IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v175y2023ics1366554523001576.html
   My bibliography  Save this article

Towards sustainable port management: Data-driven global container ports turnover rate assessment

Author

Listed:
  • Yang, Dong
  • Liao, Shiguan
  • Venus Lun, Y.H
  • Bai, Xiwen

Abstract

Accurate assessment of port turnover rate is essential for port operators and shipping carriers to benchmark and improve their operations. This study proposes a standardized method to estimate the port turnover rate based on satellite data of ocean ships. This method can be generalized to accommodate ports of different geographic and operational characteristics with minimum input and running times. To achieve the research objective, we first construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. Then, two tailor-made algorithms are proposed to estimate the berthing time of ship in a berthing event. Finally, we assess the port turnover rate with aggregate berthing time at a port and its historical port throughput. Assuming that the turnover rate is unchanged in the short term, we can use the estimated turnover to estimate the monthly throughput of global ports. The findings suggest the average Mean Absolute Percentage Error (MAPE) of our estimation is 3.84%. Standardized and high-frequency port statistics are highly valued by the industry but very costly to access. The proposed method makes high-frequency port turnover rate and throughput available for a wide range of users. The statistics and findings will enhance standardization and transparency of port statistics and promote the sustainable development of port industry.

Suggested Citation

  • Yang, Dong & Liao, Shiguan & Venus Lun, Y.H & Bai, Xiwen, 2023. "Towards sustainable port management: Data-driven global container ports turnover rate assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:transe:v:175:y:2023:i:c:s1366554523001576
    DOI: 10.1016/j.tre.2023.103169
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554523001576
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2023.103169?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peng, Peng & Yang, Yu & Lu, Feng & Cheng, Shifen & Mou, Naixia & Yang, Ren, 2018. "Modelling the competitiveness of the ports along the Maritime Silk Road with big data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 852-867.
    2. Dong Yang & Lingxiao Wu & Shuaian Wang & Haiying Jia & Kevin X. Li, 2019. "How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 755-773, November.
    3. Li, Kevin X. & Li, Mengchi & Zhu, Yuhan & Yuen, Kum Fai & Tong, Hao & Zhou, Haoqing, 2023. "Smart port: A bibliometric review and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).
    4. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    5. Feng, Mingxiang & Shaw, Shih-Lung & Peng, Guojun & Fang, Zhixiang, 2020. "Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data," Journal of Transport Geography, Elsevier, vol. 86(C).
    6. Zhong, Huiling & Zhang, Fa & Gu, Yimiao, 2021. "A Stackelberg game based two-stage framework to make decisions of freight rate for container shipping lines in the emerging blockchain-based market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    7. Bruce A. Blonigen & Wesley W. Wilson, 2008. "Port Efficiency and Trade Flows," Review of International Economics, Wiley Blackwell, vol. 16(1), pages 21-36, February.
    8. Cullinane, Kevin & Wang, Teng-Fei, 2006. "Chapter 23 Data Envelopment Analysis (DEA) and Improving Container Port Efficiency," Research in Transportation Economics, Elsevier, vol. 17(1), pages 517-566, January.
    9. Chao, Shih-Liang & Yu, Ming-Miin & Hsieh, Wei-Fan, 2018. "Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 44-57.
    10. Serebrisky, Tomás & Sarriera, Javier Morales & Suárez-Alemán, Ancor & Araya, Gonzalo & Briceño-Garmendía, Cecilia & Schwartz, Jordan, 2016. "Exploring the drivers of port efficiency in Latin America and the Caribbean," Transport Policy, Elsevier, vol. 45(C), pages 31-45.
    11. Sugrue, Dennis & Adriaens, Peter, 2021. "A data fusion approach to predict shipping efficiency for bulk carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    12. Saeedi, Hamid & Behdani, Behzad & Wiegmans, Bart & Zuidwijk, Rob, 2019. "Assessing the technical efficiency of intermodal freight transport chains using a modified network DEA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 66-86.
    13. Bai, Xiwen & Cheng, Liangqi & Yang, Dong & Cai, Ouchen, 2022. "Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data," Journal of Transport Geography, Elsevier, vol. 102(C).
    14. Odeck, James & Schøyen, Halvor, 2020. "Productivity and convergence in Norwegian container seaports: An SFA-based Malmquist productivity index approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 222-239.
    15. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    16. Hong-Oanh Nguyen & Hong-Van Nguyen & Young-Tae Chang & Anthony T. H. Chin & Jose Tongzon, 2016. "Measuring port efficiency using bootstrapped DEA: the case of Vietnamese ports," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(5), pages 644-659, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhao, Shuaiqi & Yang, Hualong & Zheng, Jianfeng & Li, Dechang, 2024. "A two-step approach for deploying heterogeneous vessels and designing reliable schedule in liner shipping services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    2. Zhang, Jing & Yang, Dong & Luo, Meifeng, 2024. "Port efficiency types and perspectives: A literature review," Transport Policy, Elsevier, vol. 156(C), pages 13-24.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Jing & Yang, Dong & Luo, Meifeng, 2024. "Port efficiency types and perspectives: A literature review," Transport Policy, Elsevier, vol. 156(C), pages 13-24.
    2. Metri F. Mdanat & Mohammad Al Hur & Omar M. Bwaliez & Ghazi A. Samawi & Raed Khasawneh, 2024. "Drivers of Port Competitiveness Among Low-, Upper-, and High-Income Countries," Sustainability, MDPI, vol. 16(24), pages 1-19, December.
    3. Li, Yiliang & Bai, Xiwen & Wang, Qi & Ma, Zhongjun, 2022. "A big data approach to cargo type prediction and its implications for oil trade estimation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    4. Gong, Xiaoxing & Wu, Xiaofan & Luo, Meifeng, 2019. "Company performance and environmental efficiency: A case study for shipping enterprises," Transport Policy, Elsevier, vol. 82(C), pages 96-106.
    5. Kerbiriou, Ronan & Serry, Arnaud, 2023. "Estimation and analysis of container handling rates in European ports," Journal of Transport Geography, Elsevier, vol. 108(C).
    6. Tadić, Snežana & Krstić, Mladen & Brnjac, Nikolina, 2019. "Selection of efficient types of inland intermodal terminals," Journal of Transport Geography, Elsevier, vol. 78(C), pages 170-180.
    7. Yan, Ran & Yang, Dong & Wang, Tianyu & Mo, Haoyu & Wang, Shuaian, 2024. "Improving ship energy efficiency: Models, methods, and applications," Applied Energy, Elsevier, vol. 368(C).
    8. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    9. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    10. Yap, Wei Yim & Hsieh, Cheng-Hsien & Lee, Paul Tae-Woo, 2023. "Shipping connectivity data analytics: Implications for maritime policy," Transport Policy, Elsevier, vol. 132(C), pages 112-127.
    11. Chia-Nan Wang & Phi-Hung Nguyen & Thi-Ly Nguyen & Thi-Giang Nguyen & Duc-Thinh Nguyen & Thi-Hoai Tran & Hong-Cham Le & Huong-Thuy Phung, 2022. "A Two-Stage DEA Approach to Measure Operational Efficiency in Vietnam’s Port Industry," Mathematics, MDPI, vol. 10(9), pages 1-21, April.
    12. Zheng, Shiyuan & Jiang, Changmin, 2024. "Consortium blockchain in Shipping: Impacts on industry and social welfare," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    13. Shilin Ye & Xinhua Qi & Yecheng Xu, 2020. "Analyzing the relative efficiency of China’s Yangtze River port system," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(4), pages 640-660, December.
    14. Merkel, Axel & Holmgren, Johan, 2017. "Dredging the depths of knowledge: Efficiency analysis in the maritime port sector," Transport Policy, Elsevier, vol. 60(C), pages 63-74.
    15. Feng, Mingxiang & Shaw, Shih-Lung & Peng, Guojun & Fang, Zhixiang, 2020. "Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data," Journal of Transport Geography, Elsevier, vol. 86(C).
    16. Guo-Ya Gan & Hsuan-Shih Lee & Yu-Jwo Tao & Chang-Shu Tu, 2021. "Selecting Suitable, Green Port Crane Equipment for International Commercial Ports," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    17. Li, Lu & Wan, Yulai & Yang, Dong, 2024. "Do shipping alliances affect freight rates? Evidence from global satellite ship data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    18. Peng, Wenhao & Bai, Xiwen, 2022. "Prospects for improving shipping companies’ profit margins by quantifying operational strategies and market focus approach through AIS data," Transport Policy, Elsevier, vol. 128(C), pages 138-152.
    19. Lorenz Kolley & Nicolas Rückert & Marvin Kastner & Carlos Jahn & Kathrin Fischer, 2023. "Robust berth scheduling using machine learning for vessel arrival time prediction," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 29-69, March.
    20. Martínez-Moya, Julián & Mestre-Alcover, Amparo & Sala-Garrido, Ramón & Furió-Pruñonosa, Salvador, 2024. "Are transhipment ports more efficient in the Mediterranean Sea? Analysing the role of time at ports using DEA metafrontier approach," Journal of Transport Geography, Elsevier, vol. 116(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:175:y:2023:i:c:s1366554523001576. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.