ORIGINAL ARTICLE

A solving algorithm of navigational collision risk through dataanalysis of fishing vessel activities

Yancai Hu1,2& Gyei-Kark Park2 & Thi Quynh Mai Pham2

Received: 2 June 2019 /Accepted: 15 October 2019# Springer Nature Switzerland AG 2019

AbstractThere is increasing concern that many collision accidents happened in fishing areas. The probability of collision risk increasesdue to the vulnerability of fishing areas where the collision accidents involving large ships may cause serious consequence. Toimprove collision risk assessment, this paper focuses on solving navigational collision risk based on the analysis of fishing vesselactivities using Automatic Identification System (AIS) data. AIS provides a high level of solution to assess fishing activities toreduce the collision influence of fishing vessels. The identification of fishing activities should be handled among the large numberof fishing vessels. Fuzzy C-Means clustering method is applied to accomplish the clusters considering the position and speed offishing vessels. The distance to the cluster of fishing area and its size are chosen asmain factors to infer the vulnerability of fishingarea using fuzzy logic reasoning. Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach(TCPA) are used as significant variables to calculate basic collision risk in practice. Then basic collision risk and vulnerability offishing boats are combined to build an algorithm to solve navigational collision risk for e-Navigation. Simulation is implementedto validate its effectiveness.

Keywords Navigational collision risk . Vulnerability . Fishing activities . e-Navigation

1 Introduction

International Maritime Organization (IMO) has carried out theimplementation of e-Navigation since 2006. Various projectshave been pursued, and many services have been developed(IMO 2018) such as the projects of European MONALISA,ACCSEAS, Japanese ENNS and SSAP. However, most ofprojects and services focused on large ships on internationalvoyages. Without considering both large and small ships, theeffect of e-Navigation will be limited. Fortunately, Korea haspromoted the SMART-Navigation project to develop the newe-Navigation system for the international Convention for theSafety of Life at Sea (SOLAS) ships as well as fishing boatsand other small ships. SMART-Navigation is promoted byseeking a route to provide both SOLAS ships and Non-

SOLAS ships including fishing vessels with e-Navigation ser-vices in a harmonized way and it is an implementation ofIMO’s e-Navigation. It shares and utilizes all related informa-tion to help Non-SOLAS ships in Korean waters and SOLASships with Korean flags to enhance safety and efficiency ofmaritime navigation. SMART-Navigation is developed to pro-vide the LTE-Maritime communication network for Non-SOLAS ships in order to reduce the navigational collisionrisk. One of the key technologies of SMART-Navigation pro-ject is the identification of maritime traffic situations and col-lision risk assessment of the traffic situation involving fishingarea is a way to improve the SMART-Navigation. The state-ment is given for this issue that Non-SOLAS vessels includingfishing vessels account for more than 99% of total number ofships while SOLAS ships account for less than 1% accordingthe survey shown in Table 1 (Ministry of Oceans and Fisheries2016). Among the number of merchant fleet and fishing ves-sels, the latter is more than 85% of the total. According to thesurvey, in Korea, 68% of maritime accidents are related tofishing vessels, 82% of maritime accidents are caused by hu-man errors (KMST 2016) and fishing vessels have relativelypoor navigation and communication means in Korea (An2016).

* Gyei-Kark Parkgkpark@mmu.ac.kr

1 School of Navigation, Shandong Jiaotong University, Jinan, China2 Department of Maritime Transport, Mokpo National Maritime

University, Mokpo, South Korea

https://doi.org/10.1007/s42488-019-00014-xJournal of Data, Information and Management (2020) 2:25–37

/Published online: 25 November 2019

As the statistics of merchant fleet and fishing vessels in theworld shown in Table 2, 98.1% of the total vessels is fishingvessels. It accounts for large proportion and leads to the causesof accidents.

Until now, many of fishing vessels don’t have communi-cation or/and navigation means when they encounter SOLASships. Considering the different behaviors of fishing vessels inoperation, there is still no means to harmonize SOLAS shipsand fishing vessels. Therefore, it has a big impact on the col-lision risk in fishing area.

In order to solve the issues of fishing vessels, there aresome solutions to avoid collision accidents for the merchantships. The routes should be planned according to the recom-mended or customary route of navigation. The route shouldnot be too close to the shore to avoid accidents or troubles withfishing vessels. In the voyage, the long-range of the radar issuggested to use to observe whether there is a dense fishingarea in front of the route. If it is found, it should be activelycircumvented and the preparation for the intensive fishingvessels group should be paid attention in advance. Activeand decisive actions are of importance to guarantee avoiding

collisions when being caught in an urgent situation forced byfishing vessels. In the fishing area, it is also suggested to payattention to the dynamics of fishing vessels and take full-scaleconsiderations of surrounding merchant ships which easilycause serious accidents. However, all this work will bringmuch work for the officers and it is more difficult for theofficer without much experience. In order to reduce the burdenof emergent situation awareness for watching fishing vessels,improvement should also be put forward.

In Fig. 1, a structure of navigation risk assessment consid-ered the vulnerability (Kim 2017) which is defined as theprobability of a marine accident or the degree of a marineaccident happens. The units of environmental risk assessmentand navigational risk assessments of fishing vessels are listedto enhance the awareness of fishing areas to improve SMART-Navigation according to the structure of Kim. Thus, this paperis to carry out an algorithm for solving the vulnerability offishing operating area. Vulnerability is combined with basiccollision risk to build navigational collision risk system formerchant ships and fishing vessels in dense fishing area.

2 Literature review

There are many studies analyzing collision risk assessment.Among the studies, fuzzy logic is considered as a suitable andeffective method to deal with collision risk. Hasegawa (1987)designed the fuzzy inference system using DCPA and TCPAas inputs, large quantities of papers have been proposed for theimprovement and application of this method. Lee and Rhee(2001) developed a fuzzy collision avoidance system usingthe expert system.Moreover, an autonomous fuzzy navigationalgorithm was conceived in paper of Lee et al. (2004). Themodified virtual force field method is presented for eight

Table 1 Statistics of merchantfleet and fishing vessels in Korea Korea (Statistical Yearbook of Ministry of Oceans and Fisheries, 2016) SOLAS 0.99%

Non-SOLAS 99.01%

1. Merchant fleet 11,501 14.6%

1) Cargo ships 1159

(1) International 751 751

(2) Domestic 408 408

2) Passenger ships 1861

(1) International 29 29

(2) Domestic 1832 1832

3) Other ships (special ships) 8481 8481

2. Fishing vessels 67,226 67,226 85.4%

1) Marine waters 62,659

2) Inland waters 3101

3) Others 1466

Total 78,727 780

Table 2 Statistics of merchant fleet and fishing vessels in the world

World (Equasis Statistics, 2015)

1. Merchant Fleet 87,233 1.9%

1) Cargo ships 49,948

(1) more than 500GT 43,006

(2) less than 500GT 6942

2) Passenger ships 6741

3) Special ships 30,544

2. Fishing vessels 4,606,000 98.1%

Total 4,693,233

J. of Data, Inf. and Manag. (2020) 2:25–3726

track-keeping or collision avoidance modes based on thefuzzy rules. Then, Park and Benedictos (2006) proposed aship collision avoidance support system using fuzzy case-based reasoning to carry out decision-making by retrievingpast similar cases.

Additionally, Bukhari et al. (2013) utilized DCPA, TCPA,bearing and variance of compass degree among all vesselsfrom the view of vessel traffic service (VTS) centre. The in-formation extracted from conventional marine equipment wasexploited to calculate and display the degree of collision risk.The radar filtration algorithm helped the VTS officer tomeasure collision risk around particular ships. Based on thesystem of Bukhari, Ali et al. (2015) presented type-2 fuzzyontology-based semantic knowledge and a simulator to reduceexperimental time and the cost of marine robots. DCPA,TCPA and variation of compass degree were still used to cal-culate the degree of collision risk. The study (Rao andBalakrishnan 2017) also developed an early warning systemfor fishing boats to avoid collision with ships by using DCPAand TCPA.

However, all these studies focused on the collision risk with-out considering fishing area. The majority of collision and con-tact incidents involved a fishing vessel and a merchant vessel(Natale et al. 2005). While fishing vessels are involved in thefishing operation, skippers cannot concentrate on plotting theposition and movement of other vessels approaching them.Fishing vessels also may cause urgencies and collisionsbetween merchant ships. Kim et al. (2013) researched a controlfactor for the marine casualty of fishing vessel using the riskquantitative method of marine casualty and sequentially timedevent analysis for the reason finding. It found that the high-riskcollision, sinking and capsizing need to be strongly controlled.The studies (Lee et al. 2013) were conducted to inquire into thecause of collision between fishing vessels and non-fishing ves-sels. Further, Jin (2014) examined the determinants of fishingvessel accident severity using vessel accident data.

Studies of Fukuda and Shoji (2017) and Li et al. (2018)pursued to identify the high navigational risk area based onAIS data. Both could estimate the high possibility of collisionby analysing the traffic conditions. The model results ofWeng

Fig. 1 Awareness system for vulnerability of vessel. Source: Kim 2017

DFA

SFA

TCPA

DCPA Fuzzy ReasoningEngine1

Fuzzy ReasoningEngine2

Basic Collision Risk

Vulnerability

Fuzzy ReasoningEngine3

ofNavigational

Collision Risk

Navigational Collision

Risk

Module1

Module2 Module3

Fig. 2 Structure of thenavigational collision risk solvingsystem. Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–37 27

et al. (2018) showed that the involvement of big ships andfishing vessels has the largest impact in increasing the proba-bility of a serious or very serious accident. Thus, there wouldbe a bigger probability of a serious accident in fishing area ifboth ships involved in the collision are merchant ships.

Vulnerability was described in the study of Berle et al.(2011). It presented a structured formal vulnerability assess-ment methodology seeking to transfer the safety-oriented for-mal safety assessment framework into the domain of maritimesupply chain vulnerability. Vulnerability also can be used todescribe the possibility of accidents caused by fishing vessels.

Maritime situational awareness was suggested byMazzarella et al. (2014) to combine greatly improved the au-tomatic identification and classification of vessel activities forthe capability of understanding events, circumstances andfishing activities within and impacting the maritimeenvironment. AIS provides the possibility to integrate andenrich the available services and information in the maritimedomain. Natale et al. (2005) also assessed the data of AISinstead of vessel monitoring system and the feasibility of pro-ducing a map of fishing effort with high spatial and temporalresolution at European scale. These studies helped to improve

the ability of fishing activities identification and highlight thefishing vessels clusters.

Particularly, this paper aims to solve navigational collisionrisk considering the vulnerability of fishing area by analysingfishing vessels activities using clustering analysis of AIS datafor the collision risk assessment.

3 Framework of navigational risk solvingsystem

The framework of navigational collision risk solving system basedon vulnerability contains three modules as described in Fig. 2.

Firstly, DCPA and TCPA evaluating collision risk andsupporting decision making, are used to calculate the collisionrisk. This basic collision risk can be obtained by designing themembership functions and rules of DCPA and TCPA.Secondly, in fuzzy reasoning engine 2, the distance to fishingarea and the size of fishing area are used to calculate vulner-ability. Finally, basic collision risk and vulnerability of fishingarea are integrated to infer navigational collision risk.

3.1 Basic collision risk

DCPA and TCPA are simultaneously considered for solvingbasic collision risk to offer a reasonable and applicable colli-sion risk assessment. DCPA is a significant input variable thatcan determine whether the encountering ships will collidewith own ship if the right alteration of heading is not executed.TCPA can be used to determine the remaining time for takingcollision-avoidance action. Note that to evade a possible col-lision, DCPA and TCPA will be considered at the same timeby employment of the following equations (Jo et al. 2018).

Table 3 Reasoning rules of DCPA, TCPA and collision risk

TCPA DCPA

VS S M B VB

VS VB VB B B M

S VB B B M M

M B B M M S

B B M M S S

VB M M S S VS

Source: Authors

0 0.3 0.6 0.9 1.2 1.50

0.5

1 VS S M B VB

DCPA(miles)

edarG

0 2 4 6 8 10 120

0.5

1 VS S M B VB

TCPA (minutes)

edarG

0 0.25 0.5 0.75 10

0.5

1 VS S M B VB

Collision Risk

edarG

Fig. 3 Membership functions forDCPA, TCPA and basic CR.Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–3728

DCPA ¼ D Vosinα−Vtsinβð ÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiV2o þ V2

t þ 2VoVtcos αþ βð Þq ð1Þ

TCPA ¼ D Vocosα−Vtcosβð ÞV2o þ V2

t þ 2VoVtcos αþ βð Þ ð2Þ

where VO is Own Ship (OS) velocity, Vt is Target Ship (TS)speed, Distance (D) is the distance from OS to TS, α is the

Fig. 4 The selected area of implementation. Source: Authors

Table 4 Information of vessels near Mokpo area at 10:00 am~10:03 am on May 5, 2018

No Longitude Latitude Speed Ship identification No Longitude Latitude Speed Ship identification

1 125.410 34.688 0 31,001,739 31 125.716 34.730 9 34,200,6082 125.932 34.719 0 31,002,575 32 125.160 34.655 8 34,200,6823 125.686 34.472 7 31,100,492 33 125.737 34.461 4 34,400,2984 125.969 34.577 2 32,000,029 34 125.186 34.694 0 35,100,4635 125.201 34.708 0 32,000,061 35 125.461 34.707 8 35,200,7076 125.934 34.720 0 32,100,291 36 125.968 34.412 3 36,001,5287 125.437 34.684 0 32,100,303 37 125.190 34.720 1 36,001,7688 125.445 34.692 0 32,100,488 38 125.175 34.683 8 36,001,7699 125.444 34.688 0 32,100,489 39 125.438 34.758 0 36,001,83910 125.933 34.720 0 32,100,551 40 125.417 34.713 0 36,001,95511 125.197 34.682 7 32,100,593 41 125.418 34.713 2 36,002,01512 125.932 34.719 0 32,200,180 42 125.193 34.682 0 36,002,58913 125.948 34.733 0 32,300,237 43 125.438 34.758 0 36,008,01714 125.934 34.721 0 32,300,281 44 125.443 34.686 0 36,009,80915 125.442 34.687 0 32,300,334 45 125.190 34.687 0 36,011,86416 125.550 34.568 0 32,300,419 46 125.369 34.765 0 36,013,46417 125.847 34.734 1 32,300,900 47 125.658 34.556 3 36,013,56918 125.906 34.538 4 32,301,007 48 125.435 34.685 0 36,015,58019 125.972 34.494 0 32,301,008 49 125.393 34.663 0 36,015,60620 125.853 34.589 0 32,301,011 50 125.290 34.436 12 36,015,62821 125.949 34.494 9 32,301,014 51 125.214 34.588 8 36,015,65422 125.845 34.555 8 33,100,193 52 125.238 34.733 8 36,015,92923 125.934 34.721 0 33,100,288 53 125.639 34.726 12 36,016,08424 125.929 34.718 0 33,100,361 54 125.454 34.736 0 36,016,15725 125.440 34.688 0 33,100,556 55 125.465 34.653 0 36,104,99126 125.287 34.518 9 33,200,654 56 125.443 34.689 0 36,300,29427 125.393 34.689 0 33,200,798 57 125.433 34.694 2 36,300,40428 125.444 34.687 0 33,300,224 58 125.431 34.689 0 36,300,47429 125.433 34.688 0 33,300,306 59 125.395 34.651 0 36,308,73030 125.394 34.689 0 34,200,222

Source: Korean AIS

J. of Data, Inf. and Manag. (2020) 2:25–37 29

relative bearing of TS based on OS and β is relative bearing ofOS based on TS.

The calculation of collision risk has two inputs and oneoutput, which are determined by the reasoning rules in

Table 3. In this collision risk solving system, fuzzy logic willbe utilized to describe such ambiguous linguistic values. Thetriangular membership functions are designed for the inputs

Table 5 Information of vessels near Mokpo area at 15:00 pm~15:03 pm on May 5, 2018

No Longitude Latitude Speed Ship identification No Longitude Latitude Speed Ship identification

1 125.410 34.688 0 31,001,739 31 125.716 34.730 3 34,200,608

2 125.932 34.719 0 31,002,575 32 125.161 34.653 0 34,200,682

3 125.686 34.472 7 31,100,492 33 125.737 34.461 4 34,400,298

4 125.969 34.577 2 32,000,029 34 125.186 34.694 0 35,100,463

5 125.201 34.708 0 32,000,061 35 125.461 34.707 8 35,200,707

6 125.934 34.720 0 32,100,291 36 125.968 34.412 3 36,001,528

7 125.437 34.684 0 32,100,303 37 125.190 34.720 1 36,001,768

8 125.445 34.692 0 32,100,488 38 125.175 34.683 8 36,001,769

9 125.444 34.688 0 32,100,489 39 125.438 34.758 0 36,001,839

10 125.933 34.720 0 32,100,551 40 125.417 34.714 1 36,001,955

11 125.198 34.681 6 32,100,593 41 125.418 34.713 2 36,002,015

12 125.932 34.719 0 32,200,180 42 125.193 34.682 0 36,002,589

13 125.948 34.733 0 32,300,237 43 125.438 34.758 0 36,008,017

14 125.934 34.721 0 32,300,281 44 125.443 34.686 0 36,009,809

15 125.442 34.687 0 32,300,334 45 125.190 34.687 0 36,011,864

16 125.550 34.568 0 32,300,419 46 125.369 34.765 0 36,013,464

17 125.847 34.734 1 32,300,900 47 125.658 34.556 3 36,013,569

18 125.906 34.538 4 32,301,007 48 125.435 34.685 0 36,015,580

19 125.972 34.494 0 32,301,008 49 125.393 34.663 0 36,015,606

20 125.853 34.589 0 32,301,011 50 125.290 34.436 12 36,015,628

21 125.949 34.494 9 32,301,014 51 125.214 34.588 8 36,015,654

22 125.845 34.555 8 33,100,193 52 125.238 34.733 8 36,015,929

23 125.934 34.720 1 33,100,288 53 125.639 34.726 12 36,016,084

24 125.929 34.718 0 33,100,361 54 125.454 34.736 0 36,016,157

25 125.440 34.688 0 33,100,556 55 125.465 34.653 0 36,104,991

26 125.287 34.518 9 33,200,654 56 125.443 34.689 0 36,300,294

27 125.393 34.689 0 33,200,798 57 125.431 34.693 6 36,300,404

28 125.444 34.687 0 33,300,224 58 125.431 34.689 0 36,300,474

29 125.433 34.688 0 33,300,306 59 125.395 34.651 0 36,308,730

30 125.394 34.689 0 34,200,222

Source: Korean AIS

Table 7 Clustering results based on speed (2018.05.05 15:00 pm-15:03 am)

Cluster No. of vessels Longitude Latitude Speed

1 40 125.53 34.69 0–1

2 7 125.77 34.57 2–4

3 3 125.36 34.66 6–7

4 7 125.44 34.62 8–9

5 2 125.47 34.58 12

Source: Authors

Table 6 Clustering results based on speed (2018.05.05 10:00 am-10:03 am)

Cluster No. of vessels Longitude Latitude Speed

1 39 125.54 34.69 0–1

2 7 125.75 34.54 2–4

3 11 125.43 34.63 7–9

4 2 125.47 34.58 12

Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–3730

and output. For the conclusion, the variables of the fuzzy rulesto solve basic CR are shown as

DCPA;TCPAð Þ→Basic collision risk ð3Þ

A succinct fuzzy reasoning model is used as a popularmethod and the membership functions for DCPA, TCPA andbasic collision risk are classified as five linguistic values: VerySmall (VS), Small (S), Medium (M), Big (B), Very Big (VB).The calculation of collision risk is determined by the reason-ing rules. It is expressed as Multiple Inputs Single Output(MISO) system which has the rules in Table 3 (Jo et al. 2018).

The degree of basic collision risk is determined by therelation of two inputs DCPA, TCPA and single output accord-ing to the fuzzy logic reasoning rules.

In Fig. 3, it shows the fuzzy membership functions ofDCPA, TCPA and basic collision risk. The centroid methodis used for defuzzification (Negnevitsky 2005). If DCPA andTCPA are approaching zero, then the value of collision riskbecomes bigger. Namely, when the value of output is close toone, it is riskier for the ship’s collision.

3.2 Analysis of fishing vessel activities using AIS data

The Fuzzy C-Means clustering method will be used in thispart to analysis the fishing operation areas. Fuzzy C-Meansclustering method was developed by Dunn in 1973 and im-proved by Bezdek in 1981 to deal with the problem of over-lapping clusters which cannot be solved in the classicalmodels. Fuzzy C-Means clustering method can use Fuzzytheory to assign data to a plurality of clusters using the mem-bership degree between 0 and 1 without belonging to a spe-cific cluster.

Let X = {x1,x2,…xN} be the set of data and V = {v1, v2,…vC} be the set of clusters’ centers in a p dimensional spacewhere p is the number of data properties, N is the number ofdata and C is the number of clusters. Centroids are used ascenters in describing the clusters.

Condition for a fuzzy partition matrix are given by:

μi∈ 0; 1½ �; 1≤ i≤C; ð4Þ

∑c

i¼1μij ¼ 1; 1≤ j≤N; ð5Þ

0≤ ∑N

j¼1μij < N ð6Þ

FCM algorithm minimizes the objective function asfollows:

Jm X;U;Vð Þ ¼ ∑N

j¼1∑C

i¼1μij

� �m∙ xj−vi�� ��2 ð7Þ

where m is weighted index number.Cluster centers are computed using the formula:

vi ¼ ∑N

j¼1umij x j= ∑

N

j¼1umij ð8Þ

The relative membership function of each data towards thecentroid is calculated as follows:

uij ¼ 1

∑C

k¼1

x j−vik k2.x j−vkk k2

� � 1m−1

ð9Þ

The following sections are structured in the following mainsteps: (1) collecting data of the ships start from area nearbyMokpo (Korea) by using AIS; (2) choosing the suitable datafor Fuzzy C-Means clustering; (3) using Fuzzy C-Means clus-tering to cluster and identify the fishing areas; (4) giving outthe solution for solving the navigational collision risk basedon the results of fishing area identification.

AIS system is providing a wealth of data that can be used toautomatically extract knowledge for situational prediction oranomaly detection. Such knowledge reflects the behavior of aportion of traffic identified by the reporting system require-ments. AIS data contains ship information including static/voyage-related information such as Maritime Mobile ServiceIdentify (MMSI) number, name, IMO number, call sign anddynamic information (e.g., position, speed) of the ship. In thisresearch, the static information just is used to discard datahaving the same identifier (MMSI). Dynamic data is usedprimarily to distinguish the operating area of the fishing ves-sels. Figure 4 shows the interested area in where the data wastaken. That is the area nearby the Mokpo port in Korea.

Table 8 Clustering results based on vessels’ coordinates (2018.05.0510:00 am-10:03 am)

Cluster No. of vessels Longitude Latitude

1 12 125.196 34.6845

2 26 125.435 34.6907

3 19 125.898 34.5336

Source: Authors

Table 9 Clustering results based on vessels’ coordinates (2018.05.0515:00 pm-15:03 am)

Cluster No. of vessels Longitude Latitude

1 13 125.194 34.6851

2 24 125.432 34.6944

3 20 125.924 34.5430

Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–37 31

Tables 4 and 5 show the data collected from AIS system,the information of vessels nearby Mokpo area on May 5th2018. The real data from AIS system is big data but for testingthe navigational collision risk solving system, only the three-minutes data was used in the study. The data includes theidentification number, the location and speed of the ship.

Then next step, after discarding the similar data which havethe same identifier (MMSI), we use the dynamic informationto cluster the ships into groups. In order to identify the realfishing operation area but not a normal ship operation, weshould consider the speed of the ship. Fabio’s research(2014) showed the behavior of reported trawling, which is a

Fig. 6 New clustering results (15:00 pm-15:03 pm). Source: Authors

Fig. 5 New clustering results (10:00 am-10:03 am). Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–3732

kind of fishing operation. It showed that the speed of a ship inthe port is zero, the trawling speed is about 2–4 knot and thesteaming speed is no more than 10 knots. Based on the re-search results of Antonio et al. (2016), the fishing activity hasspeed between 5 and 10 knots meanwhile the searching activ-ities occur at a slightly higher speed (over 11 knots).Therefore, it can be assumed that vessels with speed above10 knots are not in the scope of this research. In the next step,we will use Fuzzy C-Means clustering to classify the vesselspassing through the test area. The results will be determinedbased on the dynamic vessel. They are vessels’ coordinates(longitude, latitude) and speed. Since the scope of the studyonly refers a small sea area, the speed will be the main factor todivide the groups. Tables 6 and 7 show grouping results in 2different time periods. Based on the results, we can eliminatethe group of vessels with speed higher than 10 knots.

After eliminating the vessels that are not in the scope, weonce again proceed to divide the groups by the position of thevessel (vessel’s coordinates). The results in Table 8, Table 9,Figs. 5 and 6 show that in both periods, we can divide the

passing vessels into 3 groups based on their longitude andlatitude.

In fishing vessels operating area, fishery, fishing line, trawlnet and other fishing gears limit traffic performance and causecollision accidents between merchant ships and fishing ves-sels, and also other accidents such as obstructing the route ofother ships or netting the propeller. Catastrophic results interms of safety were caused when the other ship involvedwas a fishing vessel and it highlights the need for better plan-ning when a bulk carrier is sailing in fishing areas. Thus, theroute planning of the merchant ships is usually designed toavoid traditional fishing grounds and areas with dense fishingfleets. In this case, the fishing vessel activities will be analyzedto discover the fishing areas to obtain the input variables of thereasoning engine. Based on the Fuzzy C-Means clusteringresults, two input variables are chosen to infer vulnerabilityof fishing vessels operating areas.

0 2 4 6 8 10 12 14 16 18 200

0.5

1 Small Medium Big

Distance to Fishing Area(miles)

edarG

0 5 10 15 20 25 300

0.5

1 Small Medium Big

Size of Fishing Area(miles)edar

G

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.5

1 Very Small Small Medium Big Very Big

Vulnerability

edarG

Fig. 7 Fuzzy logic membershipfunctions for fishing beatsoperating area. Source: Authors

Table 11 Reasoning rules of navigational collision risk

Collision Risk Vulnerability

VS S M B VB

VS VS VS S M B

S S S M B VB

M M M B VB VB

B B B VB VB VB

VB VB VB VB VB VB

Source: Authors

Table 10 Reasoningrules for vulnerability forfishing beats operatingarea

SFA DFA

Small Medium Big

Small M S VS

Medium B M S

Big VB B M

Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–37 33

DFA; SFAð Þ→Vulnerability of fishing vessels operating area ð10Þwhere DFA is distance from the own ship to the center ofFishing Area and SFA is size of fishing area.

The DFA is calculated by ‘haversine’ formula as below.That formula is used to calculate the great-circle distance be-tween two points which is the shortest distance over the earth’ssurface – ‘as-the-crow-flies’ distance between the points.

a ¼ sin2Δφ2

� �þ cosφ1:cosφ2:sin

2 Δλ2

� �ð11Þ

c ¼ 2:atan2ffiffiffia

p;

ffiffiffiffiffiffiffiffiffiffiffiffi1−að Þ

p� ð12Þ

DFA ¼ R c ð13Þwhere φ1, φ2 are latitude, λ1, λ2 are longitude, Δφ =φ1 −φ2,Δλ = λ1 − λ2, R is earth’s radius (mean radius = 6371 km); c isthe angular distance in radians, and a is the square of half thechord length between the points.

SFA usually is almost irregular and cannot be measuredexactly. Mathematically, the normal way to calculate theirregular shape is to use calculus. However, considering thediscrete nature of the data and the difficulty of implementingcalculus in code, the most efficient method to calculate thearea of irregular shape is breaking up into small regularshape. The trapezoid method has been successfully used inpaper of Zhang and Zhou (2009) to calculate the size of anarea. Trapezoid method breaks an irregular shape into manytrapezoids whose areas are easier to be calculated. Thenadding relevant area of trapezoids and subtracting relevantarea of trapezoids to obtain the total area of the irregular shape.

Fuzzy logic membership functions for fishing vessels operat-ing area are noted in Fig. 7 for the distance to the center of fishingarea and size of fishing area. Based on the clusters and the surveyof experts and navigational officers, 30 miles is deemed to be bigvalue of the size of fishing area and 20 miles is deemed to be bigdistance to fishing area. Distance of 10miles and 15miles size offishing area are considered as medium values.

0 0.25 0.5 0.75 10

0.5

1 VS S M B VB

Basic Collision Risk

edarG

0 0.25 0.5 0.75 1 1.20

0.5

1 VS S M B VB

Vulnerabilityedar

G

0 0.25 0.5 0.75 10

0.5

1 Very Small Small Medium Big Very Big 1

Navigational Collision Risk

edarG

Fig. 8 Fuzzy membershipfunctions for module three.Source: Authors

Table 12 Details of target shipsin the vicinity of own ship Ship Course

(degrees)Speed(knots)

Bearing(degrees)

Distance(miles)

DCPA(miles)

TCPA(minutes)

A 240 30 050 5.0 0.46 7.38

B 260 10 025 7.5 4.22 9.83

C 150 25 350 6.1 1.43 9.15

D 087 37 280 6.5 0.12 11.5

Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–3734

According to the membership functions of DFA and SFA, thereasoning rules are described to obtain the vulnerability of fishingarea in Table 10 according to experts and navigational officers.

Because it is not possible to calculate the navigational col-lision risk by adding the values of collision risk and vulnera-bility mathematically, fuzzy logic will be also used to get thenavigational collision risk in the following part.

3.3 Navigational collision risk solving system

Combining with the modules one and two, the navigationalrisk will be calculated using two input variables of collisionrisk and vulnerability according to the designed fuzzy rules asshown in Table 11.

The reasoning rules for basic CR, vulnerability andnavigational CR are shown in Table 9. If vulnerabilitybecomes bigger, the navigational collision risk will

increase to a higher level approaching to 1. The mem-bership functions of basic collision risk, vulnerabilityand consequence are designed in Fig. 8.

4 Application of navigational collision risksolving system

The proposed algorithm will be tested with simulationto prove its validity. In the simulation, the course andspeed of OS are 10° and 14 knots respectively. FourTSs A, B, C, D in the vicinity of own ship in coastalwaterway are shown in Table 12 with the information ofcourse, speed, bearing and distance which are used tocalculate DCPA and TCPA (Jo et al. 2018).

The information of the merchant ships and fishing vesselsis listed in Table 10 and the results of vulnerability of fishing

Table 13 Results of vulnerability around fishing areas

Position 1 2 3 4 5 6 7 8 9 10

DFA 22.91 22.91 30.83 14.03 14.03 30.83 12 10 8 6

SFA 7.71 18.62 11.74 16.31 22.24 11.84 22.24 22.24 22.24 22.24

Vul. 0.19 0.32 0.22 0.42 0.52 0.22 0.56 0.62 0.65 0.67

Source: Authors

Table 14 Results of thenavigational collision risk TS Vulnerability Basic CR Nav. CR TS Vulnerability Basic CR Nav. CR

A 0.19 0.62 0.62 A 0.22 0.62 0.62

B 0.07 0.14 B 0.07 0.14

C 0.17 0.21 C 0.17 0.21

D 0.50 0.50 D 0.50 0.50

A 0.32 0.62 0.62 A 0.56 0.62 0.69

B 0.07 0.14 B 0.07 0.28

C 0.17 0.21 C 0.17 0.34

D 0.50 0.50 D 0.50 0.64

A 0.22 0.62 0.62 A 0.62 0.62 0.76

B 0.07 0.14 B 0.07 0.31

C 0.17 0.21 C 0.17 0.39

D 0.50 0.50 D 0.50 0.71

A 0.42 0.62 0.62 A 0.65 0.62 0.81

B 0.07 0.14 B 0.07 0.33

C 0.17 0.21 C 0.17 0.41

D 0.50 0.50 D 0.50 0.75

A 0.52 0.62 0.66 A 0.67 0.62 0.81

B 0.07 0.25 B 0.07 0.38

C 0.17 0.30 C 0.17 0.45

D 0.50 0.59 D 0.50 0.77

Source: Authors

J. of Data, Inf. and Manag. (2020) 2:25–37 35

areas are shown in Table 13 where Vul. is vulnerability. 10positions of own ship are selected from the situations ofFigs. 4 and 5. For instance, the own ship is involved in anencounter with four target ships including merchant ships andfishing vessels in crossing with each other. This proposedscheme for navigational collision risk solving system will beimplemented to validate its practicability.

The vulnerability of fishing area and basic collision risk offour ships can be seen from Table 14. Under the condition ofencountering ships in position 9 and 10, the collision risk isdetected for a potential collision for ship A as the value is 0.81.So that, ship A is considered as an alert of collision, while thethreshold value set as 0.80 is exceeded by the detected value.If the fishing activities are not considered, the collision riskonly using DCPA and TCPA is 0.62 which fails to alert forcollision risk and may lead to miss the best time to take col-lision avoidance.

When the ships go through dense fishing vessels, it is dan-gerous to ignore the influence caused by the fishing operationswhich may lead to accidents. Compared with conventionalcollision risk assessment, this algorithm is accurate and rea-sonable for collision assessment by way of integration.Collision accidents could be effectively prevented if the nav-igational collision risk solving system is suggested to the of-ficers and the cadets who have insufficient sea experience andnavigation competency. The proper collision avoidance ac-tions will be taken in advance when the emergent encounter-ing occurs to both fishing vessels in operations and merchantships that may cause serious accidents and lead to large loss oflife and property.

5 Conclusion

Under the background of SMART-Navigation project, thispaper proposed a comprehensive estimation to investigate po-tentials for navigational collision risk solving system and ap-ply effects to the implementation of e-Navigation solutions tofishing vessels. Based on the previous studies and surveys, thefishing abilities of fishing vessels are universally acknowl-edged as a significant factor to reduce the collision accidents.A fuzzy methodology for navigational collision risk based onmarine accident vulnerability was carried out for encounteringships including fishing vessels. The process of this solvingsystem of navigational risk are listed as below. Firstly, in thedesigned framework, basic collision risk solving system gen-erally unites DCPA and TCPA to reduce the burden of calcu-lation; secondly, the analysis of fishing vessel vulnerabilitywas investigated by using Fuzzy C-Means; finally, naviga-tional collision risk solving system integrated the vulnerabilitywith basic collision risk to provide navigational collision risk.Simulations were completed for testing the validity of the

proposed solving system for this navigational collision riskassessment.

Acknowledgement This research is a part of the project titled “SMART-Navigation project,” funded by the Ministry of Oceans and Fisheries,KOREA.

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