Analysis of Vessel Movements using Association Rules

Noviyanti Santoso, Wahyu Wibowo, and Nur Azizah

Department of Business Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Keywords: Vessel Movements, Association Rules, VISITS

Abstract: Currently, ship traffic and the situation of sea waters are essential for several countries, including Indonesia

as an archipelago. The automatic Identification System of ITS (VISITS) provides data containing a variety

of information about vessel movements and traffic characteristics in Java sea waters. We use data mining

techniques to identify and determine ships' movement patterns in the Java sea, especially the Lombok Strait.

The objects of research are ships around the Lombok Strait because it is one with hefty ship traffic. Based

on the analysis results, we obtain information that the dominant types of ships passing in the Lombok Strait

are cargo ships with medium vessel speed and relatively spread out the course over ground, as many as 25%

of ships heading to the South. Furthermore, using the a priori algorithm association rules method, 14 rules

are obtained for a maximum of 3 items and 26 rules for a maximum of 4 items. The rules with the highest

lift score state that if the ship's coordinates are at Latitude = 7 and Longitude = 116, and type = Cargo ship,

then the course over ground is about 180° - 225° or the vessel is heading South.

1 INTRODUCTION

Indonesia is a country with a big sea area called an

archipelago. Indonesia has the potential to develop

the existing marine. Apart from abundant marine

resources, Indonesian seas are also a route for world

trade. As much as 90% of world trade routes are

transported by sea, 40% of this trade passes through

Indonesia. Indonesia has an opportunity to become a

World Maritime Axis Country by improving an

integrated marine transportation system.

Traditionally, the process of tracking vessels at sea

is using radar. However, with the development of

technology, currently, there is a satellite-based

Automatic Identification System (AIS) to monitor

the activities and movements of ships passing in sea

waters.

AIS data provides much information, including

ship identity, passage time, latitude-longitude, type,

speed, course over ground, etc. Predicting the status

of vessel motion, patterns, and ship anomaly status

can use that type of data (Gustisatya, 2017). The

result is essential to maintain the security of

interactions between ships and ship traffic in sea

waters.

Several data mining approaches have been

applied to AIS data to analyze ship movements used

the Association rules and Markov model to inform

the motion patterns of ships in Chinese waters

(Deng, et al., 2014). The Markov model became the

solution for the continuous transaction. In 2011,

Mascaro detected anomalous motion of ships

passing through Sydney waters using the Bayesian

Network method (Mascaro, et al., 2013).

Furthermore, the visualization and interaction of

anomalous status on ships in Sweden using the

combined Gaussian Mixture Model and Kernel

Density Estimation and Clustering based on the

ship's trajectory (Riveiro and Falkman, 2014). Zhu F

applies Agrawal’s association rule in mining ship

trajectory patterns (Agrawal, et al., 1993).

In this study, we analyse the vessel movements

using the association rules method based on the

frequency. This work aims to determine an

appropriate pattern of vessel motions. The result

sustains and simplify to control, monitor, and predict

the vessel's activities. The paper is structured as

follows. In the first section, the background and

descriptions of several studies were appropriate. The

second section explained the analytical methods

used in the study, followed by the research

methodology covering the data sources and pre-

processing. In the third section, data analysis

consists of ship data characteristics and ship motion

patterns using association rules. The last section is

the conclusion of the research.

Santoso, N., Wibowo, W. and Azizah, N.

Analysis of Vessel Movements using Association Rules.

DOI: 10.5220/0010797800003317

In Proceedings of the 2nd International Conference on Science, Technology, and Environment (ICoSTE 2020) - Green Technology and Science to Face a New Century, pages 125-130

ISBN: 978-989-758-545-6

125

2 RESEARCH METHODS

Data mining is the exploration and analysis of a set

of data to find essential patterns and rules (Margaret,

2003). There are several data mining techniques,

including classification, clustering, and association

rules, where each technique has a different function.

The following section will describe the theory of the

association rules.

2.1 Association Rules

Association rules are techniques of data mining to

find patterns of the combination of an item. The

function of the association rule is to find shared

value items that often appear together in a database.

Several references mention association rule with

market basket analysis based on a priori algorithm

which has the principle "if an itemset is frequent

appears, then all subsets of the itemset are also

frequent appear". This principle refers to the nature

of the support measure, which means that the

support of an item set is less than its data subsets

support.

The importance of a rule is determined

according to two parameters: support (the

percentage of the item combination) and confidence

(the strength of the relationship between items).

Association analysis has defined a process for

finding all associative rules that meet the minimum

requirements for support score and minimum

requirements for confidence score [8]. The function

to determine the support value of one item as

follows:

𝑠𝑢𝑝𝑝𝑜𝑟𝑡



𝐴







𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑡𝑒𝑚 𝐴





𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑖𝑡𝑒𝑚



(1)

Equation (1) shows that item A's support is

determined by dividing the number of item A that

occurs with all items. Meanwhile, the support of

items A and B (two items) is calculated by equation

(2) as follows:

𝑠𝑢𝑝𝑝𝑜𝑟𝑡



𝐴, 𝐵







𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑡𝑒𝑚 𝐴 𝑎𝑛𝑑 𝐵





𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑖𝑡𝑒𝑚



𝑃



𝐴∪𝐵



(2)

The next step after finding all high-frequency

patterns is searching the rule which meets the

minimum of confidence. The confidence score is a

measure of the rule’s accuracy. The highest

confidence, the most substantial relationship

between items in the association rules. Formulas to

calculate the confidence score of two items are as

follows:

𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒



𝐴→𝐵





𝑠𝑢𝑝𝑝𝑜𝑟𝑡



𝐴∪𝐵



𝑠𝑢𝑝𝑝𝑜𝑟𝑡



𝐴





𝑃𝐴 ∪ 𝐵

𝑃𝐴

(3)

𝐿𝑖𝑓𝑡



𝐴→𝐵





𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒



𝐴→𝐵



𝑠𝑢𝑝𝑝𝑜𝑟𝑡



𝐵





𝑃𝐴 ∪ 𝐵

𝑃



𝐴



𝑃𝐵

(4)

Furthermore, a priori algorithm also uses score

lift as a parameter. Lift is a ratio number that shows

how many times it is possible to find an attribute

that occurs with other attributes compared to all

occurrences of the whole attribute. The value

obtained from the lift score shows the strength or

failure of the resulting rules, also supported by the

value of the support and confidence generated

(Melita, et al., 2013). The function to determine the

lift score is written in equation (Riveiro and

Falkman, 2011).

2.2 Dataset

The study uses a limited area and period of AIS

dataset. The selected AIS data from 1

October 2018

to 31

December 2018, which distributed from

latitude S 7,14° to S 8,55° and longitude E 115,29°

to E 116,03° as visualized in Figure 1. There are

over ten thousand records in the raw dataset.

Therefore, we do pre-process covering data cleaning

and data transforming.

Figure 1. Coordinate point selected area

2.3 Methodology

In the data cleaning process, first, we delete data

without MMSI, position, time, course over ground,

speed and ship type. Second, fill the ship type with

the same MMSI. Third, removing multiple data

(exact time and location) and finally removing

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unused attributes in the analysis. Table 1 present

the attributes used in this study.

Table 1. Attributes of research

Attribute Definition Data

MSI Maritime Mobile

Service Identify

Integer

Latitude Latitude ship position Numeric

Longitude Longitude ship position Numeric

Speed Speed of ship (km/hr) Numeric

Course

over

roun

course over ground of

shi

Numeric

Type Type of ship (cargo,

assenger, thanker)

Text

The last pre-processing is data transformation.

We perform a transformation on the attributes of the

ship speed and course over the ground. Based on the

AIS dynamic information standard (Deng, et al.,

2014), we categorized the speeds into five, which

are presented in Table 2. The "Exceptions" speed

category is anomalous data. Usually, the recorded

data is due to recording errors. In this study, we did

not find a ship with more than 99 km/hr speed.

Additionally, based on information from the data

and the weather in the water area, we define the

course over ground attribute into eight categories.

The interval for each category is 45° as written in

Table 3. After pre-processing the data, about 60% of

the data remains for further analysis.

Table 2. Discrete of speed

Range of speed (km/hr) Category

0 – 3 Slow

3 – 14 Medium

14 – 23 High

23 – 99 Very high

over 99 Exception

Table 3. Discrete of course over ground

Interval Cate

360° - 45° North

45° - 90° North East

90° - 135° East

135° - 180° South East

180° - 225° South

Interval Cate

225° - 270° South West

270° - 315° West

315° - 360° North West

3 RESULTS AND DISCUSSION

In this section, the results describe into two

subsections, namely descriptive statistics and

association rules analysis.

3.1 Statistics

The characteristics of the AIS data analyzed include

speed, type, and the distribution of the ship's course

over ground from the clean data obtained in the pre-

processing. The results of the analysis are presented

in Figure 3.

Based on Figure 3, ships with medium speed and

cargo ships have the most significant proportion. We

know that the area used in this study is around the

Lombok Strait, which is a busy trade route with a

high level of ship traffic density. So that the speed

"Medium" - "Low" is the recommended condition to

avoid ship accidents.

Figure 2. Traffic in the Lombok Strait

Besides, the distribution of the course over the

ship's ground shows relatively balanced data. There

are 25% of ships toward the South, 23% of ships

move towards the North, and the rest spread in other

directions according to their destination. Visually,

the marine traffic around the Lombok Strait can be

seen in Figure 2. In this figure, the green-coloured

ships, namely the type of cargo, dominate the water

traffic in the Lombok Strait. Either across the island

of Lombok - Bali, as well as to other islands.

Analysis of Vessel Movements using Association Rules

127

(a)

(b)

(c)

Figure 3. statistics of data based on (a) speed, (b) type, and

3.2 Association Rules Analysis

This study analyses vessel movement data using

association rules that use a priori algorithm method

based on the lift value. Lift is a ratio number that

shows how many times it is possible to find an

attribute with other attributes. Rules formation is

obtained based on item appearance. In this study, the

items in question are the course attributes over the

ground, speed and type of ship. We set two schemes

to compare the performance. The result of each

scheme is described as follows.

3.2.1 Scheme 1

Scheme 1 builds rules with a priori algorithm with

the provisions of the parameter max length = 3,

support = 0.1 and confidence = 0.8. The results are

14 rules. Here are five rules based on the highest lift.

Table 4. The highest five rules of scheme 1

lhs RHS support

confi

dence

lift count

{Latitude=7,

Lon

itude=116

}

{Cours

e=S

}

0.1296 0.8275 3.281 331

{Latitude=7,

Course=S}

{Longit

ude=11

}

0.1296 0.9143 2.937 331

{Longitude=116,

Course=S}

{Type=

Cargo

ship}

0.1495 0.8967 1.504 382

{Latitude=7,

Longitude=116}

{Type=

Cargo

shi

0.1366 0.8725 1.464 349

{Latitude=7,

Course=S

{Type=

Cargo

shi

0.1225 0.8646 1.450 313

Based on Table 4, it can be explained in the first

rule that if the ship's coordinate point is at Latitude =

7 and Longitude = 116, then the course over ground

is South or the ship is from the South direction.

These rules have a lift score of 3.281 with 82.75%

confidence, 0.1296 support, and the number of

activities with the same item is 331 ships. At the

value of lift 1.45, the rules are if Latitude = 7 and

Course = South, then the type of ship is cargo. The

level of confidence for these rules is 86.46%,

followed by support at 0.1225. Visually, the

distribution of parameter values in all rules can be

seen in Figure 4.

Figure 4. Scatter plot of the parameter by scheme 1

Figure 5. Rules pattern by scheme 1

10%

24%

66%

High

Low

Medium

60%

11%

17%

Cargo ship

Other type

Passenger ship

Pleasure Craft

Sailing vessel

Tanker

Tug

12%

23%

25%

11%

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128

Based on Figure 4, the rules with high

confidence have a high lift value (the colour is

getting brighter), but the support value is relatively

small. Conversely, rules with a higher support value

lower the lift value (the colour fades), with a

confidence value ranging from 80% to 90%. The

visualization of patterns between items that often

appear is shown in Figure 5, which can be explained

that items Speed = Medium and Type = Cargo Ship

are items that become result (RHS) indicated by the

direction of the arrow leading to the item.

Meanwhile, the items Course=South, Latitude=7,

and Longitude=116 are required (lhs) indicated by

the direction of the arrow that comes out of the item.

The green colour shows the main item, the red

colour shows the high lift value, and the large circle

size shows a high confidence value.

3.2.2 Scheme 2

Scheme 2 builds rules with a priori algorithm with

the provisions of the parameter max length = 4,

support = 0.1 and confidence = 0.8. The results are

26 rules. Here are five rules based on the highest lift.

Table 5. The highest five rules of scheme 2

lhs rhs support confidence lift count

{Latitude=7,

Longitude=116,

Type=Cargo

ship}

{Course=S} 0.1194 0.8739 3.465 305

{Latitude=7,

Longitude=116,

Speed=Medium}

{Course=S} 0.1170 0.8398 3.330 299

{Latitude=7,

Longitude=116}

{Course=S} 0.1296 0.8275 3.281 331

{Latitude=7,

Course=S,

Type=Cargo

ship}

{Longitude=116} 0.1194 0.9744 3.130 305

{Latitude=7,

Course=S,

Speed=Medium}

{Longitude=116} 0.1170 0.9228 2.964 299

Based on Table 5, it can be explained in the first

rule that if the ship's coordinate point is at Latitude =

7 and Longitude = 116, the type = cargo ship, then

the course over ground is South. These rules have a

lift score of 3.465 with 87.39% confidence, 0.1194

support, and the number of activities with the same

item is 305 ships. Visually, the distribution of

parameter values in all rules can be seen in Figure 6.

Based on Figure 6, the lift value is inversely

proportional to support. Rules with a high lift value

(bright red colour) followed by a support value of

0.12 - 0.13.

On the contrary, the higher the support value, the

smaller the lift value (pink colour). Likewise, the

value of confidence is inversely related to support. It

is known that the rules with the highest confidence

(98%) have a high lift, but the support value is

relatively low compared to other rules.

Figure 6. Scatter plot of the parameter by scheme 2

Figure 7. Rules pattern by scheme 2

The pattern rules for scheme two are presented in

Figure 7. The image is more complicated than in

scheme one because more arrows indicate the

combination of items set for a maximum of 4. There

are six red circles which mean the rules with the

highest lift. Besides that, there are rules with the

highest confidence (the most extensive circle size)

with Latitude = 7, then Speed = Medium.

4 CONCLUSION

The association rules are successful in analysing the

vessel movements. The pattern based on the

frequency of item which occurs together show the

prediction of the next to ship characteristics. The

outstanding lift score is obtained by maximum

length of item = 4, support = 0.1994, confidence =

87.39%. The rule state is if latitude = 7, longitude =

116, type = cargo ship, then course over ground =

South. The result is appropriate to detect the pattern

of vessel motion. It will simplify the officers to

monitor and control traffic lines in Lombok Strait.

Analysis of Vessel Movements using Association Rules

129

Furthermore, it useful for an early warning system to

prevent the accident in the sea trade route.

ACKNOWLEDGEMENTS

The author would like to thank AISITS Surabaya for

providing data sets, and Grant supported this work

for the Department of Business Statistics 2020 ITS

Surabaya.

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