://doi.org/10.1016/j.oceaneng.2016.07.059
  • Zou, X., Yue, W. L., & Vu, H. le. (2018). Visualization and analysis of mapping knowledge domain of road safety studies. Accident Analysis and Prevention, 118, 131–145. https://doi.org/10.1016/j.aap.2018.06.010
There are 47 citations in total.

Details

Primary Language English
Subjects Maritime Engineering (Other)
Journal Section Research Article
Authors

Ayyüce Yurt 0000-0002-8002-0858

Cenk Şakar 0000-0001-5821-6312

Publication Date March 22, 2023
Submission Date December 25, 2022
Acceptance Date March 2, 2023
Published in Issue Year 2023 Volume: 12 Issue: 1

Cite

APA Yurt, A., & Şakar, C. (2023). The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents. Marine Science and Technology Bulletin, 12(1), 93-103. https://doi.org/10.33714/masteb.1224160
AMA Yurt A, Şakar C. The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents. Mar. Sci. Tech. Bull. March 2023;12(1):93-103. doi:10.33714/masteb.1224160
Chicago Yurt, Ayyüce, and Cenk Şakar. “The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents”. Marine Science and Technology Bulletin 12, no. 1 (March 2023): 93-103. https://doi.org/10.33714/masteb.1224160.
EndNote Yurt A, Şakar C (March 1, 2023) The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents. Marine Science and Technology Bulletin 12 1 93–103.
IEEE A. Yurt and C. Şakar, “The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents”, Mar. Sci. Tech. Bull., vol. 12, no. 1, pp. 93–103, 2023, doi: 10.33714/masteb.1224160.
ISNAD Yurt, Ayyüce - Şakar, Cenk. “The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents”. Marine Science and Technology Bulletin 12/1 (March 2023), 93-103. https://doi.org/10.33714/masteb.1224160.
JAMA Yurt A, Şakar C. The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents. Mar. Sci. Tech. Bull. 2023;12:93–103.
MLA Yurt, Ayyüce and Cenk Şakar. “The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents”. Marine Science and Technology Bulletin, vol. 12, no. 1, 2023, pp. 93-103, doi:10.33714/masteb.1224160.
Vancouver Yurt A, Şakar C. The Bibliometric Analysis and Visualization Mapping of Research on Maritime Accidents. Mar. Sci. Tech. Bull. 2023;12(1):93-103.

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