This document provides a comprehensive literature review on fuzzy microscopic traffic models. It discusses how fuzzy logic has been applied to traffic models to more realistically simulate driver behavior. The review covers several types of fuzzy traffic models including single-lane models, multi-lane models, and models for traffic signal control. It also summarizes recent research that has used fuzzy logic approaches for traffic simulation and risk assessment related to transportation infrastructure projects.

1. CIV 8331
ADVANCE TRAFFIC ENGINEERING
(ASSIGNMENT)
ISHAKA USMAN
SPS/17/MCE/00053
SUBMITTED TO
PROF. H. M. ALHASSAN
DEPARTMENT OF CIVIL ENGINEERING
FACULTY OF ENGINEERING
BAYERO UNIVERSTY, KANO
MAY, 2018

2. ASSIGNMENT QUESTION
COMPREHENSIVE REVIEW ON FUZZY
MICROSCOPIC TRAFFIC MODELS

3. INRTODUCTION
• The term fuzzy logic was introduced with the
1965 proposal of fuzzy set theory by Lofti Zadeh.
• Fuzzy logic had however been studied since the
1920s, as infinite-valued logic notably by
Lukaisiewicz and Tarski (Standard Encyclopedia of
philosophy, 2008).
• Fuzzy logic has been applied to many fields, from
control theory to artificial intelligent which
among it; traffic models.

4. CONTD.
• The theoretical basis of fuzzy model is that in
the following process, the driver can be
regarded as a complex nonlinear system.
• It controls the process of the following vehicle,
following the preceding vehicle based on the
traffic environment and the information of the
preceding vehicle and the following vehicle
state and so on.

5. CONTD.
• The traditional differential equations models
sometimes cannot well describe the driver’s
psychological and physiological uncertainty
and inconsistency, such as the feeling and
understand.
• However, the fuzzy theory model has some
simple and feasible advantage in dealing with
complex nonlinear problems.

6. STATEMENT OF PROBLEM
• Growing traffic congestion has become one of
the most prior problems of the society in
recent years.
• In populated areas, the existing road networks
are not able to satisfy the demand.
• The construction of new roads is usually not a
solution and often not socially desired.

7. CONTD.
• With these reasons and the great economical
costs, new traffic models gains were
introduced in traffic management and
information systems.
• Due to the high congestion in urban areas,
behaviours of driver and vehicle must be
studied. Hence, fuzzy model is necessary to
minimize the problem.

8. AIM AND OBEJECTIVES
• To understand how fuzzy traffic model is used.
Objectives are:
• To make a review on fuzzy traffic model.
• To know the various types of models in fuzzy
concept.

9. LITERATURE REVIEW
• The first fuzzy-logic model was put forward by
Kikuchi and Chakroborty, who tried to ‘fuzzify’
the GM model.
• The inputs are Δx, Δv and an+1, which are
divided into 6, 6 and 12 ‘fuzzy sets’,
respectively. Moreover, each has a
membership function, for instance, the rule:
• IF Δx = ‘Adequate’,
• THEN an,i = (Δvi + an+1, ixT ) / γ,

10. CONTD.
• Where T = 1 sec (is the reaction time), γ = 2.5
sec (is the expected time of driver to catch up
with the front vehicle).
• Assume Δx ≠ ‘Adequate’, ai will change
according to the membership function.
• Nowadays, several researches are carried out
on fuzzy logic model not based on
membership function while it is the most
important factor of the model.

11. CONTD.
• Hence, the concept of fuzzy logic fits more on
the observation, thinking, understanding, and
decision-making process of human.
• In this model, human is abstracted as a fuzzy
controller, whose inputs are the status
messages of the preceding cars and output is
the decision made through a series of thinking
(Weng and Wu, 2002).

12. TRAFFIC SIMULATION SYSTEM BASED
ON FUZZY LOGIC
• This fuzzy logic method can be used to simulate
environment, design for testing and evaluation of
any fuzzy logic based traffic management system.
• The user could simulate any traffic isolated
intersection or intersection network with multiple
lanes.
• It could also specify input parameters, build fuzzy
rules that control the traffic flow and simulate the
model to monitor the efficiency of model by
noticing the output parameters.

13. FUZZY MODELS
• Traffic Models developed from fuzzy theory
include:
1. Traffic Simulation Model
2. Single-lane Model
3. Multi-lane Model
4. Fuzzy delay-based Estimation
5. Route choice model based with Fuzzy Logic
6. Mode Choice Model with Fuzzy Logic.

14. CURRENT RESEARCHES
• Some recent researches carried out on fuzzy
traffic models are given below:
1. Critical Infrastructure Renewal: A framework
for fuzzy logic-based risk assessment and
micro simulation-based traffic modelling for
assessing the traffic impacts due to
construction related bridge opening delay.
(The research was conducted at Halifax,
Canada in 2016)

15. CONTD.
2. Driver Car-following Behavior Simulation
using Fuzzy Rule-based Neural Network
(Virginia Polytechnic Institute and State
University).
3. A New Approach for Fuzzy Traffic Signal
Control (Pamukkale University, Civil
Engineering Department Denizli / TURKEY).

16. PROBLEM RESEARCH IN THE AREA
1. The capability of traffic simulation to emulate
the time variability of traffic events makes it
matchless facility for capturing complexity of
traffic systems.
2. Taha and Ibrahim (2012) stated that, "there
are many researches which implement fuzzy
but to specific problems and there are many
traffic simulation applications but with no
support for fuzzy logic".

17. FUTURE DIRECTION
• Here are some proposed topics in fuzzy traffic
models:
1. Critical Infrastructure Renewal: A framework
for fuzzy logic-based risk assessment and
micro simulation-based traffic modelling for
assessing the traffic impacts due to
construction related bridge opening delay.

18. CONCLUSION
• Driver’s behaviour simulated more realistically
at many different possible urban conditions
with Fuzzy Logic.
• Fuzzy Logic based traffic models improved
accuracy by overcoming limitations of
traditional methods.
• The fuzzy model is an object oriental model
whereby it can be run as a computer program
for a specific task (e.g. traffic signal control).

19. REFERENCES
• Novak, V. et al, (1999). Mathematical Principles of
Fuzzy Logic Dodrecht: Kluwer Academic.
• Fuzzy Logic Standard Encyclopedia of philosophy
(2008). Bryant University.
• Zadeh, L. A. (1965). Review of Mathematics of
Fuzzy Logic, Fuzzy Systems, World Scientific Press
• Öznur Y. et al (2012). “A stochastic Continuous
Cellular Automata Traffic Flow Model with a
Multi-agent Fuzzy System” A Journal of European
Working Group on Transportation (EWGT), Vol.
54, Page 1350 – 1359, Portugal.

20. CONTD.
• Muhammad A. T. and Laheeb I. (2012). “Traffic
Simulation System Based on Fuzzy Logic” A journal of
Procedia Computer Science 12 Page 356 – 360,
Missouri University of Science and Technology
Washington D.C.
• M. D Jahedul Alam (2016). “Critical Infrastructure
Renewal: A Framework for Fuzzy Logic Based Risk
Assessment and Microscopic Traffic Simulation
Modelling” A Journal of World Conference on
Transport Research (WCTR), Transportation Research
Procedia Vol. 25 Page 1397–1415, Shanghai.