International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 1

Number 2

June 2007

153

Intelligent VTS

W. Filipowicz

Gdynia Maritime University, Poland

ABSTRACT: In this paper the author depicts new approach involving fuzziness in navigational situation

assessment. Nowadays operator at maritime traffic monitoring station is assumed to have access to a great

amount of data. The data comes from different places and multiple of sensors. Properly associated data enable

the operator to approximate congestion for each restricted and considered as vital regions. Ship’s presence

function within a confined area defines a non-empty bounded closed interval. It can be denoted by the earliest

and latest bounds of the closed time interval at a given possibility level. To assess situation within any

confined region one should calculate maximal sum of safety factors present within forecast imprecise slots of

time. Safety factors themselves are also fuzzy, imprecise values.

1 INTRODUCTION

Vessels traffic is monitored using different appli-

ances. Main monitoring aim is to check that

everyone obeys imposed rules and traffic separation

is not violated. The measure introduced within

Vessels Traffic Systems (VTS), mostly used radar

stations, significantly contributed to reduction of risk

of collision and improved environment safety

standards. Nowadays radar surveillance can be

supplemented by other sources of data. AIS

(Automated Identification System) which is about to

be introduced seems rather limited in its ability. The

throughput as well as an overall functionality seems

do not meet expectations related to this new

technology. For this reason there are attempts to

create ad hoc networks to transfer even more data

among ships using wireless transceivers. Dynamic

schemes could include shore hot spot stations to

open access to the internet and transmit data to all

interested parties.

Multiple sources of data create new challenge

regarding data association. The challenge is met by

appropriate but still emerging technology called data

fusion. By means of fusion, different sources of

information are combined to improve the perfor-

mances of a whole system. The most obvious

illustration of fusion is the use of various sensors to

detect objects. Available data could be subject to

various processing and the results of different

procedures may be further combined. Objectives

such as detection, identification and tracking can be

achieved thanks to data fusion. Data fusion produces

high quality, enriched and reliable sets of data. Such

sets are necessary for further processing to create

foundations for decision making process.

Data fusion is a process dealing with the

association, correlation, and combination of data and

information from multiple sources to achieve refined

position and identity estimates for observed entities,

and to achieve complete and timely assessments of

situations and threats, and their significance. These

can contribute to further improvement of the safety

standards in particularly within restricted waters. It is

also assumed that VTS operator is able to have

access to these reliable and enriched data, which

create foundation for implementation and execution

of a policy aimed at prediction and traffic control

within the region.

Avoiding local congestion one can reduce number

of encounters and furthermore potential risk of

154

collision (Filipowicz et al. 2005). To introduce such

measures a few assumptions are to be made. First

there must be all data fused with unlimited data flow

within wide area sea routes schemes. All local

branches of VTSs are to be networked and all

available data regarding traffic along with local

conditions easily exchanged. Second there is a

decision-making body within VTS structures.

Databases are to be implemented and decision

problems to be formulated and solved. The last

comes along with proposal of the set of assessment

criteria and delivering necessary tools to decision

maker.

In order to take adequate decision one has to

compare a handful of parameters of different types.

Basically there are crisp and fuzzy values to be taken

into account. For particular vessel and each route,

she is assumed to take scheduled traffic is an

important factor. For VTS operator traffic

encountered within each restricted and considered as

crucial regions can usually be foreseen. Quality of

the forecast depends on data precision.

To process imprecise inputs one has to introduce

interval arithmetic with possibility level selected.

Ship’s presence within confined area defines a

bounded closed interval of time. It can be denoted by

the earliest and latest limits of a time slot with a

given possibility level. To assess navigational

situation within any confined region one should

calculate maximal sum of safety factors present

within at any moment. Safety factors, which reflect

tonnage of a ship as well as carried cargo, are also

likely to be fuzzy values. Subjective assessment of a

term like “large vessel” should be perceived rather as

a range of values than a single, crisp one.

Data fusion technology will be discussed in the

next chapter. Fuzzy or interval arithmetic will be

shortly presented in the further chapter. In the second

part of the paper fuzziness in the vessels traffic

engineering will be presented.

2 DATA FUSION

Modern VTS logical structure should integrate of

traditional and advanced surveillance appliances,

communications means, computer, and other techno-

logies for purposes of improving navigation safety

standards at waterways. The overall integration

techniques are embraced within data fusion. Data

fusion is a scheme of collecting, processing and

enrichment of informational aspects of available

data. Multiple level model (figure 1) extends from

raw sensor data up to situation refinement and final

decision making.

Level 1: Raw data sources

(radars, AIS, ad hoc networks).

Signals refinement

Level 2: Objects refinement

Level 3: Situation refinement

Level 4: Decision making

Fig. 1. General scheme of data fusion in maritime traffic

engineering

Multi-source data fusion enables deliver

information that is characterized by (Sarma et al.

1991):

− increased confidence - more than one sensor can

detect the same object

− reduced ambiguity - joint information from

multiple sensors reduces the set of hypotheses

about the target

− improved detection - integration of multiple

measurements of the same target increases

possibility of detection

− extended coverage and reliability - one sensor can

work when or where another sensor is out of

order or remains beyond the range

Data fusion comprises four levels. Signal, object

and situation refinement are carried out at the lower

levels. Situation recognition and identification

deliver reliable and adequate set of information for

the highest level where decisions are made and

recommendations issued. Table 1 shows main fusion

levels, their methods and techniques.

Table 1. Main fusion levels, their methods and techniques.

_________________________________________________

Fusion level Methods and main tasks Techniques

_________________________________________________

Level one signal refinement, Kalman filtering

position estimation

Level two object refinement, Bayesian methods,

pattern recognition Dempster-Shafer

reasoning

Level three situation refinement fuzzy logic,

modeling

Level four artificial intelligence, MADM Decision

optimization, Making

best passage (TOPSIS, AHP)

recommendation

________________________________________________

2.1 Level One Fusion

Association of multi-sensor raw data is the main task

for the first level of data fusion. It is supposed to

correlate sets of sensor outputs. As a result the data

can be used to estimate a target’s position, course

155

and velocity. In order to remove noise from sensor

signals many systems employ Kalman filtering

technique.

Kalman filtering produces data that estimate the

targets positions. Consequently smoothed positions

coordinates values enable better estimates of velocity

and course (Linn et al. 1991). The accuracy, the

tolerance of each sensor’s output accuracy can be

estimated and assigned. In such case object’s

approximate position can be roughly forecast.

Kalman filtering delivers ability to define limits

within which an object will be located.

2.2 Level Two Fusion

It is said that at level one sensor signals are refined.

Level two data fusion is considered to be an object

refinement level. It delivers more processed and

meaningful data. To create a model of uncertain

system states by consolidating and interpreting

overlapping data delivered by different sensors

Bayesian decision theory is widely used. Dempster-

Shafer Evidential Reasoning (DSER) is an

alternative to Bayesian approach. It is known as a

generalization of Bayesian method that offers a way

to combine uncertain information from various and

unreliable sources. It works with fuzzy data,

perceived as intervals of confidence instead of

unique probability value.

Neural network technology can be also used at the

second level of data fusion. Neural networks

produce results that incorporate input from various

information sources. A neural network consists of

processing nodes that collect and compare data.

Nodes also called neurons are interconnected and

weights and applied to their outputs, which are then

forwarded to the successor nodes. Usually a neuron

has many inputs, but it has only a single output. It

has attributed equations that define what the output

would be like for given inputs. For neural network

there must be learning scheme. During the learning

period weights are adjusted according to the

stimulation the neurons receive. During the learning

process each neuron must be taught establishing the

proper relation between their inputs and generated

output. The network is taught through the

observations of discrepancies between expected and

achieved results which cause modified weights

assigned to each neuron until an expected behaviour

is obtained.

2.3 Level Three Fusion

The third fusion level is supposed to refine situations

which result from detected objects movement. In the

discussed navigation field it should create picture of

what will take place within particular areas. In

restricted waters with heavy traffic it is important to

avoid local congestions in routes crossing regions.

It is quite often that values cannot be categorized

using strict range limits. For example it is not

practical to expect that a vessel will be crossing an

area starting at time t

1

and ending at t

2

. Instead one

must specify that she will arrive sometime around t

1

and depart from the area at about t

2

. Fuzzy logic is a

type of theory that mathematically describes

imprecision and is widely used at data fusion process.

This level also employs artificial intelligence

methods. Computer Expert Systems emulate the

behavior of a human expert. They consist of two

components: inference engine and the knowledge

base. The inference engine performs search through

available possibilities in order to arrive at

appropriate conclusions. The knowledge base is the

set of facts and rules. These rules are usually in the

form of “IF-THEN” statements. Modern expert

systems are able to cooperate with knowledge bases

using fuzzy logic.

2.4 Level Four Fusion

Level four should be discussed taking into account

specificity of a particular field of interest. There are

a few problems, which still remain unsolved, in

vessels traffic engineering. First is a VTS supervisor

problem when he is asked for advice on best

possible passage for particular vessel. The problem

is like “I am a VLCC scheduled to reach reporting

point at some time. Please advise me the best route

or an option for the passage. Should I delay in order

to pass disturbed as little as possible”. The question

is probably addressed to the VTS operator of the

local control station. At the other side the advisory

body of a VTS is supposed to be interested in such

sporadic requests, but it also should be engaged in

everlasting job of traffic allocation in order to avoid

local congestions.

3 FUZZINESS

Often it is desirable to process imprecise or

approximate values. Fuzzy numbers are useful when

dealing with imprecision (Kaufman 1991). Fuzzy

numbers are sets of the real figures that are treated as

intervals. Their geometrical images are of triangular

or trapezoid shapes and are called as membership

functions. Triangular fuzzy value is referred as to

triple of figures (a, b, c), trapezoid one as to quad (a,

b, c, d). Fuzzy values express possibility of being

within given range of values. They start from zero

(lack of possibility) and reach maximum possibility

156

level equal to one. Possibility is different than

probability. Cumulated, integrated probability

distribution must produce one as a final result. The

requirement is not valid when dealing with

membership functions.

Arithmetic of fuzzy values is related to the

possibility level (α) and is based on α-cuts. The

α-cuts of fuzzy numbers represent possibility levels

and are closed intervals of real numbers.

Mathematical operators on fuzzy values are applied

to the boundary values of α-cuts. The idea of

exploiting this fact delivers straightforward

analytical method of dealing with non-linearity as a

result of multiplication of a fuzzy values (Filipowicz

2006).

3.1 Fuzzy Safety Factors

Traffic should be classified taking into account gross

tonnage of a vessel and a kind of cargo she has on

board. Safety factors have been introduced to enable

classification of vessels. In general approach

environmentally dangerous freight and huge tonnage

increase the factor. As it was proposed the factor

vary on an integer scale such that the higher the

number the more serious the consequences of an

accident. Small value was assigned to small craft

without dangerous cargo. The largest value was

reserved for huge crude carriers. It was assumed that

safety factor is easily assigned to every ship and

classification is free from any ambiguity. Since small

and huge are imprecise linguistic terms they should

be treated as fuzzy values. Suggested assignment of

imprecise, fuzzy safety factors to selected classes of

crafts is presented in table 2.

Table 2. Fuzzy safety factors assignment.

__________________________________________________

Cargo Tonnage of craft

Small Medium Large Very Large

__________________________________________________

ND SF (0, 0, 1) (0, 1, 2) (2, 3, 4) (4, 5, 6)

Abr. S M L VL

k 1 2 3 5

MD SF (3, 4, 5) (5, 6, 7) (6, 7, 8) (8, 9, 10)

Abr. S&MD M&MD L&MD VL&MD

k 4 6 7 9

D SF (7, 8, 9) (9, 10, 11) (10, 11, 12) (12, 13, 14)

Abr. S&D M&D) L&D VL&D

k 8) 10 11 13

VD SF (11,12,13) (13,14,15) (14, 15, 16) (15, 16, 16)

Abr. S&VD M&VD L&VD VL&VD

k 12 14 15 16

__________________________________________________

General scheme of assignment is based on four

classes of ship’s tonnage: small, medium, large and

very large. There are four categories of cargo:

normal (ND - no dangerous), mildly dangerous

(MD), dangerous (D) and very hazardous (VD).

Table 2 contains proposal of assignment. Safety

factors (SF) should be divided by 16 for the sake of

normalization. Abbreviations used in examples

included in the paper as well as k value to be applied

with formula 1 are also presented in the table 1.

Final assignment embraces distortion caused by

supremacy of tonnage over cargo for the adjacent

groups of carried load. Very large vessel without

hazardous cargo has greater factor then small ship

with mildly dangerous material on board.

For given k, indicating number included in table 2

normalized fuzzy safety factor can be calculated

using formula (1).

classesofnumbertoequalisnand

n

wwhere

nkforw

nkforwkwkwk

kforw

SF

c

c

c

ck

1

1

]1,1,1[

1]*)1(,*,*)1[(

1],0,0[

1

1

111

1

−

=

=−

<<+−

=

=

(1)

3.2 Fuzziness in maritime traffic engineering

Whenever restricted area passage is considered

traffic encountered at routes crossings is to be taken

into account as important factor. Awareness of other

ships significantly increases wherever collision

avoidance is hampered. To assess navigational

situation within confined areas approximations

regarding all scheduled traffic are to be taken into

account.

Due to unforeseen deviations from intended track,

bad estimation of main engine performance and

collision manoeuvres seafarers always use estimated

time of arrival. For the same reasons ship’s presence

within any area should be treated as trapezoid fuzzy

value. The values consist of estimated earliest and

latest time of arrival as well as earliest and latest

possible time of departure from the region. Situation

within confined area are vital from safety point of

view. Figure 2 presents example with a few crafts

that are scheduled to pass restricted area. There are

four vessels that are very likely to encounter within

the region where any collision avoidance manoeuvre

is seriously hampered. Vessels types were classified

as: S&D, L&D, S&MD, S&D (see table 2). Intended

courses of the vessels are shown at figure 2. Sea and

weather condition along with tonnage and speed of

each craft are given and subsequently fuzzy

timetable of crossing the area were estimated,

example results are presented at figure 3.

As it was already mentioned time frame of ship

crossing an area can be defined by trapezoid fuzzy

value. The membership function starts at the earliest

157

time of arrival and ascends to the appropriate latest

moment. This part of the function represents

entering phase, its inclination depends on the initial

distance from the area, weather condition, tonnage as

well as on propulsion ability of a particular craft.

Collision avoidance manoeuvres (if any) introduce

further delays. Right hand side of the function

represents departure phase and consists of a leg

joining earliest and latest possible time of leaving

the region. Figure 3 shows example membership

functions for situation presented in figure 2.

S

1

S

2

S

3

S

4

Fig. 2. Vessels that are likely to encounter within confined area

create potentially dangerous situation

Let us consider situation presented at figure 2.

We assume that one of the vessels marked as S

2

seeks for advice on best passage option (route to be

taken and/or time frame suggested). In the presented

situation, according to the COLREGS regulations

(see International Maritime Organization website

www.imo.org/Conventions/ for details), she is

supposed to be give-way with respect to S

3

and S

4

.

Her status referring to S

1

is stand-on. The status of

the vessel of interest with respect to each another

yields fuzzy weight factor. Table 3 embraces all

close approach situations and suggested normalized

fuzzy weight factors.

The most uncomfortable is crossing encounter

with give-way (as stipulated by COLREGS) status.

The potential of the situation gets even worse where

there is confined room to carry out collision

avoiding manoeuvre. For this reason respective

weight coefficient is the highest one.

Table 3. Close approaches and their fuzzy weight factors.

________________________________________________

Vessel status and its

Encounter type abbreviation Fuzzy weight

________________________________________________

Crossing Give-way, CGW (0.8, 1, 1)

Crossing Stand-on, CSO (0.4, 0.6, 0.8)

Overtaking Give-way, OGW (0.1, 0.3, 0.5)

Overtaking Stand-on, OSO (0, 0.1, 0.2)

Head-ons Give-way (0, 0, 0.1)

(each vessel), HO

_______________________________________________

Abbreviations used at figure 3 stand for:

A

α

l Si

- earliest time of arrival of the ship S

i

to

the given area taking into account α

possibility level

A

α

u Si

- latest time of departure of the ship S

i

from the given area taking into account

α possibility level

f

L

Si

(t), f

R

Si

(t) - left and right hand side boundary of the

presence function for ship S

i

(linearity

assumed)

f

Si

(t) - overall presence function for ship S

i

within given area

t

m

- time for which maximum fuzzy

congestion is found

ship

f

S3

(t)

time

S1

S2

S3

f

S1

(t)

α

f

S2

(t)

α

1Su

A

α

1Sl

A

f

S2

L

(t)

f

S2

R

(t)

t

m

S4

f

S4

(t)

Fig. 3. Ship’s presence within restricted area can be perceived

as trapezoidal fuzzy values. To assess passage condition one

has to scan entry phase and staying within time slot

Table 4. Navigational condition assessment for example area.

__________________________________________________

Ship Fuzzy SF f

Si

(t

m

) Status/

α

-cuts

Weight of the product

__________________________________________________

S

1

(S&D) (7, 8, 9) 1 CSO/ [0.145, 0.436]

(0.4, 0.6, 0.8) [0.192, 0.367]

[0.244, 0.303]

0.273

S

2

(L&D) (10, 11, 12) 1 own ship/ [0.727, 0.909]

(1, 1, 1) [0.764, 0.873]

[0.800,

0.836]

0.818

S

3

(S&MD) (3, 4, 5) 1 CGW/ [0.073, 0.273]

(0.8, 1, 1) [0.112, 0.236]

[0.157, 0.200]

0.182

S

4

(S&D) (7, 8, 9) 0.8 CSO/ [0.223, 0.436]

(0.8, 1, 1) [0.281, 0.407]

[0.335, 0.378]

0.363

__________________________________________________

[

1.178, 2.055]

TOTAL [1.349, 1.883]

[1.536, 1.717]

1.636

__________________________________________________

158

To assess passage condition for given vessel one

has to scan at least her entry phase and „staying

within” time slot. The slot for the situation presented

in figure 2 is marked in figure 3 with rectangular

shape. Numerical calculation for the situation is

included in table 4. Column „

α

-cut of the product”

in this table contains boundary values for possibility

levels α respectively equal to 0, 0.4, 0.8 and 1.

Final result shows total non normalized and non

regular fuzzy value. To compare such values one has

to normalize and defuzzify them. Defuzzification

converts imprecise intervals into crisp value. Many

fuzzy number ranking methods can be used.

However, no one can rank fuzzy numbers

satisfactorily in all cases and situations.

3.3 Membership functions estimation

To foresee encounter numbers a timetable of arrival

at given points are to be constructed for each

scheduled vessel. Timetable of passage, for each

vessel, and for given area is a vector of fuzzy slots,

which are quads of values that define membership or

“presence in the region” function. Earliest arrival

time (AlE) and the latest departure time from the

area (AuL) of the particular vessel are reference

values that create a time frame. The frame is to be

scanned to evaluate crossing condition.

Shapes of the presence functions, associated with

difference between earliest and latest moments of

arrival or departure primarily depend on necessary

deviation from the prescribed trajectory. To foresee

what will take place within given area one has to

construct (or learn) all presence functions.

Approximate numbers of collision avoidance

manoeuvres are to be counted since they influence

inclinations of ascending and descending slopes of

the functions. These can be estimated based on

simulations. Modeling and simulation computer

environment is necessary for implementation of the

discussed idea.

Basic assumptions of the environment concept

embrace:

− there is a module with interface enabling defini-

tion of the routes scheme (arrival and turning

areas). Route consists of legs linking turning

areas

− there is an interface enabling input of initial

positions and intended route for all crafts

− there is an interface enabling ship domain(s)

definition and selection, there must be database of

domains available

− decision regarding collision avoidance manoeuvre

is based on domain penetration by another vessel.

Adequate manoeuvre, as stipulated by

COLREGS, is carried out if required. There must

be an option of passing through without looking

at others (no collision avoidance manoeuvres

carried out)

− Close quarter approaches are classified and

recorded, all data necessary for further analysis of

ships involved in close approach are also stored

− movement along prescribed trajectory is double

screened random Markovian process.

For the sake of membership function estimation

all initial positions of all scheduled traffic for given

moment, using all available sources and techniques

must be calculated. Schedule traffic destinations and

intended or assumed routes are to be fixed. Average

values of their engines performances must be

gathered.

Two steps of analyses „for presence within”

functions estimation are suggested. At first earliest

arrival times are calculated and close quarter

situations registered. Shortest possible paths

assuming no violation of the separation schemes are

taken into account. During simulation with collision

avoidance option switched off encounters are

detected when safe distance limit is violated. All

close approaches are recorded for further analysis.

Data of ships involved in close approach are also

stored. Two ships are registered being involved in

close approach when it first occurs, their subsequent

mutual positions are not considered unless category

of encounter is changed. Categories list of encoun-

ters embrace: meeting, overtaking and crossing,

which is further subdivided regarding angle of

crossing.

At the next step latest arrival times are estimated.

Selected domain and all encounters are taken into

account and numbers of collision avoidance mano-

euvres are estimated for each of the vessels. It is

assumed that necessary collision avoidance is carried

out whenever adopted domain is penetrated by

another vessel. Such manoeuvre influences presence

function within all regions remained for passing

along given route. All these lead to estimation of

latest moments of presence functions.

4 FINAL REMARKS

Data fusion approach to deal with multiple data

sources in vessels traffic engineering was briefly

presented. Navigational situation within restricted

regions were characterized using fuzziness. Ships

fuzzy safety factors related to gross tonnage and sort

of carried cargo were proposed. Presence within

confined area was also considered as fuzzy set.

Membership function learning method was also

discussed. Arrival and departure from selected routes

crossing areas are trapezoidal imprecise values.

159

Membership functions are to be learned for

particular region, weather condition and each class

of vessels. The imprecise, approximate data were

used to assess navigational situation within routes

crossing area. Product of fuzzy factor and imprecise

weight creates non linear result. To enable

calculations the α-cuts proved to be helpful.

Having at his disposal reliable and fused large

quantity of data and appropriate software tools VTS

operator seems to be able to forecast traffic

congestion within each confined region. Traffic

encountered inside such area is important and

contributes to overall safety standards. Adequate

methods for building hierarchy among alternatives

with widerange of parameter types have been

implemented and discussed by Szlapczynska (Szlap-

czynska 2005).

Multi criteria problem faced by VTS control

station operator was also considered by the author

(Filipowicz 2006). Practical case of decision making

in vessels traffic engineering was presented.

Example included there dealt with system of routes

with rather heavy traffic with one of the vessels that

sought advice on best passage. Best option was

calculated using extended multi-criteria decision aid

software.

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