This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.

1. Train Collision Avoidance Using Fuzzy Logic
1.Shashank Vaidya
SDMCET,Dharwad.
Insignia ID:INS1878
Email:sha_mailme@yahoo.com
Contact:9845913876
2.Ali asgar.N.R
SDMCET,Dharwad.
Insignia ID:INS1876
Email:predatorscan@gmail.com
Contact:8867596085
I. Abstract:
In view of the increasing number of traffic accidents
in the recent years, it is noted that the traffic
accidents have assumed the dimensions of a serious
problem. This article proposes a new collision
avoidance method for one of the economical public
transport i.e for the trains. Hence giving priority to
the safety of the passenger. In comparing with the
today’s technology, where there is a man power is
involved , the proposed theory is advanced and
automatic with the help of fuzzy logic.
II. Introduction:
Fuzzy logic is a form of many-valued
logic or probabilistic logic; it deals
with reasoning that is approximate rather than fixed
and exact. Compared to traditional binary sets (where
variables may take on true or false values) fuzzy
logic variables may have a truth value that ranges in
degree between 0 and 1. Fuzzy logic has been
extended to handle the concept of partial truth, where
the truth value may range between completely true
and completely false. Furthermore,
when linguistic variables are used, these degrees may
be managed by specific functions.
The term "fuzzy logic" was introduced with the 1965
proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzy
logic has been applied to many fields, from control
theory to artificial intelligence. Fuzzy logics however
had been studied since the 1920s as infinite-valued
logics notably by Łukasiewicz and Tarski; it is a
popular misconception that they were invented by
Zadeh.
III. Why Use Fuzzy Logic?
Here is a list of general observations about fuzzy
logic:
Fuzzy logic is conceptually easy to understand.
The mathematical concepts behind fuzzy reasoning
are very simple. Fuzzy logic is a more
intuitive approach without the far-reaching
complexity.With any given system, it is easy to layer
on more functionality without starting again from
scratch. Fuzzy logic is tolerant of imprecise data.
Everything is imprecise if you look closely enough,
but more than that, most things are imprecise even on
careful inspection. Fuzzy reasoning builds this
understanding into the process rather than tacking it
onto the end.
Fuzzy logic can model nonlinear functions of
arbitrary complexity.You can create a fuzzy system
to match any set of input-output data. This process is
made particularly easy by adaptive techniques like
Adaptive Neuro-Fuzzy Inference Systems (ANFIS),
which are available in Fuzzy Logic Toolbox
software.In direct contrast to neural networks, which
take training data and generate opaque, impenetrable
models, fuzzy logic lets you rely on the experience of
people who already understand your system.
Fuzzy logic can be blended with conventional control
techniques. Fuzzy systems don't necessarily replace
conventional control methods. In many cases fuzzy
systems augment them and simplify their
implementation.The basis for fuzzy logic is the basis
for human communication. This observation
underpins many of the other statements about fuzzy
logic. Because fuzzy logic is built on the structures of
qualitative description used in everyday language,
fuzzy logic is easy to use. The last statement is

2. perhaps the most important one and deserves more
discussion. Natural language, which is used by
ordinary people on a daily basis, has been shaped by
thousands of years of human history to be convenient
and efficient. Sentences written in ordinary language
represent a triumph of efficient communication.
IV. Block diagram:
Fig.1.0 FLC block diagram.
The block diagram shown in the Fig.1.0 is the fuzzy
logic controller includes fuzzyfier, fuzzy logic
controller, rule base & defuzzyfication.
V. Methodology:
The fuzzyfier does the function of fuzzyfication, i.e it
takes the specified inputs which are crisp value(real
time values).
The fuzzy logic controller calculates the o The
proposed project involves the new field in the field of
engineering i.e fuzzy logic.Which is very simple,
advantageous and even the economical. As day to
day the increasing population has made to increase
the transport facility, and hence increasing the risk
involved in the travelling. Therefore in order to
avoid the accidents, here we are concerned to apply
the fuzzy logic technique to avoid the accidents
between the trains.
utputs according to the rules stored in the rule base
system and provides a ‘Fuzzy’ output value.
The rules & conditions for the system is written in
the rule base.
The output can be taken after the defuzzyfication
which converts fuzzy values back to crisp values.
The first step is to give the input signals. Here the
input signals can be : for example the signal or the
vibrations of the train track or the frequency of the
ground due to movement of train and can be the
vibrations and frequency of the train track with
respect to the load it is carrying.
The fuzzyfier reads the input signals and sends to the
fuzzy logic controller . Here the comparison of the
received signals is done with the predetermined
signals (predetermined rules) which are present in the
rule base section. Hence the condition of the train is
verified for every input at every instant of time and
the output is taken by defuzzification.
In this article consider the input parameters as
distance, speed & frequency & built the system using
fuzzy logic toolbox in matlab software. The rules are
formed and stored in rule base section and the
verification of the output with respect to different
inputs and rules are:
Case-1: As in the Fig.1.1, It shows that the vibrating
frequency of tack is neither low nor high (average)
and the speed of the train is normal hence the µ value
of the output is 0.291 which means the train can just
move with same velocity as it maintained earlier.
Case-2: The condition shown in Fig.1.2 shows the
worst condition. The frequency is very high
(dangerous) and the distance between the two trains
on the same track is very low and the speed of the
train is very high, the output µ value for this input
condition is 0.971 which means that the train should
apply breaks and should stop immediately.
Case-3:The condition in Fig.1.3 shows that the
frequency is less (no other train on the same track)
and the train is moving very slowly, hence the
calculated output µ value is 0.103 which means that
the train should increase its velocity.

3. Fig.1.1 Case-1
Fig.1.2 Case-2
Fig.1.3 Case-3
Advantages:
· Uses linguistic variables
· Allows imprecise/contradictory inputs
· Permits fuzzy thresholds
· Reconciles conflicting objectives
· Rule base or fuzzy sets easily modified
· Relates input to output in linguistic terms, easily
understood
· Allows for rapid prototyping because the system
designer doesn't need to know everything about the
system before starting
· Cheaper because they are easier to design
· Increased robustness
· Simplify knowledge acquisition and representation
· A few rules encompass great complexity
· Can achieve less overshoot and oscillation
· Can achieve steady state in a shorter time interval
(Rao, 1995)
Disadvantage:
The internal vibrations of the train is higher than that
of the normal vibrations produced by it. So it may be
a tedious process to study the vibrations of the train.
Applications:
The Japanese were the first to utilize fuzzy logic for
practical applications. The first notable application
was on the high-speed train in Sendai, in which fuzzy
logic was able to improve the economy, comfort, and
precision of the ride. It has also been used in
recognition of hand written symbols in Sony pocket
computers, Canon auto-focus technology, Omron
auto-aiming cameras, earthquake prediction and
modeling at the Institute of Seismology Bureau of
Metrology in Japan, Can be used for the safety of the
passengers, applied to ships, applied to air vehicles
etc.

4. References:
 Higher vibration modes in railway
tracks at their cutoff frequencies, by
markus.r.pfaffinger.
 Fuzzy decision on optimal collision
avoidance measures for ships in vessel
traffic service.-journal of marine science &
technology.
 Use of railway track vibration
behavior for design & maintenance by
coenraad esveld & amy deman,netherland.
 Fuzzy logic controllers, advantages,
drawbacks. By pedro albertos & Antonio
sala. University of politecnica de Valencia.
 A review of vehicle collision
avoidance by a.n.mngani & m.akyarit in 6th
Saudi engg coference.
 Wikipedia:
http://en.wikipedia.org/wiki/Fuzzy_logic
 http://wiki.answers.com/Q/What_ar
e_the_advantages_and_disadvantages_of_fu
zzy_logic