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Methodology for Estimating Waterway Traffic Capacity at Shanghai Estuary of the Yangtze River

Published online by Cambridge University Press:  05 July 2019

Jinxian Weng ,
Shiguan Liao  and
Dong Yang
Jinxian Weng*
Affiliation:
(College of Transport and Communications, Shanghai Maritime University, Shanghai, China 201306)
Shiguan Liao
Affiliation:
(College of Transport and Communications, Shanghai Maritime University, Shanghai, China 201306)
Dong Yang
Affiliation:
(Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China)
*
(E-mail: wjx206@gmail.com)
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Abstract

The objective of this study is to propose a methodology for assessing waterway traffic capacity in the Shanghai estuary of the Yangtze River. To achieve this objective, we first put forward the estimation method which utilises the minimum collision distance taking the dynamic ship domain into consideration. Considering possible effects caused by unknown external factors, the waterway traffic capacity is then represented by a probability distribution. Finally, we quantify the equivalent units of ships with various ship sizes as well as the effects of large-sized ships on the waterway traffic capacity. Results show that a large-sized ship is equivalent to more small-sized ships during the daytime period than at night. In addition, the deployment of large-sized ships could increase the waterway traffic capacity and such an increment highly depends on the increased proportion of large-sized ships in the waterway traffic.

Keywords

AISEstimationUncertaintyShip domain
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 75 - 91
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Bellsolà Olba, X., Daamen, W., Vellinga, T., and Hoogendoorn, S. P. (2017). Network capacity estimation of vessel traffic: an approach for port planning. Journal of Waterway, Port, Coastal, and Ocean Engineering, 143(5), 04017019.Google Scholar
Coldwell, T.G. (1983). Marine traffic behaviour in restricted waters. The Journal of Navigation, 36(3), 430–444.Google Scholar
Davis, P.V., Dove, M.J. and Stockel, C.T. (1980). A computer simulation of marine traffic using domains and arenas. The Journal of Navigation, 33(2), 215–222.Google Scholar
Fan, H.S.L. and Cao, J.M. (2000). Sea space capacity and operation strategy analysis system. Transportation Planning and Technology, 24(1), 49-63.Google Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24(4), 543–552.Google Scholar
Goodwin, E.M.A. (1975). Statistical study of ship domains. The Journal of Navigation, 28(3), 328–344.Google Scholar
Hansen, M.G., Jensen, T.K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F. M. and Ennemark, F. (2013). Empirical ship domain based on AIS data. Journal of Navigation, 66(6), 931–940.Google Scholar
Hörteborn, A., Ringsberg, J., Svanberg, M. and Holm, H. (2019). A Revisit of the Definition of the Ship Domain based on AIS Analysis. The Journal of Navigation, 72(3), 777–794.Google Scholar
Kadarsa, E., Lubis, A.R.S., Sjafruddin, A. and Frazila, R.B. (2017). Fairway traffic capacity in Indonesia. Procedia Engineering, 171, 1443–1453.Google Scholar
Kijima, K. and Furukawa, Y. (2003). Automatic collision avoidance system using the concept of blocking area. IFAC Proceedings, 36(21), 223–228.Google Scholar
Krata, P., Montewka, J. and Hinz, T. (2016). Towards the assessment of critical area in a collision encounter accounting for stability conditions of a ship. Prace Naukowe Politechniki Warszawskiej Transport, 2016, 169–178.Google Scholar
Liu, J., Zhou, F., Li, Z., Wang, M. and Liu, R.W. (2016). Dynamic ship domain models for capacity analysis of restricted water channels. The Journal of Navigation, 69 (3), 481–503.Google Scholar
Massey, F.J. Jr. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68–78.Google Scholar
Michiel, M.M., Hein, B.B. and Piet, H.L. (1997). Assessment of roadway capacity estimation methods. Transportation Research Record, 1572, 59–67.Google Scholar
Montewka, J., Hinz, T., Kujala, P. and Matusiak, J. (2010). Probability modelling of vessel collisions. Reliability Engineering and System Safety, 95, 573–589.Google Scholar
Montewka, J., Goerlandt, F. and Kujala, P. (2012). Determination of collision criteria and causation factors appropriate to a model for estimating the probability of maritime accidents. Ocean Engineering, 40, 50–61.Google Scholar
Pietrzykowski, Z. (2008). Ship's fuzzy domain – a criterion for navigational safety in narrow fairways. The Journal of Navigation, 61(3), 499-514.Google Scholar
Qu, X., Meng, Q. and Li, S. (2011). Ship Collision Risk Assessment for the Singapore Strait. Accident Analysis and Prevention, 43, 2030–2036.Google Scholar
Śmierzchalski, R. and Michalewicz, Z. (2000). Modeling of a ship trajectory in collision situations at sea by evolutionary algorithm. IEEE Transactions on Evolutionary Computation, 4(3), 227–241.Google Scholar
SOLAS. (2003). International Convention for the Safety Of Life At Sea (SOLAS), International Maritime Organization (IMO), London, 2003.Google Scholar
Szłapczyński, R. and Szłapczyńska, J. (2017). Review of ship safety domains: Models and applications. Ocean Engineering, 145, 277–289.Google Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel collision frequency estimation in the Singapore Strait. The Journal of Navigation, 65(2), 207–221.Google Scholar
Weng, J. and Xue, S. (2015). Ship Collision Frequency Estimation in Port Fairways: A Case Study. The Journal of Navigation, 68(3), 602–618.Google Scholar
Weng, J. and Yan, X. (2016). Probability distribution-based model for work zone capacity prediction. Journal of Advanced Transportation, 50(2), 165–179.Google Scholar
Wu, B, Yan, X., Wang, Y. and Soares, C.G. (2017). An Evidential Reasoning-Based CREAM to Human Reliability Analysis in Maritime Accident Process. Risk Analysis, 37(10), 1936–1957.Google Scholar
Wu, B, Zong, L., Yan, X. and Soares, C.G. (2018). Incorporating evidential reasoning and TOPSIS into group decision-making under uncertainty for handling ship without command. Ocean Engineering, 164, 590–603.Google Scholar
Zhu, X., Xu, H. and Lin, J. (2001). Domain and its model based on neural networks. The Journal of Navigation, 54(1), 97–103.Google Scholar
Zheng, J. and Yang, D. (2016). Hub-and-spoke network design for container shipping along the Yangtze River. Journal of Transport Geography, 55, 51–57.Google Scholar