Abstract
The automatic identification system (AIS) enables authorities, shipping companies and researchers all over the world using ever better computer technologies to understand and track vessel movements. This publication focuses on analysing vessels’ waiting times for berth at anchoring places near ports using the example of the port of Rotterdam, Europe’s biggest port. The objective is to define clearly the concept of waiting, i.e. when a vessel waits and when not, and to investigate the amount of waiting vessels and the respective waiting times during a time span of more than two years, using solely AIS data. The indicated anchoring zones in front of the port of Rotterdam, where vessels wait, are clearly detected by visualizing the analysed data. The results of the conducted AIS data analysis show significant differences in waiting times between different vessel types, as well as a correlation between the number of waiting vessels and the average waiting time. The in detail described data pre-processing and statistical analysis are extendable and applicable to other regions and ports all over the world. Additionally, the presented data pre-processing approach is an optimal basis analysis of current waiting conditions and for applying machine learning to AIS data in order to predict future waiting times.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersson, P., Ivehammar, P.: Cost benefit analysis of dynamic route planning at sea. Transp. Res. Procedia 14, 193–202 (2016). https://doi.org/10.1016/j.trpro.2016.05.055
Andersson, P., Ivehammar, P.: Green approaches at sea – the benefits of adjusting speed instead of anchoring. Transp. Res. Part D: Transp. Environ. 51, 240–249 (2017). https://doi.org/10.1016/j.trd.2017.01.010
Coomber, F.G., D’Incà, M., Rosso, M., et al.: Description of the vessel traffic within the north Pelagos Sanctuary: inputs for marine spatial planning and management implications within an existing international marine protected area. Mar. Policy 69, 102–113 (2016). https://doi.org/10.1016/j.marpol.2016.04.013
Cotteleer, A., Koldenhof, Y.: Ship’s travelling time in approaching the Port of Rotterdam (2013). https://www.marin.nl/publication/shipas-travelling-time-in-approaching-the-port-of-rotterdam. Accessed 03 Jul 2019
Gao, X., Makino, H., Furusho, M.: Analysis of actual situation of waiting ship using AIS data. In: Chung, J.S. (ed.) Conference proceedings of 25th International Ocean and Polar Engineering Conference. Gas Hydrates and Ocean Mining. ISOPE, Cupertino, California, pp. 883–888 (2015)
International Maritime Organization: SOLAS. Consolidated text of the International Convention for the Safety of Life at Sea, 1974, and its protocol of 1988: articles, annexes and certificates; Incorporating All Amendments in Effect from 1 July 2014, 6th. edn., consolidated edition. IMO-publication(IMO), London (2014)
International Maritime Organization (ed.): Revised guidelines for the onboard operational use of shipborne Automatic Identification Systems (AIS). Resolution A 1106(29), pp. 1–17 (2015)
Port of Rotterdam: Fakten & Zahlen. Ein Reichtum an Informationen. Make it happen (2019). https://www.portofrotterdam.com/de/unser-hafen/fakten-und-zahlen-zum-hafen. Accessed 30 Aug 2019
Port Regulator of South Africa: Port Benchmarking Report: SA Terminals 2015/16, pp. 1–34 (2016)
Qu, X., Meng, Q., Suyi, L.: Ship collision risk assessment for the Singapore Strait. Accid. Anal. Prev. 43(6), 2030–2036 (2011). https://doi.org/10.1016/j.aap.2011.05.022
Shelmerdine, R.L.: Teasing out the detail: how our understanding of marine AIS data can better inform industries, developments, and planning. Mar. Policy 54, 17–25 (2015). https://doi.org/10.1016/j.marpol.2014.12.010
Svanberg, M., Santén, V., Hörteborn, A., et al.: AIS in maritime research. Mar. Policy (106) (2019). https://doi.org/10.1016/j.marpol.2019.103520
Watson, R.T., Holm, H., Lind, M.: Green Steaming: a methodology for estimating carbon emissions avoided. In: Proceedings of the 36th International Conference on Information Systems, pp. 1–15 (2015)
Wee, V.: New Port of Rotterdam app cuts waiting time by 20%, reduces CO2 emissions (2018). http://www.seatrade-maritime.com/news/europe/new-port-of-rotterdam-app-cuts-waiting-time-by-20-reduces-co2-emissions.html. Accessed 03 Jul 2019
Xiao, F., Ligteringen, H., van Gulijk, C., et al.: Comparison study on AIS data of ship traffic behavior. Ocean Eng. 95, 84–93 (2015). https://doi.org/10.1016/j.oceaneng.2014.11.020
Zhang, L., Meng, Q., Fwa, T.F.: Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transp. Res. Part E: Logist. Transp. Rev., 287–304 (2017). https://doi.org/10.1016/j.tre.2017.07.011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Franzkeit, J., Pache, H., Jahn, C. (2020). Investigation of Vessel Waiting Times Using AIS Data. In: Freitag, M., Haasis, HD., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. LDIC 2020. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-030-44783-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-44783-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44782-3
Online ISBN: 978-3-030-44783-0
eBook Packages: EngineeringEngineering (R0)