This document outlines a study to identify the range of thresholds for fuzzy inputs in traffic flow. It begins with the aim and objectives, which are to review literature on fuzzy logic applications in traffic engineering and understand traffic flow conditions and parameters. It then provides an overview of fuzzy logic and its applications in areas like traffic congestion, gap acceptance, overtaking, lane changing, and speed limits. Thresholds identified from various studies are presented. The document demonstrates a fuzzy logic model for traffic congestion detection in MATLAB. It concludes that identifying threshold ranges can help traffic managers maintain efficient flow and addresses unanswered questions and recommendations for future research.

1. BAYERO UNIVERSITY, KANO
NIGERIA
SPS/21/MCE/00030
CIV8331 (Advanced Traffic Engineering)
December, 2023
IDENTIFYING THE RANGE OF THRESHOLDS
FOR FUZZY INPUTS IN TRAFFIC FLOW
PETER, VICTOR ENEJI

2. OUTLINE
• Aim and Objectives
• Introduction
• Fuzzy Logic Overview
• Review on Applications of Fuzzy Logic
• Traffic Congestion
• Gap-Acceptance Behaviour
• Overtaking and Lane Changing
• Speed Calming and Speed Limits
• Fuzzification Process Overview
• Fuzzy Logic, Its Elements, and Methodology of Approaching a Problem
• Conclusion
• Unanswered Questions
• Future Directions and Recommendations

3. ❖ To review relevant literature on Fuzzy Logic applications in Traffic Engineering.
❖ Reviewing literature and understanding traffic flow conditions are crucial steps to
achieving the aim, laying the foundation for a comprehensive study.
❖ To understand traffic flow conditions and identify parameters influencing driver
decisions.
❖ Reference to key studies where researchers successfully identified fuzzy thresholds for
traffic parameters is crucial.
OBJECTIVES:
AIM:
To identify the range of thresholds for fuzzy inputs in
traffic flow

4. INTRODUCTION
• Managing traffic flow effectively is still a major challenge in the dynamic and ever-changing field of transportation
engineering. Think of a major city during rush hour, where sudden surges in traffic demand occur due to various
factors like events or accidents.
• Conventional traffic control systems find it difficult to adjust to the natural uncertainties and complexities of real-
world traffic conditions because they frequently rely on strict and predefined algorithms.
• Fuzzy logic's strength lies in its ability to handle uncertainty and adapt to changing conditions, making it an ideal
solution for the dynamic nature of traffic flow, as humans often rely on linguistic information such for processing
traffic parameters in traffic scenarios.
• Ideal circumstances for effective traffic flow are represented by the normal ranges for traffic parameters. There may be
the possibility of divers traffic issues when traffic parameters are outside of these ranges. However, the threshold
ranges specify the points at which traffic management interventions ought to be taken into account.
4

5. FUZZY LOGIC OVERVIEW
5
Figure 1.0: From absolutely true or false statements (A) to partially true or false statements (fuzzy) (B). Source:
• We can understand the passenger next to us quite well when he tells us to “move a little bit closer to the
right” or “wait a bit for this speeding car to pass” when we are driving and backing out of a parking
space. This simple example shows that in certain situations we accept linguistic information much more
easily than numerical information
• Fuzzy logic is a system introduced by Lofti Zadeh in 1965. It doesn’t rely on binary outcomes (0,1); but
instead, deals with degrees of truth, allowing for a more complex representation of information.

6. FUZZY LOGIC OVERVIEW CONTINUES
6
• The degree of truth are the linguistic variables (e.g., "low," “very low,” “medium,” “high,”
“extremely high.”). This can take any adjective representation, depending on the traffic flow
parameter in consideration.
• When describing traffic density, fuzzy logic might use terms like “light,” “moderate,” and “heavy”
instead of specific numerical values.
• Fuzzy logic’s adaptability aligns well with the uncertainties inherent in traffic parameters, making
it a suitable tool for traffic flow management.

7. 7
• Numerous studies on traffic congestion, Gap-Acceptance behaviour, Overtaking and Lane changing,
Speed calming and Speed limits, were conducted using fuzzy logic approach to effectively model complex
traffic scenarios, and providing a more realistic representation by adopting and establishing thresholds on
applicable traffic flow parameters.
• We will briefly look at an overview of each scenarios highlighted above for more insight.
• For better understanding of the fuzzy logic approach, a study on “Fuzzy inference approach in traffic
congestion detection” by (Kalinic et al., 2019) will be demonstrated using MATLAB.
• Different thresholds from various studies have been identified and documented in a more detailed report.
REVIEW ON APPLICATIONS OF FUZZY LOGIC

8. TRAFFIC CONGESTION
8
• Globally, traffic congestion is a major issue and it has been the subject of extensive research for many years.
However, there isn't a single, widely recognized definition of traffic congestion.
• According to (Pedram et al., 2017), congestion is a situation in which there is a greater demand for road space
than there is available space.
• Traffic congestion is still faced today, and this issue will be more prevalent in places where unlimited access
roads, (such as arterials) and limited access roads (such as highways) are connected.
• There are also different definitions for the threshold at which congestion occurs (Afrin & Yodo, 2020). Some of
these threshold values have being identified and summarized as presented in the next slide

9. TRAFFIC CONGESTION
9

10. GAP-ACCEPTANCE
10
• In traffic engineering, fuzzy logic has been widely applied especially when simulating gap acceptance
behaviour at unsignalized intersections.
• (Sangole & Patil, 2014) defines gap-acceptance as a process by which a car in the secondary access accepts
openings in the primary traffic stream explaining how it has become a crucial driver behaviour characteristic
that impacts capacities, delays, and road safety at unsignalized intersection.
• In order to understand and predict pedestrian crossings behaviours at unsignalized intersections, (Saleh &
Lashin, 2020) proposed a fuzzy logic system to predict the critical gap for pedestrian crossing at unsignalized
intersections
• Results on Gap-acceptance showed the effectiveness of fuzzy logic in predicting critical gaps with good
accuracy considering factors such as pedestrian crossing speed, approaching vehicle speed, and vehicle type
and other various traffic parameters as inputs. Some threshold values extracted from different research works
are presented in the next slide.

11. GAP-ACCEPTANCE
11

12. OVERTAKING AND LANE CHANGE
12
• Fuzzy logic has emerged as a promising approach for modelling and controlling overtaking behaviour in
autonomous vehicles and traffic flow systems, enhancing safety and efficiency by handling imprecise
information.
• According to (Ghaffari et al., 2017). driver’s error contributes to over 75 percent of road crashes especially in
overtaking maneuvers, and that the development of intelligent algorithms to describe, model and control this
phenomenon was needed.
• (Almadi et al., 2022) investigated the use of fuzzy logic to model driver decision-making during overtaking
maneuvers. The authors developed a fuzzy logic system that incorporates driver characteristics, such as
experience, risk perception, and driving style, to predict overtaking decisions.
• In order to predict lane change behaviour with high accuracy, (Sultanova, 2023) suggested a fuzzy logic-based
planning strategy for autonomous vehicles to navigate safely when changing lanes. Various thresholds on
overtaking and lane changing are presented in the next slide.

13. OVERTAKING AND LANE CHANGE
13

14. SPEED CALMING AND SPEED
LIMITS
14
• In adaptive speed calming, Fuzzy logic has been be used to develop adaptive speed calming systems to adjust
the speed of vehicles in real time.
• A fuzzy logic controller for adaptive speed calming was proposed by (Guo et al., 2021), the controller modifies
the height of speed bumps and the speed limit for cars based on real-time traffic data.
• (Rahman et al., 2022) suggest ranking the importance of speed-calming initiatives in school zones using fuzzy
logic. The approach prioritizes various speed calming measures based on variables like school hours, pedestrian
traffic, and proximity to crossing points and it was discovered to be successful in identifying school zones that
need more speed-calming measures.
• Another interesting study was conducted by (Almadi et al., 2022) to explored the relationship between road
safety, driver behaviour, and weather conditions using fuzzy logic to determine desirable safe speed values as
presented in the next slide.

15. 15
SPEED CALMING AND SPEED
LIMITS

16. 16
FUZZIFICATION PROCESS OVERVIEW
Input Data
(Crisp)
Fuzzification
Fuzzy Rule
Inference
Defuzzification
Fuzzy set Representation
• Linguistic Variable
• Linguistic Term
• Membership Function
Rule base
IF…. THEN
Inference
Translate into
Crisp values
• Survey and interview
• Expert experience
Output
membership
function
Output Data
(Crisp)
Figure 1: Basic elements of fuzzy logic process

17. 17
The basic elements of a fuzzy logic system
• Rules: Calculation of the membership function and decision of whether an element belongs to
the fuzzy set or not,
• Fuzzification: as input data are most usually crisp values, the process of fuzzification is to map
real scale values into fuzzy values,
• Inference engine: it maps fuzzy numbers into fuzzy sets,
• Defuzzification: it aims to choose the one (and appropriate) value for the output variable
FUZZY LOGIC, ITS ELEMENTS, AND
METHODOLOGY OF APPROACHING A PROBLEM

18. 18
• MATLAB is used for a simple demonstration on how the Fuzzy Logic System works
adopting various thresholds values.
• A study on its application in determining the level of traffic congestion is replicated using
relevant thresholds for the parameters.
• The inputs parameters adopted are Flow and Density with the aim of establishing a crisp
output value for Level of congestion (LOC).
• The linguistic variables and the applicable rules are presented in table 5 and 6 in our next
slide
PRACTICAL APPLICATION ON TRAFFIC
CONGESTION

19. 19
• Defining input parameters is a challenging task as it involves both knowledge and experience
in the specific field of interest.
• The next five slides illustrates how these input parameters are fuzzified using MATLAB
PRACTICAL APPLICATION ON TRAFFIC
CONGESTION
Table 5: Fuzzy IF–THEN rules with AND operator.
Source: Maja Kalinic & Jukka M. Krisp (2019)
Table 6: Fuzzy inference output parameter (level of congestion)

20. FUZZY INPUT PARAMETER FOR FLOW IN
DETERMINING LEVEL OF TRAFFIC CONGESTION
20
Figure 2: Range of value for flow (0 to 80) Veh/hr

21. 21
FUZZY INPUT PARAMETER FOR DENSITY IN
DETERMINING LEVEL OF TRAFFIC CONGESTION
Figure 3: Range of value for Density (0 to 25) Veh/km

22. 22
FUZZY OUTPUT PARAMETER (CONGESTION
LEVELS)
Figure 4: Range of output value for congestion level (0 to 1)

23. 23
IF-THEN RULE FOR INFERENCING
Figure 5: Seven firing inference rules

24. 24
FUZZY OUTPUT RESULT
Figure 6: Command window for running various input command
• It can be clearly seen that for input values of 60 and 15 (flow and Density), the level of congestion is
50%
• Other input values within the range of thresholds can be applied to obtain corresponding output value.

25. CONCLUSION 25
As you can see, ideal circumstances for effective traffic flow are represented by the normal ranges
for traffic parameters. There may be the possibility of divers traffic issues when traffic parameters
are outside of these ranges. However, the threshold ranges specify the points at which traffic
management interventions ought to be taken into account. Traffic managers can contribute to the
preservation of efficient traffic flow and the reduction of several traffic problems by keeping an
eye on traffic parameters and acting when they approach threshold levels.
Fuzzy logic, with its ability to handle uncertainties and adapt to changing conditions, is well-
positioned to play a significant role in the future of intelligent traffic management. However, there
are still a number of unanswered questions that should be taken into account for further study in
this field.

26. 26
There are still a number of unanswered questions that should be
taken into account for further study in this field. Among them are:
• Incorporating more complex traffic scenarios,
• Considering driver behaviour heterogeneity,
• Modelling driver perception and decision-making,
• Validating models with real-world data.
UNANSWERED QUESTIONS

27. 27
• It is recommended that further research incorporate driver’s variabilities and emotions, advance
real-time sensor data from infrastructure and vehicles, including radar, lidar, and cameras, to enable
the dynamic adaptation of overtaking decisions to real-time traffic environment.
• In studies to aid drivers’ decision-making behaviours with respect to speed limits on different
weather conditions, little attention is paid to environmental inputs, like road gradient, time, and
more extreme weather. These can be included in future work to increase the similarity on the
population of diverse drivers.
• Several research gaps such as the requirement for standardization and guidelines, integration with
other transportation systems, adaptation to dynamic traffic conditions, validation using real-world
data, and long-term impact assessment need to be addressed to further enhance effectiveness and
applicability.
FUTURE DIRECTIONS AND
RECOMMENDATIONS

28. THANK YOU!