Multi-measure navigation safety estimation and digital represent for marine area

The paper is devoted to the problem of ensuring the safe movement of ships. The problem of assessing the safety of a traffic pattern implemented in a specific water area is considered. Five different safety metrics are introduced. The first metric - “traffic intensity” - the traditionally used traffic density estimate, is calculated as the number of vessels passing through a particular section of the water area per unit time. It is supplemented by the metrics “intensity plus speed” (second) and “intensity plus size of ships” (third). When calculating them, respectively, the speed of the vessels and their length, which determine the "weight" of each vessel, are taken into account. The fourth metric - “stability of traffic parameters” - takes into account the nature of the movement of ships in terms of the regularity of their courses and speeds. The paper discusses various options for the metric, to illustrate the simplest of them is implemented - an estimate of the standard deviation of the ship's course. The fifth metric - “traffic saturation” - characterizes the density of movement of ships in terms of the possibility of their maneuvers. The metric appeals to the traditional model representations of the collective motion parameters of the vessels in the form of a “speed-course” diagram and makes it possible to indirectly assess the difficulty of decision-making by skippers and the emotional burden on traffic participants. In the discussion of the results of the work, the option of integrating the five proposed metrics in the form of a system of rules giving an integrated assessment of traffic safety in a particular section of the water area is considered. The work is accompanied by the results of calculations of the proposed metrics on real data on the movement of ships in the Tsugaru Strait and their discussion. It is shown that the proposed system of metrics allows you to create a systematic idea of the degree of danger of traffic implemented in the water area.

1. Gagarskij Je.A., Kozlov S.G., Kirichenko S.A. Bezopasnost' sudohodstva pri proektirovanii morskogo porta. Transport: nauka, tehnika, upravlenie. 2018;(1):14-18.

2. Astrein V.V. Sistemy preduprezhdenija stolknovenij sudov, tendencii razvitija (k 40-letiju MPPSS-72). Vestnik Astrahanskogo gosudarstvennogo tehnicheskogo universiteta. Serija: Morskaja tehnika i tehnologija. 2012;(1):7-17.

3. Boran-Keshish'jan S.L. Optimizacija sudovyh putej pri kupirovanii neblagoprijatnyh pogodnyh uslovij v koncepcii edinogo informacionnogo polja e-Navigacii. Jekspluatacija morskogo transporta. 2018;2(87):69-79.

4. Gladskih E.P., Kostin V.N., Maksimov V.A., Repin Ju.M. Razvitie sredstv navigacionnogo oborudovanija pribrezhnoj zony Rossijskoj Federacii v sootvetstvii s koncepciej eNavigacii. Navigacija i gidrografija. 2016;(43):13-21.

5. Sedova N.A., Sedov V.A., Levchenko N.G. Ocenka stepeni opasnosti nabljudaemoj celi na more s ispol'zovaniem sistem iskusstvennogo intellekta. Morskie intellektual'nye tehnologii. 2017;3-4(38):106-114.

6. Sholohova A.A. Poisk anomalij v sensornyh dannyh na primere analiza dvizhenija morskogo sudna. Modeling, Optimization and Information Technology. 2017;(3).

7. Dmitriev V.I., Karetnikov V.V. Metody obespechenija bezopasnosti moreplavanija pri vnedrenii bespilotnyh tehnologij. Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S.O. Makarova. 2017;6(9):1149-1158.

8. Tam Ch. K., Bucknall R., Greig A. Review of collision avoidance and path planning methods for ships in close range encounters. Journal of Navigation. 2009; 3(62):455–476. DOI: 10.1017/S0373463308005134.

9. Lyu H. COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields. Journal of Navigation. 2019; 3(72):588–608. DOI: 10.1017/S0373463318000796.

10. Lentarjov A.A. Morskie rajony sistem obespechenija bezopasnosti moreplavanija: uchebnoe posobie. Vladivostok: Morskoj gosudarstvennyj universitet. 2004.

11. Lentarjov A.A., Maksimov A.A. Primenenie sudovoj navigacionnoj apparatury dlja opredelenija statisticheskih harakteristik sudopotokov. Transportnoe delo Rossii. 2015;(6):156-158.

12. Brodskij P. G., Rumjancev Ju.V., Nekrasov S.N. K voprosu ocenki vlijanija intensivnosti sudohodstva na avarijnost'. Navigacija i gidrografija. 2010;(30):36-42.

13. MarineTraffic [Web]. – Rezhim dostupa: http://www.marinetraffic.com (data obrashhenija: 01.11.19).

14. ADS-B Technologies Website [Web]. – Rezhim dostupa: http://www.ads-b.com (data obrashhenija: 01.11.19).

15. Zhao L., Shi G., Yang J. Ship Trajectories Pre-processing Based on AIS Data. Journal of Navigation. 2018;5(71):1210-1230. DOI: 10.1017/S0373463318000188.

16. Grinyak V.M., Devjatisil'nyj A.S., Trofimov M.V. Vizual'noe predstavlenie parametrov traektorii bezopasnogo dvizhenija sudna. Morskie intellektual'nye tehnologii. 2016;1- 3(33):269-273.

17. Grinyak V.M., Ivanenko Ju.S., Devjatisil'nyj A.S. Vizualizacija parametrov traektorii bezopasnogo dvizhenija sudna. Informacionno-izmeritel'nye i upravljajushhie sistemy. 2016;8(14):52-60.

18. Degre T., Lefevre X. A collision avoidance system. Journal of Navigation. 1981;2(34):294– 302. DOI: 10.1017/S0373463300021408.

19. Szlapczynski R., Szlapczynska J. A target information display for visualising collision avoidance manoeuvres in various visibility conditions. Journal of Navigation. 2015;6(68):1041–1055. DOI: 10.1017/S0373463315000296.

20. Golovchenko B.S., Grinyak V.M. Informacionnaja sistema sbora dannyh o dvizhenii sudov na morskoj akvatorii. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova. 2014;2(24):156-162.

21. Wu L., Xu Y., Wang Q., Wang F., Xu Zh. Mapping global shipping density from AIS data. Journal of Navigation. 2016;1(70):67–81. DOI: 10.1017/S0373463316000345.

22. Ol'hovik E.O. Issledovanie plotnosti transportnyh potokov 2018 goda v akvatorii Severnogo morskogo puti. Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota im. admirala S.O. Makarova. 2018;5(10): 975-982.

23. Weng J., Xue S. Ship collision frequency estimation in port fairways: a case study. Journal of Navigation. 2015;3(68):602-618. DOI: 10.1017/S0373463314000885.

24. Grinyak V.M., Devjatisil'nyj A.S., Ljul'ko V.I. Ocenka opasnosti trafika morskoj akvatorii po dannym Avtomaticheskoj identifikacionnoj sistemy. Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova. 2017;(4):681-690. DOI: 10.21821/2309-5180-2017-9-4-681-690.

25. Grinyak V.M., Ivanenko Ju.S. Ispol'zovanie dannyh AIS dlja ocenki opasnosti kollektivnogo dvizhenija na morskoj akvatorii. Modeling, Optimization and Information Technology. 2017;(3).

26. Zhao L., Shi G. Maritime Anomaly Detection using Density-based Clustering and Recurrent Neural Network. Journal of Navigation. 2019;4(72):894–916. DOI: 10.1017/S0373463319000031.