Prediction of ship collision risk based on CART

Prediction of ship collision risk based on CART

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The primary function of a collision risk index is to determine the time when ships take action to avoid a collision. In this study, based on the complex non-linear relationship between the collision risk degree and its influencing factors, classification and regression trees (CARTs) are applied to construct a prediction model for ship collision risk. The fuzzy comprehensive evaluation method is used to evaluate the risk of ship encounter samples to build a collision risk identification library containing expert collision avoidance experience information. The authors’ proposed CART regression model is trained using the samples in this identification library to develop a collision risk prediction model based on the CART. Their experimental results show that their proposed CART prediction model is better that the existing ship collision risk prediction model in terms of prediction accuracy and prediction speed when the feature dimension is low and the sample size is small.


    1. 1)
      • 1. Kearon, J.: ‘Computer programs for collision avoidance and track keeping’, Proc. Conf. on Mathematical Aspect on Marine Traffic, London, 1977.
    2. 2)
      • 2. Zhang, Z.H.: ‘The research of the ship collision risk model based on fuzzy comprehensive evaluation’. M.E. thesis, Dept. Marine Science and technology, Dalian Maritime University, Dalian, China, 2012.
    3. 3)
      • 3. Bi, J.Q.: ‘The study on inland ship automatic collision avoidance decision’. M.E. thesis, Dept. Marine Science and technology, Dalian Maritime University, Dalian, China, 2016.
    4. 4)
      • 4. Yang, G, Yang, Z.: ‘Research of computing the vessels’ collision risk Index by multiple parameters based on neural network with genetic Algorithm’. Conf. Third Int. Conf. on Instrumentation, Measurement, Computer, Communication and Control, Shenyang, China, 2013, pp. 712714.
    5. 5)
      • 5. Lu, Z. Y.: ‘Genetic neural network algorithm in the application of determining collision risk’, J. Ship Sci. Technol., 2016, 2, (38), pp. 149154.
    6. 6)
      • 6. Chin, H.C., Debnath, A.K.: ‘Modeling perceived collision risk in port water navigation’, Saf. Sci., 2009, 10, (47), pp. 14101416.
    7. 7)
      • 7. Gang, L, Wang, Y, Sun, Y, et al: ‘Estimation of vessel collision risk index based on support vector machine’, Adv. Mech. Eng., 2016, 11, (8), pp. 110.
    8. 8)
      • 8. Cui, X.S., Wu, Y.M., Zhang, J.: ‘Fishing ground forecasting of Chilean jack mackerel in the southeast Pacific Ocean based on CART decision tree’, J. Ocean Univ. Chin., 2012, Z2, (42), pp. 5965.
    9. 9)
      • 9. Park, J., Kim, J.: ‘Predictive evaluation of ship collision risk using the concept of probability flow’, IEEE J. Ocean. Eng., 2017, 42, (99), pp. 110.
    10. 10)
      • 10. Borkowski, P.: ‘The ship movement trajectory prediction algorithm using navigational data fusion’, Sensors, 2017, 17, (6), p. 1432.
    11. 11)
      • 11. Visintin, C., Golding, N., Ree, R.V.D., et al: ‘Managing the timing and speed of vehicles reduces wildlife-transport collision risk’, Transp. Res. D Transp. Environ., 2018, 59, pp. 8695.
    12. 12)
      • 12. Fang, Z., Yu, H., Ke, R., et al: ‘Automatic identification system-based approach for assessing the near-miss collision risk dynamics of ships in ports’, IEEE Trans. Intell. Transp. Syst., 2018, (99), pp. 110.
    13. 13)
      • 13. Jiao, J., Ren, H., Sun, S.: ‘Assessment of surface ship environment adaptability in seaways: A fuzzy comprehensive evaluation method’, Int. J. Nav. Archit. Ocean Eng., 2016, 8, (4), pp. 344359.
    14. 14)
      • 14. Li, H.: ‘Statistical learning method’ (Tsinghua university press, Beijing, China, 2012, 1st edn.).
    15. 15)
      • 15. Lazarowska, A.: ‘Safe ship control method with the Use of Ant colony optimization’, Solid State Phenom., 2014, 2822, (210), pp. 234244.
    16. 16)
      • 16. Szlapczynski, R., Szlapczynska, J.: ‘An analysis of domain-based ship collision risk parameters’, Ocean Eng., 2016, 1, (126), pp. 4756.
    17. 17)
      • 17. Xu, Y.Y., Di, X., Kong, Q.J.: ‘Short-term prediction method of freeway traffic flow’, J. Traffic Transp. Eng., 2013, 2, (13), pp. 114119.
    18. 18)
      • 18. Ayodele, T. R., Ogunjuyigbe, A. S. O.: ‘Prediction of monthly average global solar radiation based on statistical distribution of clearness index’, Energy, 2015, 2, (90), pp. 17331742.
    19. 19)
      • 19. Ross, S. M.: ‘Introduction to probability models’ (Academic Press, Cambridge, MA, USA, 1972, 11th edn. 2014).

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