Hostname: page-component-5b777bbd6c-f9nfp Total loading time: 0 Render date: 2025-06-18T22:11:38.968Z Has data issue: false hasContentIssue false

Ship domain-based traffic capacity of Singapore Strait

Published online by Cambridge University Press:  28 May 2025

Zhaoyang Lu
Affiliation:
School of Economics and Management, Southwest Jiaotong University, Chengdu, PR China
Liujiang Kang*
Affiliation:
Key Laboratory of Transport Industry of Comprehensive Transportation Theory, Ministry of Transport, Beijing Jiaotong University, Beijing, PR China
*
Corresponding author: Liujiang Kang; Email: kanglj@bjtu.edu.cn

Abstract

The Singapore Strait, as one of the busiest shipping waterways in the world, contains two chokepoints of the Straits of Malacca and Singapore. With an increasing number of large-sized ships passing through the Singapore Strait in recent years, its traffic capacity has undoubtedly been affected significantly. Therefore, this study aims to assess the traffic capacity of the Singapore Strait under various mixed vessel compositions including different vessel types, vessel sizes and traffic volumes. A ship domain-based method for the estimation of the strait capacity and its variance is derived by using the minimum distance to collision among various vessel types. Then, based on the Automatic Identification System data, the strait capacity and its variances are quantitatively estimated for the two chokepoints of this waterway. Our results confirm that the strait capacity is decreasing with an increasing proportion of large-sized ships. It is also found that this traffic capacity is directly affected by the width of the strait, the size, the composition and the speed of the ships.

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Coldwell, T. G. (1983). Marine traffic behaviour in restricted waters. The Journal of Navigation, 36, 430444.10.1017/S0373463300039783CrossRefGoogle Scholar
Davis, P., Dove, M. and Stockel, C. (1980). A computer simulation of marine traffic using domains and arenas. The Journal of Navigation, 33, 215222.10.1017/S0373463300035220CrossRefGoogle Scholar
Dinh, G. H. and Im, N. K. (2016). The combination of analytical and statistical method to define polygonal ship domain and reflect human experiences in estimating dangerous area. International Journal of e-Navigation and Maritime Economy, 4, 97108.10.1016/j.enavi.2016.06.009CrossRefGoogle Scholar
Endrina, N., Rasero, J. C. and Konovessis, D. (2018). Risk analysis for RoPax vessels: A case of study for the Strait of Gibraltar. Ocean Engineering, 151, 141151.10.1016/j.oceaneng.2018.01.038CrossRefGoogle Scholar
Fan, H. S. L. and Cao, J. M. (2000). Sea space capacity and operation strategy analysis system. Transportation Planning and Technology, 24, 4963.10.1080/03081060008717660CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24, 543552.10.1017/S0373463300022384CrossRefGoogle Scholar
Gao, Y., Luo, M. and Zou, G. (2016). Forecasting with model selection or model averaging: A case study for monthly container port throughput. Transportmetrica A: Transport Science, 12, 366384.10.1080/23249935.2015.1137652CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28, 328344.10.1017/S0373463300041230CrossRefGoogle Scholar
Hui, E. C. M., Ng, M. H., Xu, J. J. and Yip, T. L. (2010). The cargo throughput response to factor cost differentials–an analysis for the port of Hong Kong. Transportmetrica, 6(4), 235248.10.1080/18128600903194427CrossRefGoogle Scholar
Ince, A. and Topuz, E. (2004). Modelling and simulation for safe and efficient navigation in narrow waterways. The Journal of Navigation, 57, 5371.10.1017/S0373463303002510CrossRefGoogle Scholar
Kadarsa, E., Lubis, H. R. S., Sjafruddin, A. and Frazila, R. B. (2017). Fairway traffic capacity in Indonesia. Procedia Engineering, 171, 14431453.10.1016/j.proeng.2017.01.466CrossRefGoogle Scholar
Kang, L., Lu, Z., Meng, Q., Gao, G. and Wang, F. (2019a). Maritime simulator based determination of minimum DCPA and TCPA in head-on ship-to-ship collision avoidance in confined waters. Transportmetrica A: Transport Science, 15(2), 11241144.10.1080/23249935.2019.1567617CrossRefGoogle Scholar
Kang, L., Meng, Q. and Liu, Q. (2018). Fundamental diagram of ship traffic in the Singapore Strait. Ocean Engineering, 147, 340354.10.1016/j.oceaneng.2017.10.051CrossRefGoogle Scholar
Kang, L., Meng, Q., Zhou, C. and Gao, S. (2019b). How do ships pass through L-shaped turnings in the Singapore strait? Ocean Engineering, 182, 329342.10.1016/j.oceaneng.2019.04.033CrossRefGoogle Scholar
Kijima, K. and Furukawa, Y. (2003). Automatic collision avoidance system using the concept of blocking area. IFAC Proceedings Volumes, 36, 223228.10.1016/S1474-6670(17)37811-4CrossRefGoogle Scholar
Köse, E., Başar, E., Demirci, E., Güneroǧlu, A. and Erkebay, Ş. (2003). Simulation of marine traffic in Istanbul Strait. Simulation Modelling Practice and Theory, 11, 597608.10.1016/j.simpat.2003.10.001CrossRefGoogle Scholar
Lam, J. S. L. (2016). Strategy of a Transhipment Hub: The Case of Port of Singapore. Palgrave Macmillan UK.Google Scholar
Li, Q. and Lam, J. S. L. (2017). Conflict resolution for enhancing shipping safety and improving navigational traffic within a seaport: Vessel arrival scheduling. Transportmetrica A: Transport Science, 13, 727741.10.1080/23249935.2017.1326068CrossRefGoogle Scholar
Lu, Z., Kang, L., Gao, G. and Meng, Q. (2018). Determination of minimum distance to obstacle avoidance in the Singapore Strait. Transportation Research Record, 2672, 7380.10.1177/0361198118794056CrossRefGoogle Scholar
Luo, M., Liu, L. and Gao, F. (2012). Post-entry container port capacity expansion. Transportation Research Part B: Methodological, 46, 120138.10.1016/j.trb.2011.09.001CrossRefGoogle Scholar
Maritime and Port Authority of Singapore. (2019). Soms Study: Preliminary Findings Show Capacity to Handle Traffic Growth in the Singapore Strait. http://www.nas.gov.sg/archivesonline/speeches/view-html?filename=20091104006.htm. Accessed December 2019.Google Scholar
Namgung, H. and Kim, J. S. (2021). Regional collision risk prediction system at a collision area considering spatial pattern. Journal of Marine Science and Engineering, 9(12), 1365.10.3390/jmse9121365CrossRefGoogle Scholar
Qu, X. and Meng, Q. (2012). The economic importance of the Straits of Malacca and Singapore: An extreme-scenario analysis. Transportation Research Part E: Logistics and Transportation Review, 48, 258265.10.1016/j.tre.2011.08.005CrossRefGoogle Scholar
Qu, X., Meng, Q. and Suyi, L. (2011). Ship collision risk assessment for the Singapore Strait. Accident Analysis & Prevention, 43, 20302036.10.1016/j.aap.2011.05.022CrossRefGoogle ScholarPubMed
Rusli, M. (2020). Navigational hazards in international maritime chokepoints: A study of the Straits of Malacca and Singapore. Journal of International Studies, 8, 4775.Google Scholar
Shipfinder. (2019). The Live Marine Traffic Tracking App. http://www.shipfinder.com/. Accessed December 2019.Google Scholar
SIGTTO (2015). Passage Planning Guide – Straits of Malacca and Singapore (SOMS) (PPG-SOMS 2015 Edition). Witherby Seamanship International Ltd.Google Scholar
Śmierzchalski, R. and Michalewicz, Z. (2000). Modeling of a ship trajectory in collision situations at sea by evolutionary algorithm. IEEE Transaction on Evolutionary Computation, 4, 227241.10.1109/4235.873234CrossRefGoogle Scholar
Toyoda, S. and Fujii, Y. (1971). Marine traffic engineering. The Journal of Navigation, 24, 2434.10.1017/S0373463300047755CrossRefGoogle Scholar
Wang, N., Chang, D., Yuan, J., Shi, X. and Bai, X. (2020). How to maintain the safety level with the increasing capacity of the fairway: A case study of the Yangtze Estuary Deepwater Channel. Ocean Engineering, 216, 108122.10.1016/j.oceaneng.2020.108122CrossRefGoogle Scholar
Weng, J., Liao, S., Wu, B. and Yang, D. (2020). Exploring effects of ship traffic characteristics and environmental conditions on ship collision frequency. Maritime Policy & Management, 47, 523543.10.1080/03088839.2020.1721584CrossRefGoogle Scholar
Zhang, L. and Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130.10.1016/j.oceaneng.2019.106130CrossRefGoogle Scholar
Zhang, L., Meng, Q. and Fwa, T. F. (2019). Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters. Transportation Research Part E: Logistics and Transportation Review, 129, 287304.10.1016/j.tre.2017.07.011CrossRefGoogle Scholar
Zhang, L., Meng, Q., Xiao, Z. and Fu, X. (2018). A novel ship trajectory reconstruction approach using AIS data. Ocean Engineering, 159, 165174.10.1016/j.oceaneng.2018.03.085CrossRefGoogle Scholar
Zhang, S., Pedersen, P. T. and Villavicencio, R. (2019). Probability of Ship Collision and Grounding. Butterworth-Heinemann. 10.1016/B978-0-12-815022-1.00001-3CrossRefGoogle Scholar