Fuzzy presentation1
- 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.
Editor's Notes
- #4: Fuzzy logic is a form of many-valued logic in which the true values of variables may be any real number between 0 and 1. It is employed to handle concept of partial truth, where the truth value may range between completely truth and completely false (Novak, V., et al, 1999).
- #10: GM means General Motor Model