Fuzzy Traffic Light Methods
W. Silvert, IPIMAR, Portugal
P. Fanning, R. Halliday,
and R. Mohn
Why are we doing this?
vThe first question to be asked about any
approach is why it is needed.
vSGPA Term of Reference D is to revise
the description of PA concepts to make
them more intelligible for non-fishery
vThe Traffic Light Approach is one of the
types of descriptions currently under
investigation to simplify the process of
The Traffic Light Method
vThe Precautionary Approach (and Risk
Management in general, not just for
fisheries) requires that masses of
complex data be presented clearly to
managers, fishermen and other
vThe Traffic Light Method is an easily
understood way of presenting information
about stock conditions.
Indicators & Characteristics
vWe speak of Indicators, which are basic
properties of the system, and
Characteristics, which are integrated
variables representing several Indicators.
vAbundance is a typical Characteristic,
since it represents the result of
combining several Indicators, such as:
ØResearch Trawl data
ØCatch per Unit Effort
Standard Traffic Lights
vEach Indicator or Characteristic is
represented by a single traffic light, red,
yellow or green in the standard traffic
vThere is no smooth transition, just two
sharp lines separating red-yellow and
vThe meaning of the lights can be very
sensitive to the location of these cuts.
Example: 4VsW Cod
vA “crisp” traffic light indicator
Øsharp transitions between colours make
positioning the boundaries very critical
ØA lot of information is lost
Criteria for Improvement
The objective is to develop a more
general approach with the following
vThe most serious problem with the
standard traffic light method is the way
that the lights change discontinuously
when the Indicators change smoothly.
vThere is general agreement that there
must be a more gradual representation of
the significance of changing indicators.
vA less obvious point, but one which is
clearly relevant to fisheries management,
is the need to represent the degree of
uncertainty in the interpretation of
Indicators, and to provide a mechanism
for expressing conflicting evidence or
It is also clear that not all Indicators are
equally significant. They can be:
vOf varying accuracy
vOf different relevance
vOf dubious value
vNew and untested
vMost alternatives to the standard traffic
light method use some sort of averaging
to show that an Indicator is on the
border between red and yellow or
between yellow and green.
vOne example is using intermediate
colours, such as orange between red and
Fuzzy Traffic Lights
vFuzzy Sets offer one way to improve the
standard traffic light method.
vWith fuzzy traffic lights an Indicator
can correspond to more than one light.
vFor example, instead of using orange to
show that an Indicator is on the red-
yellow boundary, we can simply show both
red and yellow lights.
Advantages of Fuzzy
vFuzzy traffic lights are continuous, we
can switch between colours gradually to
achieve higher resolution.
vFuzzy traffic lights show uncertainty if
we illuminate several lights at once.
vFuzzy traffic lights can be weighted to
show relative importance of indicators.
vThe key idea behind Fuzzy Set Theory is
that something can belong to more than
one set at a time.
vWhen we say that a light is red, that
means that it belongs to the set “red”.
vWith fuzzy sets we can have a light that
is 50% in set red and 50% in yellow.
vLet the amount of each light displayed vary
with the level of the indicator
vUse a combination of colours
Øgradual transitions show uncertainty and
contain more information than solid colour
Note that some bars have multiple colours
Application to Haddock
vNote how much data is included on this
figure, and how easy it is to see a pattern
Application to White Hake
vWe have no VPA results, but we still
can present an assessment
Uncertain Reference Levels
vWide yellow zone reflects uncertainty
vThe use of Fuzzy Traffic Lights to
represent stock status means that we
also use fuzzy rules to make management
vSome typical (and familiar) fuzzy rules:
ØIF it feels cold THEN light a fire
ØIF you are hungry THEN eat something
vFuzzy rules are like crisp rules:
ØIF the temperature falls below 14.7º C THEN
switch on the heater
Fuzzy Control of Fisheries
vFuzzy rules are of the form:
IF (condition) THEN (act)
Displaying Fuzzy Lights
vThere are several ways to show a fuzzy
vBubble charts, which look a lot like real
vPie charts, which display information
vStacked bar graphs, which are less
familiar but very effective
vA Bubble Chart looks
like a regular traffic
light, but the sizes of
the ”lights” are
proportional to the
membership in each
of the three sets,
red yellow & green.
vA pie chart looks less
like a traffic light,
but it gives a more
of how much of each
light is lit,
vThe area of each
slice represents the
Stacked Bar Graphs
vA stacked bar graph
is somewhat like a
traffic light with
vThe area of each part
of the bar represents
the membership in
Choosing the Display
vThe bubble chart resembles traffic lights
most, but it does not give a good sense of
the quantitative information about
vThe pie chart and the stacked bar graph
both represent the relative memberships
vThe bubble graph does not give a good
idea of the relative weights of the
vBy varying the diameter of the pie charts
or the width of the bar graphs we can
show the relative importance of different
vAt present weighting has not been well
implemented in trial applications and it is
difficult to achieve agreement on it.
Comparison of Pie Charts
Comparison of Bar Graphs
vTraffic Lights offer a
clear way to present
complex fisheries data.
vFuzzy Traffic Lights
provide more information
with little loss of clarity.