International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 4
Number 1
March 2010
55
1 INTRODUCTION
In recent years, shipping is developing rapidly over
the world to meet the growing economic demands.
Ships are getting greater, speedier, and more profes-
sional, and the number of ships improves dramati-
cally. These factors make ports and channels more
and more crowded and complicated, and the resulted
traffic congestion or jam may enhance the risk of
collision and decrease the traffic efficiency in a great
extent. So the traffic congestion degree is becoming
a more and more important parameter for traffic
monitoring and management.
The concept of marine traffic congestion degree
and its calculation, however, have not been well de-
veloped like in the road transportation domain. One
important reason is the difficulty of collecting ma-
rine traffic information. Fortunately, more and more
vessels have been equipped with AIS (Automatic
Identification System), which can frequently broad-
cast own-ship's position, name, speed, course, size,
etc. and so can facilitate marine traffic information
collecting very much(Zhaolin W.&Jun Z.2004).
With position reports of all ships in restricted wa-
ters, we can try to evaluate traffic congestion degree
from the aspect of all ships. However, in restricted
waters, there may be only partial vessels equipped
with AIS while others may not for it’s not compulso-
ry for them according to the regulations. So in the
context of this paper, we classify the ships into two
category, one is the local small boat with or without
AIS equipment, and the other is the general business
ship of certain tonnage (for example, 500GT) or
above, and ships belong to this category are usually
equipped with AIS equipment. To ships of different
category, the same traffic situation may mean differ-
ent traffic congestion degree for they have different
handling capacity and need different size of room
for sailing. In this paper, we focus on the evaluation
of main traffic congestion degree from the aspect of
general business vessels by considering the exist-
ence of the small local boats.
This paper is organized as follows: Section 2 pre-
sents the features of marine traffic congestion, the
traditional methods to determine it and their disad-
vantages. Section 3 proposes a fuzzy reasoning
model to determine the main traffic congestion de-
gree with main traffic flow velocity. Section 4 illu-
minates the method to calculate the main traffic flow
velocity with AIS reports. Finally, main conclusion
and discussion are offered in section 5.
2 MARRINE TRAFFIC CONGESTION
FEATURES AND TRADITIONAL
DESCRIPTION OF TRAFFIC CONGESTION
DEGREE
At present, there is no definite definition of traffic
congestion degree of restricted waters. In fact, ma-
Evaluation of Main Traffic Congestion Degree
for Restricted Waters with AIS Reports
Q. Hu, J. Yong & C. Shi
Shanghai Maritime University, Shanghai, China
G. Chen
Shanghai Maritime Administration, Shanghai, China
ABSTRACT: Traditionally, marine traffic congestion degree in restricted waters is usually deduced from traf-
fic volume or traffic density. Both of which, however, can not be easily and accurately determined and can
not fully reflect the traffic congestion degree. This paper uses the concept of main traffic flow velocity, which
varies with the main traffic congestion from a statistics view, to determine the main traffic congestion degree
in restricted waters. Main traffic flow velocity can be calculated by averaging the speeds of all ships equipped
with an AIS transponder if the percentage of these ships over all vessels in the main traffic is great enough
and they are well-distributed, and a fuzzy relationship is established to determine the traffic congestion degree
under varying main traffic flow velocity. The concept of main traffic flow velocity provides a more intuitive
and accurate way to evaluate the main traffic congestion degree of restricted waters than traffic density and
traffic volume in certain situations, and can be easily implement.
56
rine traffic congestion always exists and can be man-
ifested as:
1 With low sailing velocity and speeding up and
down frequently.
2 With disorder navigation.
3 With too many vessels blocked in the restricted
waters.
From the domestic and international research of
marine traffic, it is found that marine traffic engi-
neers prefer to use traffic density or traffic volume to
determine traffic congestion degree (Yan L. et
al.2007&Yansong G. & Zhaolin W.2001)
.
Traffic density is the instant average quantity of
the vessels per unit area in the surveyed waters,
while traffic volume is the number of vessels
through a certain waters during a certain time period
（Zhaolin W. & Jun Z.2004）. Both traffic density and
traffic volume can not describe the above 1) and 2)
features of marine traffic congestion. Besides that,
there are two other major disadvantages when traffic
density and volume are applied to determine the ma-
rine traffic congestion degree.
1 It is not convenient to get the source data for cal-
culating traffic density or volume. Manual or
semi-automatic traffic survey, radar observation
and aerial photography are generally needed.
2 Ships of different sizes need to be unified when
calculating traffic density or volume, and the uni-
fication can not be done accurately.
So traffic density or volume is not a perfect pa-
rameter to determine traffic congestion degree.
3 A FUZZY EVALUATION MODEL OF MAIN
TRAFFIC CONGESTION DEGREE BASED
ON MAIN TRAFFIC FLOW VELOCITY
It is well known that traffic congestion degree can be
determined by average velocity on road, such as:
smooth traffic means that the average velocity is
more than 30 kilometers per hour, normal traffic
means that the average is between 20 and 30 kilome-
ters per hour, crowed traffic means that the average
is between 10 and 20 kilometers per hour and block-
ing traffic means that the average velocity is not
more than 10 kilometers per hour or maybe nearly
zero (Huapu L. & Janwei W.2003).
Similar to road traffic, when the traffic in restrict-
ed waters is not congested, vessels can sail fast to
the upper limit, while congested, vessels can only
move slowly or even stop. Based on this similarity,
this paper tries to propose a new evaluation method
for marine main traffic congestion degree by using
average velocity of vessels in the main traffic or
main traffic flow velocity. Because the congestion is
a fuzzy concept, a simple fuzzy inference system to
calculate the congestion degree with traffic flow ve-
locity as the input is designed（Khaled H. & Shinya K
2002）
.
3.1 Fuzzy inference system
Fuzzy inference system, based on fuzzy set theory,
fuzzy rule of If-then and fuzzy inference, contains
three parts: 1) many fuzzy rules of If-then; 2) data-
base for defining membership function; 3) inference
engineering to get fuzzy results by input and fuzzy
rules( JANG J S R. 1997). Figure 1 shows the general
structure of a fuzzy inference system.
Inference
rule
Defuzzifi
cation
velocity
Traffic
congestion
degree
Y*
X
Figure 1. General structure of fuzzy inference system
3.2 Building fuzzy sets of traffic flow velocity and
traffic congestion degree and their membership
function
Considering people’s evaluating scale, the fuzzy sets
can be set as: traffic flow velocity= {“very fast”，
“fast”, “middle”, “ slow”， “very slow”}, traffic
congestion degree= {“blocking”, “crowed”, “not
steady”, “normal ”， “smooth”}
Figure 2 shows the membership function of the
traffic flow velocity, where v
e
is the ratio of the cur-
rent traffic flow speed and the free speed and v
e
∈
[0,
1], and V
m
is the ratio of the designed speed or the
recommended speed for prevailing weather condi-
tion and normal traffic and the free speed.
e
v
u
0
1
2
13
m
V
very
slow
very fast
slow
fastmiddle
4
13
m
V
8
13
m
V
m
V
10
13
m
V
12
13
m
V
1
14
5
m
V+
23
5
m
V+
6
13
m
V
Figure 2. Membership function of traffic flow velocity
Given v
e
and the membership function of traffic
flow velocity, we can determine the linguistic value
of v
e
by finding the linguistic value on which v
e
gets
the max membership. For example, if u
very slow
(v
e
)
=0.6 and u
slow
(v
e
)=0.4, the linguistic value of v
e
is
very slow.
Figure 3 shows the membership of traffic conges-
tion degree (TCD), which is quantified between 0
57
and 1, where 0 means traffic state is jam and 1 sim-
plifies that it is very smooth in the waters.
u
0
1
1
blocking
smooth
crowed
normal
not steady
0.12
COG
Figure 3. Membership function of traffic congestion degree
3.3 Fuzzy inference rule of the evaluation
Here the fuzzy inference rule between traffic flow
velocity and traffic congestion degree should be:
− If traffic flow velocity is “very fast”, then traffic
congestion degree is “smooth”, and given v
e,
u
very
fast
(v
e
) = u
smooth
(v
e
).
− If traffic flow velocity is “fast”, then traffic con-
gestion degree is “normal”, and given v
e,
u
fast
(v
e
)
= u
normal
(v
e
).
− If traffic flow velocity is “middle”, then traffic
congestion degree is “not steady”, and given v
e,
u
middle
(v
e
) = u
not
steady(v
e
).
− If traffic flow velocity is “slow”, then traffic con-
gestion degree is “crowed”, and given v
e,
u
fast
(v
e
)
= u
crowed
(v
e
).
− If traffic flow velocity is “very slow”, then traffic
congestion degree is “blocking”, and given v
e,
u
very slow
(v
e
) = u
blocking
(v
e
).
Table 1 shows the mapping relationship between
the fuzzy sets of traffic flow velocity and traffic
congestion degree.
Here velocity has been divided into five grades
and every grade is measured by designed speed Vm,
which has considered influential factors of velocity,
such as, visibility and can change a lot under differ-
ent weather condition.
Table1. Fuzzy set mapping between traffic congestion degree
and traffic flow velocity
___________________________________________________
Grade Traffic Membership of traffic
congestion degree flow velocity
___________________________________________________
1 smooth very fast
2 normal fast
3 not steady middle
4 crowed slow
5 blocking very slow
3.4 Defuzzification
As the output of fuzzy inference system is fuzzy, it
is necessary to map the fuzzy congestion degree into
a concrete value, which is called defuzzification.
There are five defuzzification techniques and the
most typical one is center of gravity (COG) (JANG J
S R. 1997)
, which is used in the context of this re-
search.
For example, if u
very slow
(v
e
) =0.6 and u
slow
(v
e
)=0.4,
then u
blocking
(v
e
) =0.6 and u
crowed
(v
e
)=0.4, and finally
the defuzzification value y* is 0.12 as figure 3
shows.
4 MAIN TRAFFIC FLOW VELOCITY
CALCULATION WITH SPEED
INFORMATION PROVIDED BY AIS
REPORTS
Generally, traffic flow velocity can be calculated by
equation (2), where n means the total number of
ships in an investigated waters and v
i
means the cur-
rent speed of i-th ship.
1
n
i
i
v
v
n
=
=
∑
(2)
When we use equation (2) to calculate the main
traffic flow velocity with the information provided
by AIS reports, we shall note that not all ships in an
investigated waters is equipped with AIS transpond-
er, so the total number of ships can not be acquired.
Figure 4. Schematic diagram of marine traffic, where black tri-
angles stand for the ships equipped with AIS and in the main
traffic, gray triangles present the ships without AIS, while
white triangles signify local traffic ships with AIS
In this paper, we regard each ship with AIS tran-
sponder in the main traffic as a sampling sensor, so
if the percentage of these ships over all ships in the
main traffic is great enough and they are well-
distributed, the average speed of these ships will be
able to reflect the traffic congestion degree.
For example, in Figure 4, we regard the average
speed of all black vessels as the main traffic flow ve-
58
locity. All white vessels are ignored because they are
not in main traffic, and their speeds are not closely
related to the traffic congestion degree for they may
at anchor, berthing, etc.
5 CONCLUSIONS AND DICUSSION
This paper proposed to apply the concept of main
traffic flow velocity to determine the main traffic
congestion degree in restricted waters. Main traffic
flow velocity is calculated by averaging the speed of
all ships equipped with AIS transponders in the main
traffic. A fuzzy inference model was built to deter-
mine the main traffic congestion degree under vary-
ing main traffic flow velocity. Comparing to traffic
volume or density, the concept of main traffic flow
velocity provides a more intuitive and accurate way
to evaluate the main traffic congestion degree of re-
stricted waters in certain situation, and can be easily
implement.
The more percentage of ships equipped with AIS
transponders in the main traffic is, the more reason-
able the evaluating result given by the method pro-
posed in this paper is. For the restricted waters
where the percentage is not determinable, there are
two conditions shall be satisfied before applying the
method proposed in this paper: (1) the percentage of
vessels with AIS transponder over all ships in the
main traffic is great enough, and (2) the ships are
well-distributed. The lower limit of the percentage
and how to determine whether the ships are well-
distributed shall be further studied. Besides that,
traffic volume or density may be combined with
traffic flow velocity to make the evaluation. We also
have plans to apply clustering method to determine
the limits between congested waters and smooth wa-
ters, and to render the marine traffic congestion de-
gree on the Web sea map to facilitate ship owners
and marine safety authorities to monitor the traffic.
ACKNOWLEDGEMENTS
This work was supported by Shanghai Education
Committee under grant No.08YZ107 and by Shang-
hai Leading Discipline Project under grant No.
S30602
REFERENCES
Huapu L. & Janwei W. 2003.Analysis of Urban Traffic Con-
gestion and Dynamic Distinction Method. Beijing: Peoples’
Transportation Press
Jang J.S.R. 1997. Neuro-fuzzy and soft computing. USA: In-
ternational Edition
Khaled H. & Shinya K. 2002. Developing a Measure of Traffic
Congestion: Fuzzy Inference Approach .Transportation Re-
search Record (1802): 77-85.
Yan L. et al. 2007. Analysis and evaluation of navigation safe-
ty influenced by navigation environment in port. Journal of
Waterway and Harbor28 (5):342-347
Yansong G. & Zhaolin W. 2001. Analysis and evaluation on
the danger degree of environment in the channel of Xiamen
port. Journal of Dalian Maritime University 27 (1): 5-8
Zhaolin W. & Jun Z. 2004.Marine traffic engineering. Dalian:
Dalian Marinetime University Press
