Nowadays, traffic congestion has become a major problem in most developed countries such as Malaysia. Roads are the primary liaison between one place to another place. Large cities are places where traffic jams have become common and something that must be faced by the road users daily. With the increasing number of vehicles, road system planning is very important and a priority task. Failure to planning a good road system and efficient would worsen this problem. Road junction with traffic lights must have a system that is efficient and also intelligent to make the decisions based on fluctuations in the number of vehicles through the intersection. Most of the traffic light is controlled by a controller called the Fuzzy Logic Controller System, instead of using manual methods such as policemen, pre-timed traffic signal controller, ext. However, control system based on fuzzy logic controller, only widely used at single intersection. The purpose of this study is to design a system called "Intelligent Fuzzy Traffic Controller" to be adopted at the two intersections which is located nearby and has a high density of vehicles. This fuzzy logic controller will be design using MATLAB or Simulink software environment. This traffic signal controller consists with two main parts: (i) Fuzzy Phase Selector and (ii) Fuzzy Green Extender. Further, elaboration of this controller is discussed in Chapter 3. The simulation on proposed controller will be built together with actuated traffic signal controller to compare and evaluate the performance.
TABLE OF CONTENTS
ABSTRACT i
DECLARATION ii
LIST OF FIGURES v
LIST OF TABLES vi
LIST OF APPENDIX vii
LIST OF FIGURES
FIGURE TITLE PAGE
LIST OF TABLES
TABLE TITLE PAGE
Table 1: Waiting time comparison for 1000 vehicles (Royani et al. 2010). 12
Table 2: Comparison between fixed-time and Real-time based system. (Singh et al. 2009) 14
Table 3: Performance of Fuzzy Traffic Controller AND Actuated Traffic Controller ( Azura Che Soh et al. 2010) 31
LIST OF APPENDIX
APPENDIX TITLE PAGE
APPENDIX A: GANTT CHART: SEMESTER 3 34
APPENDIX B: GANTT CHART: SEMESTER 4 35
CHAPTER 1
INTRODUCTION
1.1 Introduction
Fuzzy logic is used in system control and analysis design because it shortens the time of engineering development and sometimes in the case of highly complex systems, is the only way to solve the problem. Dr. Mamdani, London University U.K., stated firmly and unequivocally that utilizing a fuzzy logic controller for speed control of a steam engine was much superior to controlling the engine bay by conventional mathematically based control systems and logic control hardware (Abbas et al. 2009). Using the conventional approach, Dr. Mamdani found that the extensive trial and error work was necessary to arrive at successful control for a specific speed set-point. Further, due to the non-linearity of the steam engine operating characteristics, as soon as the speed set-point was changed, the trial and error effort had to be done all over again to arrive at affective control. This did not occur with the fuzzy logic controller, which adapted much better to changes, variations and non-linearity in the system (www.fuzzy-logic.com).
Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, based on priori communication, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions (Abdy, 2006).
Traffic controllers that are able to think like the way of human thinking are designed using Artificial Intelligent such as fuzzy logic. The fuzzy controllers have the ability to adapt to the real time data from detectors to perform constant optimizations on the signal timing plan for intersection. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. In adaptive signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult (Abdy, 2006). For that reason, the adaptive signal control in most cases is not based on precise optimization but on the green extension principle.
In traffic control system, isolated intersection control is the basis. Isolated intersection control includes pre-timed control, actuated control, intelligent control and so on (Yujie and Zhoa, 2010). The pre-times traffic control is a control method that makes a fixed time plan basing on the historical traffic data collected at an isolated intersection. As is well known, the traffic system is a dynamic system, which great randomness in real-time traffic, so the pre-timed control hardly adapts to the dynamic condition.
Hypothesis and principles of fuzzy traffic signal control is used to maximize the efficiency of the existing traffic systems. However, the efficiency of traffic systems can even be fuzzy. The modern programmable traffic signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy controllers have proven effective in controlling a single traffic intersection, even when the intersection is somewhat complex. In this research, further development of multiple intersections took place by adopting fuzzy logic based on traffic signal control without turning vehicles, phase sequence and time determination.
1.2 Problem Statement
Intersections are common bottlenecks in roadway systems. The traditional traffic control system based on controller fixed timing representation of policemen, are generated based on historical data and experiences to create optimized timing plans. This however are no longer the ideal solution for traffic flow management at intersections due to fluctuating traffic volumes and the ever increasing number of vehicles also the number of intersections on the roadway. So that, the intelligent traffic signal control make the current roadway system operate more efficiently and effectively without building new roadways or widening existing roadways which are often impossible due to scarce land availability, cost increasing and greenhouse effect. The intelligent traffic controller is developed based on the waiting time and vehicles queue lengths at current green phase and the other phases. The controller controls the traffic lights timing and phase sequences to ensure smooth flow of traffic with minimal waiting time, minimal queue length and minimal delay time. The existing intelligent controller such as fuzzy traffic controller, are proven can reduced the consumer travel times on roadway for a one intersection. With the increasing the number of intersection, this intelligent traffic controller become more complex and difficult to design also to implement it. The shorter the gap between intersection, the more complex the controller is to build. Therefore, integrating traffic system at more than one intersection is necessary, so that they work synchronously and intelligently to ease traffic flow at any one of those traffic directions.
1.3 Research Objectives
The research objectives of this project are:
To analyze existing fuzzy logic method in traffic management for a single intersection.
To design a fuzzy logic control algorithms for intelligent traffic controller system at double intersection.
To perform numerical validation of the proposed design using simulation.
1.4 Scopes of Project
The scopes of this project are:
Analysis of existing method in traffic management for a single intersection. Using Fuzzy Rule Base System, many researchers designed the traffic controller for single oversaturated intersections with many parameters.
This proposed paper will describes and designs a fuzzy rule and fuzzy logic control algorithms. The intelligent of traffic controller is to design for double intersections. The intelligent traffic controller is developed based on the waiting time and vehicles queue lengths at current green phase and the other phases.
MATLAB is the sole program used in implementing the whole project. The traffic signal controllers will be design using SIMULINK block diagram provided by MATLAB. A graphical user interface (GUI) tool is used to build the fuzzy rules and controllers. The simulation of Actuated Traffic Controller and Fuzzy Traffic Controller will be developing to compare the performance.
1.5 Limitations of Project
The limitations of this project are:
The isolated intersection model used consists of single lane in each approach.
The traffic signal controller will allow the traffic with go straight, left and rights turn.
No turn back or U-turn in traffic flow
The controlling of emergency vehicles and pedestrians is not applied for this project
CHAPTER 2
LITERATURE RIVIEW
Traffic flow is usually characterized by randomness and uncertainty. Fuzzy logic is known to be well suited for modeling and control such problems. Since first attempt made to design Fuzzy Traffic Controller by Pappis and Mamdani in 1977, there are many follow researchers done almost the same method with objectives is to enhance or improved the Pappis and Mamdani approach. Pappis and Mamdani (1977) applied fuzzy logic for an isolated intersection and control by a two-phased signal. Pappis and Mamdani (1977) also compared the Fuzzy Logic Model (FLM) with the traffic-actuated signal control with considering average delays of vehicles. The result is decreasing in delays about 20 percent (Murat and Gedizlioglu 2003). Following this encouraging and positive result, then, there are many researches take in action to create better and efficient traffic signal controller. Many models and approaches exist since then.
One of the studies conducts by Abbas, Sheraz and Noor (2009) are using a fuzzy based control system to controls traffic signal for regulating traffic on oversaturated intersection of left and right turns. This research is enhancing from the previous research done by L. Zhang and H. Li which is develop the same controller but did not considering the left and right turns. Based on Pappis and Mamdani work, Abbas et al. (2009) develop the controller that determines whether to extend or terminate the current green phase based on a set of fuzzy rules. The fuzzy rules compare traffic conditions with the current green phase and traffic conditions with the next candidate green phase. The emergency vehicles are also considered in Abbas research. The Figure 1 shows the flow diagram of Abbas et al. (2009) traffic controller.
Figure : Fuzzy Traffic Signal Control Flow Diagram created by Abbas et al. 2009
Meanwhile, the other research was also considered another approach such as multi-phase controller. Like Murat and Gedizlioglu (2003), studied on fuzzy logic multi-phased signal control (FLMuSiC) model for isolated signalized intersections. This model comprises two systems which are for green phase time arrangement and the other phase for traffic volumes sequences as shown on Figure 2.
Figure : Structure of the FLMuSic Model (Murat and Gedizlioglu 2003)
The comparison of FLMuSiC controller with existing traffic-actuated simulation and aaSIDRA vehicles actuated model are resulting to the better performance and very encouraging (Figure 3).
Figure : Comparison of the FLMuSiC model with the traffic actuated simulation and the aaSIDRA vehicle actuated (Murat and Gedizlioglu 2003)
As Adaptive Dynamic Programming (ADP) is more popular and acceptable with providing a feasible and effective way to achieve optimal control performance, more studies are being conducted. Yujie and Zhao (2010) also had done a study on fuzzy traffic signal controller. The main objective of this study is to build a new simple ADP algorithm to control the alternation of traffic phases of an isolated intersection. But, before the practical application of the control algorithms, it is critical to evaluate their performance in a simulation environment. Microscopic traffic simulation software named Traffic Software Integrated System (TSIS) is adopted on Yujie and Zhao (2010) study. A simple road network is built (Figure 4). Then, the algorithms are applied in the same intersection, groups of random simulations are conducted and several Measures of Effectiveness (MOEs) are chosen to assess the performance of different algorithms. The simulation results is, it keeps the lowest average delay time, maintains a highest average speed for total vehicles discharged from the intersection, and keeps smallest in control delay time for the left turn vehicles in the intersection. The proposed ADP does not have any phase failure in the whole simulation compared to the one for actuated control and one hundred and eighty-four for the pre-timed control. For percent stops, ADP control has a better performance than the pre-timed control, too. And lags, only nearly four percent behind the actuated control. Figure 5 and 6 shows the graphical result of Yujie and Zhao (2010) study.
Figure : A signalized isolated intersection (Yujie and Zhao 2010)
Figure : MOE: Average Delay.
Figure : MOE: Control Delay Left vehicle.
Source: Yujie and Zhao 2010
Royani, Haddadnia and Alipoor (2010) also develop the traffic controller using fuzzy logic method. The studied has been conduct on isolated intersection with four-legs and three lanes in each leg and ran the simulation using MATLAB 7.8 software with considering a real situation. The result is compares with fixed time control system and resulting improvement about 40 percent for decreasing waiting time for 1000 vehicles. Table 1 shows, the simulation results for a waiting time in fixed time control and fuzzy intelligent control for 1000 vehicles in four lanes of intersection.
Table 1: Waiting time comparison for 1000 vehicles (Royani et al. 2010).
Control Method
Average Waiting Time
(second)
Fixed Time Control
330s
FNN Control
178s
With the same objective as to decrease delay times and extend the green time for saturated intersection, Leena, Tripathi and Arora (2009), proposed own generic algorithms model for traffic controller. The objective of Leena et al. (2009) study is to develop the emulator for representation of traffic conditions at an isolated intersection with the features such as Graphical User Interface (GUI), random generation of vehicles, random vehicular direction, collision avoidance, surveillance of traffic conditions at specific intervals and traffic signal with minimum green length duration. Figure 9 shows the traffic emulator model developed by Leena et al. (2009).
Figure : Modeled diagram of traffic signal control (Leena. S et al. 2009).
The traffic emulator model is to implement a fully concurrent emulator of car and traffic signal lights interaction at an intersection. This model consists of four traffic lights for controlling straight and right turn traffic while the left turn is free. The researchers also comparing their model with fixed time and real time based traffic systems which improved significant performance increase of 21.9 percent in case of real time based system as shown in Table 2.
Table 2: Comparison between fixed-time and Real-time based system. (Singh et al. 2009)
In addition, Zarandi and Rezapour (2009) had designed multi-level fuzzy signal control system (FSCS) as presented in Figure 10. Within this controller, fuzzy phase selector function is working on the sequence level while fuzzy green phase extender function belongs to the green light extension.
Figure : Fuzzy traffic signal control in different levels (Zarandi & Rezapour 2009)
There are three main steps in Zarandi et al. (2009) traffic light controller:
Step 1: System receives the necessary data from the detectors of intersection.
Step 2: Determines if current green phase should extend or terminate (fuzzy green phase extender function).
Step 2.1: If current green phase should extend, then goes to Step 1.
Step 2.2: If current green phase should terminate, then goes to Step 3.
Step 3: Determines the next green phase (phase selector function) and goes to Step 1.
The phase selector determines the most suitable phase order for the traffic condition and will accomplish by selecting the next green phase. The traffic situation is monitored continuously and when the green phase is terminated, the decision of the next phase is updated. Figure 11 presents the phases and the basic phase order of the intersection model, in which the control function is tested, used on Zarandi et al. (2009). If the current green phase A is to be terminated, the phase selector decides whether to launch next the phase B or the phase C (Zarandi et al. 2009).
Figure : Basic phase sequence of the signal control at the test intersection. (Zarandi et Al. 2009)
Then, the proposed Zarandi et al. (2009) FSCS controller is evaluated against pre-time control strategies with the average length of queue in different roads of the intersection as one of evaluation criteria. As a result, FSCS provides the shortest average of queue length. The simulation results indicate that FSCS controller can cause more improvement over pre-timed control strategy at over-saturated intersections as shown in Figure 12.
Figure : Simulation results with same arrival rate (Zarandi et al. 2009)
Based on literatures finding, there are many researchers focus on developed fuzzy logic traffic signal controller for a single oversaturated intersection since Pappis & Mamdani (1977) research. This fuzzy controller can be developed easily for every full intersection with two-way streets and left-turn lanes. The fuzzy controller simulates the control logic of experienced humans such as police officers directing traffic who often replace signal controls when intersections experience unusual heavy traffic volumes. This fuzzy controller has the potential to improve operations at oversaturated intersections. The fuzzy controller develop "intelligent" system makes "real time" decisions as to whether to extend (and how much) current green time and very promising applications. With the increasing in traffic volumes and increasing the number of intersections, the intelligent fuzzy logic traffic signal controller must be developing as it become more and more complex. Based on that, the objective of this study is to develop the intelligent multi-phased fuzzy traffic signal controller for a double oversaturated intersection.
CHAPTER 3
RESEARCH METHODOLOGY
In this section, overall description of method that will be used in this study is elaborated. This study is focusing on developing the intelligent fuzzy traffic controller for double isolated intersection. Mamdani-type fuzzy inference system (FIS) is using as editor to develop fuzzy rules, input and output membership functions. MATLAB program will be used in implementing the whole project. In this project, graphical user interface (GUI) for fuzzy based modeling using GUIDE and Fuzzy Toolbox in MATLAB will be developing. The isolated intersection traffic model is design using SIMULINK block diagram. In this project, the Multi-phased fuzzy traffic controller is proposed with consists of Fuzzy Green Phase Extender and Fuzzy Phase Selector. The rules of both are different. At last, the performance of proposed controller will evaluate by simulation as compare to actuated traffic signal controller.
3.1 Model of Double-Isolated Intersection
The proposed traffic signals for double-isolated intersection shown in Figure 13 are designed based on Mustafa (2009). In this model several assumptions has been made. The distance between two junctions, D is less than 50 meter, and no vehicles are stop or present at this distance at any time. In the other words, the green light must actuate until there is no vehicles at the link between two intersections. It can achieve by placing the detectors to detect the present of vehicles between the intersections. The other assumptions are, only one phase will be triggering the green light at one time. For example, if phase 1 is actuated (green light is ON) allowing vehicles from West go through to 1st and 2nd intersections, the others phase (phase 2, 3, 4, 5 and 6) are not actuated (Red light is ON) and so on.
Figure : Propose Double-isolated Intersection Model and Location of the sensors based on Mustaffa (2009)
The proposed intelligent fuzzy controller allow the vehicles to go straight, turn right or turn left at any intersection when the green light is ON at corresponding lane or intersection. There are two sensors placed on the road for each lane. The first sensor behind each traffic light counts the number of vehicles passing the traffic lights, and the second sensor which is located behind the first sensor counts the number of vehicles coming to the intersection at distance L from the lights. The number of vehicles between the traffic lights is determined by the difference of the reading between the two sensors. The distance between the two sensors L, is determined accordingly following the traffic flow pattern at the particular intersection. The fuzzy logic controller is responsible for controlling the length of the green light time and the phase sequences according to the traffic conditions.
3.2 Proposed Design of Intelligent Fuzzy Traffic Controller
The fuzzy traffic signal controller for this project is designed using Mamdani-Type fuzzy inference system in MATLAB Toolbox. Currently, there are two types of fuzzy rules namely, Mamdani fuzzy rules and Tagaki-Sugeno-Kang (TSK) fuzzy rules. Mamdani's controller are using min-max operator on fuzzy rules to make a decision. The main difference between the two methods lies in the consequent of fuzzy rules. Mamdani fuzzy systems use fuzzy sets as a rule consequent whereas TSK fuzzy systems employ linear functions of input variables as rule consequent. All the existing results on fuzzy systems as universal approximations deal with Mamdani fuzzy systems only and no result is available for TSK fuzzy systems with linear rule consequent (Sivanandam, 2007). The most importance reasons to choose Mamdani FIS method because it is the most commonly method and defined it for the Fuzzy Logic Toolbox. It has widespread acceptance, enhances the efficiency of defuzzification process and well suited to human input (Mustafa, 2009).
The proposed Intelligent Fuzzy Traffic Controller consists of two parts. The first part is (i) Fuzzy Phase Selector and second part is (ii) Fuzzy Green Phase Extender, based on Zarandi et al. (2008), Azura et al. (2010) and Murat et al. (2005) work. In this fuzzy controller, fuzzy phase selector function and fuzzy green phase extender function are located in different level of the multi-level signal control system as presented in Figure 14.
Fuzzy Green Phase Extender
Extent or terminate the current green phase
Fuzzy Phase Selector
Selecting the next green phase
Sensor/Detector
Intersection
Detector Data
Extend the Current Green Phase
Terminate
Next Green Phase
Figure : Multi-phase Fuzzy Traffic Controller Block Diagram (Zarandi et al. 2008)
3.2.1 Fuzzy Green Extender Function
Green light extension time of the green phase is produce by using this function according to the condition of observed traffic flows. The traffic flows are observed by receives data from the detector. The two detectors are located in each arm. The extender will determines the right timing for the green phase by tuning the duration of the current green phase with green extension of different lengths, or by terminating the current phase. The fuzzy rules compare traffic conditions with the current green phase and traffic conditions with the next candidate green phase.
3.2.1.1 Input Parameters of Fuzzy Green Extender Function.
Input parameters of fuzzy logic green extender are the following:
(a) The Longest of the Queues in the Red Signal (QR)
This parameter determines the approach of an intersection which has longest queues during the red signal. The input membership function of QR will subdivided into five ranges: Very Short (VS), Short (S), Long (L), Very Long (VL) and Extremely Long (EL). Each range will correspond to a membership functions.
(b) The Number of Vehicles Approaching the Green Signal (QG)
Approaching vehicles during green signal is selected as the second input parameter of the green extender functions. The input membership function of QG will subdivided into five ranges: Very Few (VF), Few (F), Moderate (MD), Many (M) and Too Many (TM).
(c) The Present of Vehicles in Link of Intersection (PV)
The present of vehicles at the link between intersection 1 and 2 is selected as third input. The membership functions are: Present (P) and Not Present (NP)
These two input parameters are considered simultaneously while controlling the traffic flow in this proposed traffic controller. The membership functions of all parameters will be determines using Gaussian Membership Functions.
3.2.1.2 Output Parameters of Fuzzy Green Extender Function.
Output parameter of fuzzy logic green extender is following:
(a) Extension Times (ET)
Extension time which means the extension time of green lights at the current green phase. It will subdivide into five ranges of membership functions of ET: Zero or Terminate (Z), Short (S), Long (L), Very Long (VL) and Extremely Long (EL). The membership functions of all parameters will be determines using Gaussian Membership Functions
The example of rules of Fuzzy Green Extender Function can be expressed as:
IF Queues in the Red Signal (QR) is Very Short (VS) AND
Vehicles Approaching the Green Signal (QG) is Very Few (VF) AND
Vehicles at the link (PV) is Not Present (NP)
THEN Extension Time (ET) is Zero or Terminate (Z) the current green phase.
3.2.2 Fuzzy Logic Phase Selector
The phase selector determines the most suitable phase order. Phase selector controls the phase sequence based on the vehicle's queue length and the extension time of green light from Green Extender Function. This is accomplishing by selecting the next green phase. The traffic situation will be monitor continuously based on data from the detectors and when the green phase is terminated, the decision of the next phase is updated (Zarandi et al., 2009). Figure 15 present the phases and the basic phase order of the intersection model (Figure 13). For example, if the current green phase 1 is to be terminated, the phase selector decides whether to launch next the phase 2 or phase 3 or phase 4 or phase 5 or phase 6. It means, the normal cycle 1 - 2 - 3 - 4 - 5 - 6 can be changed for example 1 - 2 - 4 - 3 - 4 - 6 - 5 - 1, depending on the traffic situation. In this system there is freedom in phase sequencing.
3.2.2.1 Input Parameter of Fuzzy Phase Selector
Input parameter of fuzzy phase selector is following:
(a) The Longest of the Queues in the Red Signal (QR)
This parameter is the most deterministic parameter in fuzzy logic phase selector. This is used for determining the phase sequence in fuzzy logic phase selector. The fuzzy inference is based on weights of each phase. The weights can be defined by the number of queuing vehicles waiting for the green signal in each red phase. Each phase will have its own membership function. The rules are formed to give priority to the phase with highest demand for green time.
For example, if the phase 1 is just terminated the phase selector rules based on number of vehicles in the red signal for another 5 phase (2, 3, 4, 5 and 6). . The input membership function will subdivided into five ranges: Very Short (VS), Short (S), Long (L), Very Long (VL) and Extremely Long (EL)
Phase 1
Phase 4
Phase 5
Phase 6
Phase 3
Phase 2
Figure : Basic/Planning Sequences of the Signal Control at the Intersection Model
3.2.2.2 Output Parameter for Fuzzy Phase Selector
Output parameter of fuzzy phase selector is following:
(a) Phase Ordering (PO)
Phase ordering is the output parameter of the fuzzy phase selector for this proposed controller. According to the input parameter, the next phase will be selected and a proper phase sequence will be decided.
The example of rules of Fuzzy Phase Selector Function can be expressed as:
Assumed, Green Signal for Phase 1 is just terminated, and then fuzzy phase selector will determines the next phase that should be trigger using this rules. For example:
IF Queues in the Red Signal (QR) on lane 2 Very Long (VL) AND
Queues in the Red Signal (QR) on lane 3 Very Short (VS) AND
Queues in the Red Signal (QR) on lane 4 Long (L) AND
Queues in the Red Signal (QR) on lane 5 Very Long (VL) AND
Queues in the Red Signal (QR) on lane 6 Short (S)
THEN lane 2 is chosen as next green phase.
3.2.3 Rule bases of the Proposed Traffic Controller
There are two rules bases on this controller. One of them is relevant with the fuzzy logic green extender and the other is the fuzzy phase selector. Rule bases will be built on the combination of inputs and output parameters. Mamdani method will be used for developing the rule bases where using this method the consequent part is defined as fuzzy sets as antecedents part of the rules
3.2.4 Inference and Defuzzification of Proposed Fuzzy Traffic Controller
Mamdani (max-min) Inference Method will be used in this controller. The defuzzification process will be applied to get the crisp value. Defuzzification means conversion of the fuzzy values to the crisp values. The method of defuzzification will be used either Center of Gravity (COG) or Mean of Maximum (Sivanandam et al., 2007).
3.2.5 SIMULINK, SimEvent, Graphical User Interface (GUI) and Simulation.
The isolated traffic intersection model will develop in MATLAB using SIMULINK and SimEvent toolbox. The Graphical User Interface (GUI) will also develop using GUIDE (Graphic User Interface Developing Environment) in MATLAB. The purpose to develop a programmed GUI is to interact with fuzzy variables in order to model the traffic controller with different inputs.
The actuated traffic signal controller for double isolated intersection will also develop in this study in order to compare their performance with proposed fuzzy traffic controller. Simulation method will be used to test the performance of fuzzy traffic controller and the result are discussed based on (i) average waiting time, (ii) average delay time (deceleration, stop, acceleration delays) and (iii) average queue lengths as performance index for controlling traffic flow at the intersection. Table 3 present the example table that will be used to compare the performance is of Fuzzy Traffic Controller and Actuated Traffic Controller. The values of each parameter are gain from the simulation on different conditions of traffic volumes such as Low Volume, High Volume and Saturated Volumes of traffics.
Table 3: Performance of Fuzzy Traffic Controller AND Actuated Traffic Controller ( Azura Che Soh et al. 2010)
Performance
Measure
Phase
Fuzzy Traffic Controller
Actuated Traffic Controller
Improvement
(%)
average waiting time
(seconds)
Phase 1
:
Phase 6
average delay time
(seconds)
Phase 1
:
Phase 6
average queue lengths
(seconds)
Phase 1
:
Phase 6
3.3 Project Flow-chart
Flow-chart in Figure 16 and 17 shows the sequence of the entire work (from discuss and confirmation the title of project until report writing and report submission) in order to complete the research.
Figure : Project Flow-chart 1
Figure : Project Flow-chart 2
