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

Trajectory clustering

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Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

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Commercial Arctic shipping through the Northeast Passage: routes, resources, governance, technology, and infrastructure

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Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters

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Empirical Ship Domain based on AIS Data

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Satellite Communications in the New Space Era: A Survey and Future Challenges

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Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

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Applications of smart technologies in logistics and transport: A review

Chung, S. (2021) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2021.102455 · Cited by 295

Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond

Zhang, X., Fu, X., Xiao, Z., Xu, H., & Qin, Z. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3192574 · Cited by 201

Applications of machine learning methods in port operations – A systematic literature review

Filom, S., Amiri, A. M., & Razavi, S. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102722 · Cited by 220

Anomaly Detection in Maritime AIS Tracks: A Review of Recent Approaches

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Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan, R., Wang, S., & Psaraftis, H. N. (2021) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2021.102489 · Cited by 170

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

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Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review

Yang, Y. Z., Liu, Y., Li, G., Zhang, Z., & Liu, Y. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103426 · Cited by 167

A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis

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A review on risk assessment methods for maritime transport

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A machine learning method for the prediction of ship motion trajectories in real operational conditions

Zhang, M., Kujala, P., Musharraf, M., Zhang, J., & Hirdaris, S. E. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.114905 · Cited by 123

Intelligent Edge-Enabled Efficient Multi-Source Data Fusion for Autonomous Surface Vehicles in Maritime Internet of Things

Liu, R. W., Guo, Y., Nie, J., Hu, Q., Xiong, Z., Yu, H., & Guizani, M. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3158004 · Cited by 127

Assessment of the impact of ship emissions on air quality based on a complete annual emission inventory using AIS data for the port of Naples

Toscano, D., Murena, F., Quaranta, F., & Mocerino, L. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109166 · Cited by 116

GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

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Decarbonization of Maritime Transport: Is There Light at the End of the Tunnel?

Psaraftis, H. N., & Kontovas, C. A. (2020) · Sustainability · doi:10.3390/su13010237 · Cited by 111

An interpretable knowledge-based decision support method for ship collision avoidance using AIS data

Zhang, J., Liu, J., Hirdaris, S. E., Zhang, M., & Tian, W. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108919 · Cited by 110

Arctic shipping risk management: A bibliometric analysis and a systematic review of risk influencing factors of navigational accidents

Fu, S., Goerlandt, F., & Xi, Y. (2021) · Safety Science · doi:10.1016/j.ssci.2021.105254 · Cited by 113

Emerging approaches applied to maritime transport research: past and future

Yan, R., Wang, S., Zhen, L., & Laporte, G. (2021) · Communications in Transportation Research · doi:10.1016/j.commtr.2021.100011 · Cited by 111

A machine learning method for the evaluation of ship grounding risk in real operational conditions

Zhang, M., Kujala, P., & Hirdaris, S. E. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108697 · Cited by 106

A machine learning approach for monitoring ship safety in extreme weather events

Rawson, A., Brito, M. P., Sabeur, Z. A., & Tran-Thanh, L. (2021) · Safety Science · doi:10.1016/j.ssci.2021.105336 · Cited by 99

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

Li, H., Lam, J. S. L., Yang, Z., Liu, J., Liu, R. W., Liang, M., & Li, Y. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103856 · Cited by 99

Data-driven methods for detection of abnormal ship behavior: Progress and trends

Wang, Y., Liu, J., Liu, R. W., Liu, Y., & Zhi, Y. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.113673 · Cited by 93

Impacts of wind and current on ship behavior in ports and waterways: A quantitative analysis based on AIS data

Zhou, Y., Daamen, W., Vellinga, T., & Hoogendoorn, S. P. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107774 · Cited by 89

Improved kinematic interpolation for AIS trajectory reconstruction

Guo, S., Mou, J., Chen, L., & Chen, P. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109256 · Cited by 89

Deep bi-directional information-empowered ship trajectory prediction for maritime autonomous surface ships

Li, H., Xing, W., Jiao, H., Yang, Z., & Li, Y. (2023) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2023.103367 · Cited by 93

Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications

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Analyzing the spatio-temporal correlation between tide and shipping behavior at estuarine port for energy-saving purposes

Shu, Y., Han, B., Song, L., Yan, T., Gan, L., Zhu, Y., & Zheng, C. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123382 · Cited by 82

A deep learning approach for port congestion estimation and prediction

Peng, W., Bai, X., Yang, D., Yuen, K. F., & Wu, J. (2022) · Maritime Policy & Management · doi:10.1080/03088839.2022.2057608 · Cited by 88

AIS data-driven approach to estimate navigable capacity of busy waterways focusing on ships entering and leaving port

Liu, C., Liu, J., Zhou, X. Y., Zhao, Z., Wan, C., & Liu, Z. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.108215 · Cited by 79

A critique of the use of domain analysis for spatial collision risk assessment

Rawson, A., & Brito, M. P. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.108259 · Cited by 76

AISClean: AIS data-driven vessel trajectory reconstruction under uncertain conditions

Liang, M., Su, J., Liu, R. W., & Lam, J. S. L. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117987 · Cited by 76

The impact of an EU maritime emissions trading system on oil trades

Cariou, P., Lindstad, E., & Jia, H. (2021) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2021.102992 · Cited by 74

Robust berth scheduling using machine learning for vessel arrival time prediction

Kolley, L., Rückert, N., Kastner, M., Jahn, C., & Fischer, K. (2022) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-022-09462-x · Cited by 74

Improving ship energy efficiency: Models, methods, and applications

Yan, R., Yang, D., Wang, T., Mo, H., & Wang, S. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123132 · Cited by 74

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Xiao, G., Yang, D., Xu, L., Li, J., & Jiang, Z. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12040624 · Cited by 73

Digitalization in Maritime Transport and Seaports: Bibliometric, Content and Thematic Analysis

Jović, M., Tijan, E., Brčić, D., & Pucihar, A. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10040486 · Cited by 72

Multi-scale collision risk estimation for maritime traffic in complex port waters

Xin, X., Liu, K., Loughney, S., Wang, J., Li, H., Ekere, N. N., & Yang, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109554 · Cited by 72

A semi-supervised deep learning approach for vessel trajectory classification based on AIS data

Duan, H., Ma, F., Miao, L., & Zhang, C. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2021.106015 · Cited by 72

Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data

Feng, M., Shaw, S., Peng, G., & Fang, Z. (2020) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2020.102741 · Cited by 66

Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters

Xin, X., Liu, K., Loughney, S., Wang, J., & Yang, Z. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108936 · Cited by 65

Testbed Scenario Design Exploiting Traffic Big Data for Autonomous Ship Trials Under Multiple Conflicts With Collision/Grounding Risks and Spatio-Temporal Dependencies

Bakdi, A., Glad, I. K., & Vanem, E. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3095547 · Cited by 61

Structure and resilience changes of global liquefied natural gas shipping network during the Russia–Ukraine conflict

Xiao, R., Xiao, T., Zhao, P., Zhang, M., Ma, T., & Qiu, S. (2024) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2024.107102 · Cited by 61

Review on the protective technologies of bridge against vessel collision

Zhang, W., Pan, J. P., Calderón-Sánchez, J., Li, X. B., & Xu, M. C. (2024) · Thin-Walled Structures · doi:10.1016/j.tws.2024.112013 · Cited by 63

Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics

Gil, M., Kozioł, P., Wróbel, K., & Montewka, J. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108311 · Cited by 59

Loitering behavior detection and classification of vessel movements based on trajectory shape and Convolutional Neural Networks

Zhang, Z., Huang, L., Peng, X., Wen, Y., & Song, L. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111852 · Cited by 59

Hazard identification and risk analysis of maritime autonomous surface ships: A systematic review and future directions

Tao, J., Liu, Z., Wang, X., Cao, Y., Zhang, M., Loughney, S., Wang, J., & Yang, Z. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.118174 · Cited by 59

Data-driven financial and operational risk management: Empirical evidence from the global tramp shipping industry

Bai, X., Cheng, L., & Iris, Ç. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102617 · Cited by 58

A novel ship trajectory clustering analysis and anomaly detection method based on AIS data

Zhang, C., Liu, S., Guo, M., & Liu, Y. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.116082 · Cited by 58

Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study

Wang, X., Liu, Z., Yan, R., Wang, H., & Zhang, M. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2022.106377 · Cited by 60

Space Weather Effects on Transportation Systems: A Review of Current Understanding and Future Outlook

Xue, D., Wu, L., Xu, T., Wu, C., Wang, Z., & He, Z. (2024) · Space Weather · doi:10.1029/2024sw004055 · Cited by 57

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Guo, S., Mou, J., Chen, L., & Chen, P. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9060609 · Cited by 56

Quantifying the impact of pandemic lockdown policies on global port calls

Bai, X., Xu, M. F., Han, T., & Yang, D. (2022) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2022.08.002 · Cited by 55

Adaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm

Wei, Z., Gao, Y., Zhang, X., Li, X., & Han, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122229 · Cited by 55

MVFFNet: Multi-view feature fusion network for imbalanced ship classification

Liang, M., Zhan, Y., & Liu, R. W. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.07.024 · Cited by 53

Impacts of COVID-19 on Ship Behaviours in Port Area: An AIS Data-Based Pattern Recognition Approach

Wang, C., Li, G., Han, P., Osen, O. L., & Zhang, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3147377 · Cited by 53

Deep learning framework for vessel trajectory prediction using auxiliary tasks and convolutional networks

Shin, Y., Kim, N.-W., Lee, H., In, S. Y., Hansen, M. M., & Yoon, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107936 · Cited by 56

Bi-directional information fusion-driven deep network for ship trajectory prediction in intelligent transportation systems

Li, H., Xing, W., Jiao, H., Yuen, K. F., Gao, R., Li, Y., Matthews, C., & Yang, Z. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103770 · Cited by 56

Multi-stage and multi-topology analysis of ship traffic complexity for probabilistic collision detection

Xin, X., Yang, Z., Liu, K., Zhang, J., & Wu, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118890 · Cited by 52

Port efficiency types and perspectives: A literature review

Zhang, J., Yang, D., & Luo, M. (2024) · Transport Policy · doi:10.1016/j.tranpol.2024.07.014 · Cited by 51

A novel ship trajectory clustering method for Finding Overall and Local Features of Ship Trajectories

Tang, C., Chen, M., Zhao, J., Liu, T., Liu, K., Yan, H., & Xiao, Y. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.110108 · Cited by 51

Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model

Zu, L., Yue, W., Jie, H. J., & Wan, J. (2023) · Electronics · doi:10.3390/electronics12122556 · Cited by 53

Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Xin, X., Liu, K., Loughney, S., Wang, J., Li, H., & Yang, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120825 · Cited by 50

Comparison of deterministic and ensemble weather forecasts on ship sailing speed optimization

Luo, X., Yan, R., & Wang, S. (2023) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2023.103801 · Cited by 48

Can we trust the AIS destination port information for bulk ships?–Implications for shipping policy and practice

Yang, D., Wu, L., & Wang, S. (2021) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2021.102308 · Cited by 49

Digital empowerment for shipping development: a framework for establishing a smart shipping index system

Xiao, Y., Chen, Z., & McNeil, L. (2021) · Maritime Policy & Management · doi:10.1080/03088839.2021.1894364 · Cited by 45

Greenhouse gas impact of digitalizing shipping documents: Blockchain vs. centralized systems

Pu, S., & Lam, J. S. L. (2021) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2021.102942 · Cited by 45

Data-driven framework for extracting global maritime shipping networks by machine learning

Liu, L., Shibasaki, R., Zhang, Y., Kosuge, N., Zhang, M., & Hu, Y. H. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113494 · Cited by 44

Time Management as a Critical Success Factor in the Oil Industry of Basra Governorate: An Accounting Information Systems Study

Hussain, H. N., Alabdullah, T. T. Y., Jamal, K. A. M., & Ahmed, E. R. (2023) · International Journal of Scientific and Management Research · doi:10.37502/ijsmr.2023.6605 · Cited by 44

Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

Guan, Y., Zhang, J., Zhang, X., Li, Z., Meng, J., Liu, G., Bao, M., & Cao, C. (2021) · Remote Sensing · doi:10.3390/rs13101952 · Cited by 46

Choose clean energy or green technology? Empirical evidence from global ships

Bai, X., Hou, Y., & Yang, D. (2021) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2021.102364 · Cited by 43

Towards a Secure and Scalable Maritime Monitoring System Using Blockchain and Low-Cost IoT Technology

Freire, W. P., Melo, W. S., do Nascimento, V. D., Nascimento, P. R. M., & de Sá, A. O. (2022) · Sensors · doi:10.3390/s22134895 · Cited by 43

Abnormal Ship Behavior Detection Based on AIS Data

Shi, Y., Long, C., Yang, X., & Deng, M. (2022) · Applied Sciences · doi:10.3390/app12094635 · Cited by 39

How liner shipping heals schedule disruption: A data-driven framework to uncover the strategic behavior of port-skipping

Zhang, L., Yang, D., Bai, X., & Lai, K. (2023) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2023.103229 · Cited by 39

Unsupervised knowledge discovery framework: From AIS data processing to maritime traffic networks generating

Guo, Z., Qiang, H., Xie, S., & Peng, X. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2024.103924 · Cited by 39

Port 4.0: a conceptual model for smart port digitalization

Behdani, B. (2023) · Transportation research procedia · doi:10.1016/j.trpro.2023.11.154 · Cited by 38

Challenges of maritime human resource management for the transition to shipping digitalization

Theotokas, I., Lagoudis, I. N., & Raftopoulou, K. (2024) · Journal of Shipping and Trade · doi:10.1186/s41072-024-00165-0 · Cited by 69

A machine learning method for the recognition of ship behavior using AIS data

Ma, Q., Lian, S., Zhang, J., Lang, X., Rong, H., Mao, W., & Zhang, M. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119791 · Cited by 38

Intelligent geospatial maritime risk analytics using the Discrete Global Grid System

Rawson, A., Sabeur, Z. A., & Brito, M. P. (2021) · Big Earth Data · doi:10.1080/20964471.2021.1965370 · Cited by 38

A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

Liu, L., Zhang, Y., Hu, Y. H., Wang, Y., Sun, J., & Dong, X. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10030342 · Cited by 38

Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Kang, M. J., Zohoori, S., Hamidi, M., & Wu, X. (2021) · Journal of Ocean Engineering and Science · doi:10.1016/j.joes.2021.10.010 · Cited by 37

Automatic Identification System (AIS) Data Supported Ship Trajectory Prediction and Analysis via a Deep Learning Model

Chen, X., Wei, C., Zhou, G., Wu, H., Wang, Z., & Biancardo, S. A. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10091314 · Cited by 37

Trajectories prediction in multi-ship encounters: Utilizing graph convolutional neural networks with GRU and Self-Attention Mechanism

Zeng, X. R., Gao, M., Zhang, A., Zhu, J., Hu, Y., Chen, P., Chen, S., Dong, T., Zhang, S., & Shi, P. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109679 · Cited by 36

A Data-Driven Iterative Multi-Attribute Clustering Algorithm and Its Application in Port Congestion Estimation

Bai, X., Ma, Z., Hou, Y., Li, Y., & Yang, D. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3286477 · Cited by 35

Maritime shipping ports performance: a systematic literature review

Kishore, L., Pai, Y. P., Ghosh, B. K., & Pakkan, S. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00299-y · Cited by 35

Development of denoising and compression algorithms for AIS-based vessel trajectories

Yan, R., Mo, H., Yang, D., & Wang, S. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111207 · Cited by 35

Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data

Bai, X., Cheng, L., Yang, D., & Cai, O. (2022) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2022.103385 · Cited by 34

Evaluation and prediction of punctuality of vessel arrival at port: a case study of Hong Kong

Chu, Z., Yan, R., & Wang, S. (2023) · Maritime Policy & Management · doi:10.1080/03088839.2023.2217168 · Cited by 35

Identifying influential ships in multi-ship encounter situation complex network based on improved WVoteRank approach

Tong, Y., Zhen, R., Dong, H., & Liu, J. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115192 · Cited by 33

Structural analysis and vulnerability assessment of the European LNG maritime supply chain network (2018–2020)

Mei, Q., Hu, Q., Hu, Y., Yang, Y., Liu, X., Huang, Z., & Wang, P. (2024) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2024.107126 · Cited by 33

Graph deep learning recognition of port ship behavior patterns from a network approach

Xin, R., Pan, J., Yang, F., Yan, X., Ai, B., & Zhang, Q. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117921 · Cited by 33

From ports to routes: Extracting multi-scale shipping networks using massive AIS data

Liu, R. W., Zhou, S., Liang, M., Gao, R., & Wang, H. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.118969 · Cited by 33

Shipping connectivity data analytics: Implications for maritime policy

Yap, W. Y., Hsieh, C., & Lee, P. T. (2022) · Transport Policy · doi:10.1016/j.tranpol.2022.12.018 · Cited by 32

Can maritime big data be applied to shipping industry analysis? Focussing on commodities and vessel sizes of dry bulk carriers

Kanamoto, K., Murong, L., Nakashima, M., & Shibasaki, R. (2020) · Maritime Economics & Logistics · doi:10.1057/s41278-020-00171-6 · Cited by 32

Effects of ship domain shapes on ship collision risk estimates considering collision frequency and severity

Weng, J., Du, J., Shi, K., & Liao, S. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115070 · Cited by 32

Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis

Potamos, G., Stavrou, E., & Stavrou, S. (2024) · Sensors · doi:10.3390/s24113458 · Cited by 31

A Scopus-based bibliometric study of maritime research involving the Automatic Identification System

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