Improving traffic and emergency vehicle clearance at congested intersections using fuzzy inference engine

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 4, August 2021, pp. 3176~3185
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i4.pp3176-3185  3176
Journal homepage: http://ijece.iaescore.com
Improving traffic and emergency vehicle clearance at congested
intersections using fuzzy inference engine
Aditi Agrawal, Rajeev Paulus
Department of Electronics and Communication Engineering, VIAET, SHUATS, Prayagraj, INDIA
Article Info ABSTRACT
Article history:
Received Aug 24, 2020
Revised Dec 12, 2020
Accepted Jan 13, 2021
Traffic signals play an important role in controlling and coordinating the
traffic movement in cities especially in urban areas. As the traffic is
exponentially increasing in cities and the pre-timed traffic light control is
insufficient in effective timing of the traffic lights, it leads to poor traffic
clearance and ultimately to heavy traffic congestion at intersections. Even the
Emergency vehicles like Ambulance and Fire brigade are struck at such
intersections and experience a prolonged waiting time. An adaptive and
intelligent approach in design of traffic light signals is desirable and this
paper contributes in applying fuzzy logic to control traffic signal of single
four-way intersection giving priority to the Emergency vehicle clearance.
The proposed control system is composed of two parallel controllers to select
the appropriate lane for green signal and also to decide the appropriate green
light time as per the real time traffic condition. Performance of the proposed
system is evaluated by using simulations and comparing with pre-timed
control system in changing traffic flow condition. Simulation results show
significant improvement over the pre-timed control in terms of traffic
clearance and lowering of Emergency vehicle wait time at the intersection
especially when traffic intensity is high.
Keywords:
Emergency vehicle anticipation
Fuzzy logic
Intelligent traffic light control
Single intersection
This is an open access article under the CC BY-SA license.
Corresponding Author:
Aditi Agrawal
Department of Electronics and Communication Engineering
VIAET, SHUATS
Naini, Prayagraj, INDIA
Email: aditi.agrawal@shiats.edu.in
1. INTRODUCTION
As the number of vehicles are continuously increasing on the streets, a serious problem of traffic
congestion occurs even if the traffic signals are implemented for controlled flow of traffic. This problem
occurs because traffic is characterized by uncertainty and random behaviour whereas the pre-timed
conventional green/red traffic signals have constant time phases switched between cycles. Although, the
design of pre-timed signal control is simple, generally its performance is poor in heavy traffic clearance in
minimizing wait time for emergency transit, so need for an intelligent and adaptive control system arises. The
objective of this research is to design an adaptive traffic light control system that aids in heavy traffic
clearance at crossroads as well as assisting in emergency vehicle clearance, thus saving both time and lives of
commuters.
Fuzzy logic started by Zadeh [1] has been widely used by researchers for solving traffic congestion
problem at intersections. Fuzzy logic-based control scheme is very similar to human thinking and can imitate
an ideal policeman at the intersection. Fuzzy logic in signal control [2] was firstly presented by illustration of
a hypothetical model based on fuzzy logic controllers for confined crossing points. In [3] the development of

Int J Elec & Comp Eng ISSN: 2088-8708 
Improving traffic and emergency vehicle clearance at… (Aditi Agrawal)
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fuzzy logic in traffic control was extensively discussed. Fuzzy logic traffic system [4] was designed to adjust
the phase sequence and duration of traffic lights at isolated intersection. This system was tested by collecting
real time data from signalized intersection in Hawalli governorate in the State of Kuwait. Proposed system
was tested and compared with the vehicle actuated system and improved performance resulted in case of
heavy traffic volumes. A fuzzy based control system for green time extension of traffic lights [5] was
compared with fixed time conventional method. The simulation results were better in terms of average delay
experienced by the vehicles. Other remarkable works in this area include [6-11] in which the designed fuzzy
controllers reduced the average waiting time of vehicles as compared to pre-timed traditional systems. A two
-stage fuzzy traffic signal control method [12] was suggested and compared with pre timed control method.
The results were analysed in terms of average waiting time of vehicles and reduced waiting time was
obtained but as the cascaded controllers required different inputs to take decision of next green phase and its
delay time, it added to the cost and complexity. Another work [13] introduced a hybrid system by integrating
WSN with fuzzy logic. Total of sixteen sensors were deployed at an intersection to collect data in terms of
traffic quantity and wait time for green light by using fuzzy inference and the priority degree for each lane
was generated. Reduction in average waiting time and increased traffic flows from all lanes was obtained but
the designed system lacked giving priority to emergency transit. Traffic clearance at intersections by giving
priority to emergency transit [14-18] extended the green time on detection of emergency vehicle. The recent
works in this area include [19] in which a fuzzy inference system uses five input variables to fix the green
light interval. Although an 18% improvement in performance was obtained while conducting experiments in
simulated environment but due to large scale of inputs the system complexity was high.
The phase sequencing in [4] was fixed and cycle time was adjusted to abridge idle green time but
the system lacked giving priority to emergency transit. In our proposed work, the design supports two fuzzy
controllers for lane sequencing and adjusting green time as to prioritize emergency transit while considering
the traffic density too. Our proposed system is designed such that the emergency transit is given the highest
priority and cleared in minimum time. Other designs [5-11] contributed in improving the waiting time of
vehicles at isolated intersections yet, a simple design for easy implementation was needed in terms of
collecting the input parameters for the system. The system proposed in this paper makes use of two parallel
fuzzy controllers designed to work on the same inputs fed to decide for the next green phase and select its
green-time. This made the system less complex and ease of physical implementation. The cost and
complexity of the systems [12-19] due to large scale of inputs required was taken care in the proposed design
as the system requires inputs in terms of the traffic volume sensed at each lane and detection of EMV. The
proposed system in this paper is designed in a way to achieve simplicity in collection of inputs and easy
implementation for both busy and underutilized intersection.
2. PROPOSED METHOD
2.1. Model of parallel fuzzy control system
Modeling is a vital step to develop an effective control system and for simulation of a physical
process. Fuzzy logic is a distinct idea for emergent models of physical processes. Fuzzy models are less
intricate; they can be understood straightforwardly and are much appropriate for stochastic processes. The
applications of fuzzy logic in designing intelligent control systems are given by [20-22]. Therefore, a fuzzy
control model is developed for traffic light control of a typical intersection with four lanes and each lane
having two sub-lanes for going forward and turning left respectively as shown in Figure 1. Traffic signal
design principles and related terms are explained clearly in [23]. In the perceived model, traffic is considered
to enter the lanes in groups and emergency vehicles enter randomly on any lane. The traffic parameters are
sensed using roadside sensors and given as input to the fuzzy controller. Various traffic sensing techniques in
sensor networks for data collection are discussed in [24-25]. For easy and cost-effective actual
implementation of the designed system we have chosen the inputs with a motive that they can be sensed by
simple and readily available sensors without much investment needed in providing infrastructure to the
designed system. To select the next green light lane and decide the appropriate green time of the selected lane
in an isolated four-lane traffic intersection we propose a Mamdani model of parallel fuzzy inference system
with five inputs that can be easily sensed by the roadside sensors. The inputs are taken as:
 Traffic at lane i (li), where i=1,2,3,4 (number of vehicles in lane i)
 Emergency vehicle (EMV) presence in any lane
Decision making of the fuzzy system is based on the weights w(li) of the sensed inputs of each lane
where i=1,2,3,4. The designed system takes the total number of vehicles on each lane and emergency vehicle
detected on any lane as weights for decision making. The block diagram of the proposed fuzzy inference
system is shown in Figure 2. The maximum traffic for which the fuzzy system works efficiently is taken to be
100 vehicles on each lane.

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Int J Elec & Comp Eng, Vol. 11, No. 4, August 2021 : 3176 - 3185
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Figure 1. Typical isolated intersection showing traffic movements
Figure 2. Block diagram of the proposed parallel fuzzy system
The sensed inputs from roadside detectors are fed to both the fuzzy green selector and fuzzy green
time decider controllers working in parallel. The membership function of input traffic on lanes (li) is shown
in Figure 3. Three characteristics are taken for designing the membership function for this input variable and
their values are given in Table 1. The membership function for detection of emergency vehicle on lane li
where i=1,2,3,4 is shown in Figure 4. The membership functions are designed as:
 EmVehicle=1; If Emergency vehicle appears on l1
 EmVehicle=0; If Emergency vehicle is absent
Figure 3. Membership function of input variable Traffic on lanes (li)

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Table 1. Values of the membership function of input variable traffic on lanes (li)
Variable Characteristics Value (vehicle count)
Traffic on lanes (li) Low 0-23
Medium 12-42
High 35-100
Figure 4. Membership function of EM vehicle
The Rule editor of fuzzy green selector system is given in Figure 5. Total 85 rules are constructed in
Rule Base 1 to select the next green lane. For example, the third rule can be expressed as: IF traffic on Lane
1=low AND traffic on Lane2=low AND traffic on Lane3=low AND traffic on Lane4=high AND EMVehicle
= Absent THEN Next Greenlane(lg)=Lane4.
A no-differ (random selection of next green-lane) output is used for input values that are similar or
with little difference so that output selection can be any lane for nextgreen. Similarly, fuzzy greentime
decider works using 93 rules that are formulated in Rule Base 2 to decide the greentime of the selected lane.
For example: IF traffic on Lane 1=low AND traffic on Lane2=low AND traffic on Lane3=low AND traffic on
Lane4=high AND EMVehicle=Absent THEN Greentime(tg)=high.
Two outputs are obtained from the designed system. The output obtained from fuzzy green
selector is next green lane (lg) and from fuzzy greentime decider is greentime for selected lane (tg). The
membership functions for both the outputs are triangular functions and shown in Figure 6(a) and Figure 6(b)
respectively.
Figure 5. Rule editor of fuzzy green selector

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(a)
(b)
Figure 6. Membership functions: (a) Next greenlane (lg) and (b) Greentime of selected lane (tg)
2.2. Simulation environment and algorithm to evaluate the proposed system performance
It is necessary to choose a simulation environment like that of a real time traffic intersection and
consider all the possible traffic statistics to test the designed system. Performance of the proposed parallel
fuzzy logic controller is evaluated by comparing it with the existing fixed-timed control method. In order to
make comparisons, identical conditions have been set during the simulations. The initial parameters are set
according to the intersection before the actual implementation. The flowchart of the proposed fuzzy traffic
light control system is given in Figure 7. The algorithm has been designed by considering and evaluating the
following system parameters:
 Traffic is assumed to enter in platoons on each lane and the count is stored in matrix xi, where i=1,2,3,4.
The traffic density on lanes is varied in terms of low, medium and high traffic by taking different values
of xi.
 The cycletime (tc) is the time for one complete cycle which is divided between lanes as greentime to clear
the traffic xi efficiently. If there are remaining vehicles after completion of cycletime tc(k) they are added
to the vehicles entering in the next platoon and are cleared in the next cycletime tc(k+1). The matrix
totalCars stores the total traffic count on each lane.
 A timeout (to) is also considered to take care of the lane which has not received green signal since a long
time to avoid extensive wait time at lanes that have very less traffic.
 Simulations are extensively performed for maximum vehicles (Cmax) ranging from 100 to 1000 moving
towards the intersection in all four lanes in all possible traffic scenarios to get a wide range of results for
evaluating the performance of designed system.
 The percentage clearance of traffic specially for the high traffic and average emergency vehicle (EMV)
wait time are considered as the parameters to evaluate and compare the performance of the proposed
system and traditional fixed time system.
The flowchart in Figure 7 clearly demonstrates the steps taken to simulate the designed fuzzy traffic
light control system. It is observed that vehicles are entering in platoons on all four lanes and need to be
cleared in a particular cycletime while giving priority to emergency transit in such a way that once detected,
the EMV is passed through the intersection with minimum waiting time. The vehicles entering on each of the
four lanes and presence of emergency transit is detected by roadside sensors and given as input to the
designed parallel controllers namely fuzzy next green selector and fuzzy greentime decider. The next lane to

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have green light is decided with the help of designed fuzzy rules and its greentime is also decided in parallel.
The algorithm also checks the condition where a low traffic lane does not get green clearance for a prolonged
time and thus avoids the problem of high waiting time for vehicles present in low traffic lanes. This is done
by comparing the green-wait-time of lanes with a timeout (to). The system is simulated for groups of vehicles
entering on the lanes ranging from 100 to 1000 cars/lane and performance is studied in terms of traffic
clearance and waiting time of EMV. The next section of the paper discusses the performance of the designed
system.
Figure 7. Flowchart of the proposed Fuzzy traffic light control system
3. RESULTS AND DISCUSSION
For extensive evaluation of the designed system, the simulation is performed taking different traffic
scenarios including low, medium, high and even mixed traffic conditions on lanes of the intersection and
results are compared with the traditional fixed time traffic light control system. The controller was tested
using fuzzy logic toolbox in MATLAB. Following assumptions are made before running the simulations.
 Traffic is considered to enter in platoons in each cycle and size of these platoons is varied to obtain
different traffic scenarios.
 The system is assumed to start from state 0 i.e there are no vehicles on any lane.
 An optimum cycle time is chosen so that both fuzzy and fixed time systems clear maximum incoming
traffic.
 It is assumed that 2% of traffic entering in each lane is emergency vehicle and its clearance in minimum
waiting time is a major criterion in deciding the performance of the designed system.
 The fixed time traffic light control system has a fixed green time of 1 minute for each lane with fixed lane
sequencing (l1 l2 l3 l4).
 The fuzzy system is designed to work effectively within a range of 0-100 cars waiting/lane as this data is
close to the real scenario.

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Further, the results are obtained for all the possible traffic scenarios given in Table 2 that can be
perceived at an isolated urban intersection.
Table 2. Traffic scenarios and traffic distributions taken to evaluate the designed system
Traffic scenarios Traffic distribution
Uniform Traffic
Distribution on all lanes
Low Traffic 80 vehicles/cycle with 20 vehicles/lane
Medium Traffic 120 vehicles/cycle with 30vehicles/lane
High Traffic 160 vehicles/cycle with 40 vehicles/lane
Non-Uniform Traffic
Distribution on all lanes
Mixed Traffic Scenario 1 High on one lane, medium on two lanes and low traffic on one lane.
Mixed Traffic Scenario 2 High on two lanes, medium on one and low on one lane.
Mixed Traffic Scenario 3 High on three lanes and low traffic on one lane
3.1. Uniform traffic distribution on all lanes
The obtained simulation results for low traffic are shown in Figure 8. It can be perceived that both
systems show similar performance with 100% traffic clearance but there is a comparable difference in EMV
wait time at intersection. Proposed fuzzy system clears the EMV in minimum time of 30 sec throughout the
extend of simulation as it addresses the EMV as soon as it is detected whereas fixed time traditional systems
clear the EMV in time ranging between 1.5 to 3.5 minutes which is significantly high specially when traffic
is low. For medium traffic distribution on all lanes the difference in traffic clearance is clearly observed in
Figure 9. Proposed Fuzzy system shows percentage clearance of 95%-99% whereas the traditional fixed time
control system clears 80%-82% traffic. The EMV waiting time for fuzzy system in this case is observed to be
a minimum of 30sec whereas it varies between 2 to 3 minutes in fixed time system which is significantly
high. For high traffic distribution, difference in traffic clearance is clearly observed in Figure 10. Traffic
clearance in fuzzy system range is between 93%-98% whereas in fixed time control system it is observed to
be 85%-87% for the same cycle time. The EMV waiting time for fuzzy system even in the case of heavy
traffic is observed to be a minimum of 30 sec as seen in results obtained, whereas it varies between 2 to 3
minutes in fixed time system which is significantly high.
Figure 8. Comparison of traffic clearance and EMV waiting time for low traffic scenario
Figure 9. Comparison of traffic clearance and EMV waiting time for medium traffic scenario

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Figure 10. Comparison of traffic clearance and EMV waiting time for high traffic scenario
3.2. Non-uniformly distributed traffic on lanes
This scenario is created with non-uniform traffic entry rate on each lane at the intersection which is
close to the real time scenario. We have evaluated the performance of both systems under all possible
scenarios.
Mixed traffic scenario 1: The traffic distribution for this scenario is taken as given in Table 2. The
percentage traffic clearance of the lane with high traffic intensity in fuzzy system is nearly 100% whereas
fixed time system clears only 70-75% of the traffic in high traffic lane as seen in Figure 11. So, there is an
improvement of 25-30% in heavy traffic clearance. The average EMV waiting time is minimum (30 sec) for
fuzzy system and between 2-3 minutes for fixed time control. The huge difference in both traffic clearance
and EMV wait time proves the benefit of fuzzy system over fixed time system.
Mixed traffic scenario 2: The scenario uses traffic distribution as given in Table 2. Improvement of
25-30% in traffic clearance by fuzzy system is clearly obtained in Figure 12 and the average EMV waiting
time is minimum (30 sec) for fuzzy system and between 2-3 minutes for fixed time control system. This
shows an appreciable improvement in clearance efficiency of the proposed fuzzy controller along with
attending and clearing the EMV in minimum time at the intersection.
Figure 11. Comparison of traffic clearance and EMV waiting time for mixed traffic scenario 1

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Mixed traffic scenario 3: This scenario is created with heavy traffic on three lanes and low on one
lane as given in Table 2. Figure 13 shows the simulation results for percentage traffic clearance of high
traffic lanes. An average high traffic clearance of 95% is obtained for proposed fuzzy system whereas fixed
time system gives only 75% clearance for the heavy traffic lanes. The average EMV waiting time of 3
minutes for fixed time control system as seen from Figure 13 is significantly high as compared to the
minimum time of 30 seconds obtained by the fuzzy controller.
4. CONCLUSION
The results are evaluated by considering multiple traffic scenarios at a single intersection. The
results clearly portray that the proposed fuzzy traffic light controller gives a better traffic clearance at the
intersection in each traffic scenario and that too by addressing the EMV as soon as it arrives and is detected
at the intersection hence reducing the EMV waiting time and taking its fast clearance as a priority. The
proposed fuzzy system clears the EMV in minimum duration in all the possible traffic scenarios. Results
clearly portray that traffic clearance for low traffic scenario is same for fixed and proposed fuzzy system but
for medium, high and non-uniform traffic entry rate at intersection the proposed system gives fairly better
clearance. The proposed system integrates traffic clearance with fast EMV clearance at intersections and so it
can be easily implemented in the present traffic signal infrastructure of cities to provide intelligent traffic
signaling. Future work is emphasized on enhancing the system to work for connected intersections for
streamlined passage of EMV detected at one intersection through the lane of the next intersection without
even stopping. Other enhancements can be done by considering the number of pedestrians waiting to cross
the road and minimizing the waiting time of pedestrians.
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BIOGRAPHIES OF AUTHORS
Aditi Agrawal is currently pursuing Ph.D. from SHUATS, Prayagraj. She received her M. Tech.
degree in Electronics and Communication Engineering (Wireless Communication Engineering)
from SHIATS, Allahabad and Bachelor in Engineering degree from Chaudhary Charan Singh
University, Meerut. She has been working as an Assistant Professor in the Department of
Electronics & Communication Engineering, Sam Higginbottom University of Agriculture,
Technology & Sciences since 2013. She has co-authored over 20 international publications in
peer-reviewed national, international journals. Her current research interests include Vehicular
communications and networks, Smart transportation systems, Sensor and Ad-hoc networks and
Intelligent system design.
Rajeev Paulus received his Ph.D. degree in Electronics & Communication Engineering from
SHIATS, Allahabad, M. Tech. degree in Electrical Engineering from MNNIT, Allahabad and
Bachelor degree in Electronics from University of Pune. He has been working as an Assistant
Professor in the Department of Electronics & Communication Engineering, Sam Higginbottom
University of Agriculture, Technology & Sciences since 2005. He is a senior member of IEEE,
Life member of ISTE and many other technical professional bodies. He has co-authored over
100 international publications in peer-reviewed national, international journals, presented
number of papers in Conferences at both National and International Level. He has reviewed
various scientific publications. His current research interests include next generation Wi-Fi,
wireless network performance evaluation methods, Cellular network (5G), Sensor & Adhoc-
network, Internet of Things.

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