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12th International Conference on Computational Intelligence and Communication Networks
978-1-7281-9393-9/20/$31.00 ©2020 IEEE 17
DOI: 10.1109/CICN.2020.33
Infrared Sensor based Self–Adaptive Traffic Signal
System using Arduino Board
Dhruv Patel
Electrical and Electronics Engineering
IET, JK Lakshmipat University
Jaipur, India
dhruvpatel@jklu.edu.in
Yogesh Rohilla
Electrical and Electronics Engineering
IET, JK Lakshmipat University
Jaipur, India
Abstract—An infrared sensor based prototype of self–
adaptive traffic signal control system has been developed in this
paper. An autonomous traffic signal system can be an effective
measure to alleviate congestion of urban traffic. The system
adjusts the traffic signal parameters according to the intensity
of vehicles in respective lanes and improves the efficiency of
traffic operation on urban road networks. The self–adaptive
traffic signal system is a closed loop control system. It measures
the intensity of vehicles in each lane and gives a priority green
signal to the lane, which has the highest intensity of vehicles and
the red signal to every other lane. However, the priority depends
on the type of road network and type of intersections.
This infrared sensor based self–adaptive system measures
the density of vehicles using infrared sensors at each of the four
roads at the intersection. It sends data to Arduino Mega
microcontroller board which accordingly gives priority to the
most concentrated lane. A detailed description of each of the
components with its pin diagram and technical details has been
provided. The functioning of the system has been shown with
the help of system architecture, a block diagram, a flow chart,
and a closed loop control system diagram. The system has been
simulated on Proteus 8 simulation software. A step by step
process of hardware connections has been given so that anyone
can make this prototype. The results of the prototype are
exciting in controlling the traffic.
Keywords—adaptive traffic signal system, Arduino Mega,
Arduino IDE, Infrared sensor, closed loop control system,
Proteus 8
I. INTRODUCTION
Urban traffic congestion is one of the major problems in
the world's big cities [1, 2]. The problem is not only the time
spent in traffic but also the carbon dioxide emission. The
longer the vehicles stuck in the traffic, the more their engines
continue to run on idle condition. Solving this issue will not
only make the lives of people living in cities easier but also
create a positive effect on the climate.
Present traffic light system is an open loop control system.
Generally, a traffic light arrangement consists of three lights
of red, yellow, and green colour. These lights operate based
on the fixed time interval given in them irrespective of the
traffic density. The fixed timings of operation create issues of
inappropriate operation of traffic lights, jumping of traffic
signals by drivers, increased waiting time, and loss of
petroleum [3].
Many solutions have been proposed in the literature to
make traffic signal automatic and behave as per the traffic
density rather than fixed timings. These solutions were based
on PIC microcontroller, IR sensors, and Xbee communication
[4]; innovative algorithms like fuzzy and genetic algorithms
[5-7]; IoT devices [8, 9]; and other methods [10-12].
This article is based on a self–adaptive traffic light system
that senses traffic density and gives a green signal to the traffic
lane with the highest traffic density. The infrared sensor (IR)
is used for this purpose. The prototype uses 2 IR sensors per
route, but each route can have several sensors as needed for
real-life applications. A measure of the density is provided by
the number of IR sensors in front of which an object is
detected. For example, let one IR sensor in one lane detect
traffic and two IR sensors in another lane detect traffic. A
green signal from Arduino microcontroller board will then be
given to the lane with 2 IR sensors detecting traffic for a fixed
interval of time. After the specified time is over, a green signal
is issued to the first lane. When no vehicle is visible, a green
signal will be sent sequentially to each lane for a specified
time before an IR sensor detects traffic. The IR sensor output
is therefore measured in this paper by the detection of an
object in the front of it, and the traffic lights are controlled by
the control signals generated by the Arduino microcontroller
board.
After the introduction section, the paper provides detailed
descriptions of the components in section II. Sections III
discusses the self–adaptive traffic control system. Section IV
gives the process of simulation of the system in Proteus 8.
Section V enumerates the step by step process of hardware
connections. Section VI discusses the results followed by
section VII for future scope.
II. COMPONENTS AND THEIR DESCRIPTION
Key components used in this prototype development are
Arduino Mega 2560 microcontroller board, IR sensors, red
light emitting diodes (LEDs), yellow LEDs, green LEDs,
connecting wires, Arduino power cable, and resistors. The
detailed description of these components are as follows:
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17
A. Arduino Mega 2560
Arduino Mega 2560 is an 8-bit ATmega2560
microcontroller development board. This development board
is used in prototyping of projects. As the name suggests, this
board is sufficiently wide for 54 input-output digital pins. 15
of these pins can be used for pulse width modulation (PWM).
It also contains 16 analog input pins, 4 serial ports (UART),
USB and a Power Jack, a 16 ܯܪݖ crystal oscillator, and a
reset button. The Arduino Mega board and its pin description
are shown in Fig. 1 [13]. Functions of these pins are as
follows:
1) Digital input-output pins: These pins are used for
receiving digital signals from sensors whose output is in the
form of a digital signal. These pins can also be used for digital
output, i.e. ͳ and Ͳ.ͳ denotes ͵Ǥͷ െ ͷܸ, and Ͳ denotes Ͳെ
ʹܸ.
2) Analog pins: These are used for analog input and
outputs in the range from Ͳ െ ͷܸ.
3) Reset: It is used to reset the microcontroller board.
4) Serial pins (ܴݔǡ ܶݔ): These are used for serial
communication. ܴݔ for receiving and ܶݔ for transmitting
TTL serial data.
5) PWM pins: These provide pulse width modulation
output. It means that these pins can be used to provide analog
output for the switching operation to multiple switches
working in coordination. Programming command
"analogWrite" (PWM pin, value) is used for this purpose.
6) ܸ
:It is used to provide input power to the Arduino
board if an external ͷܸ supply is available.
7) ͷܸ pin: It is used to supply ͷܸ to onboard
components and other external components if power is to be
given using the Arduino board.
8) ͵Ǥ͵ܸ:It is a regulated supply generated using a
voltage regulator on the Arduino Mega board to supply
external components (if needed).
9) ܩܰܦ pins: These pins are used to provide ground to
the external components.
Arduino board programming can be carried out using the
programming language C++ based Arduino IDE freeware.
Code is uploaded on the Arduino Mega board via USB D type
port. Digital input-output pins (20 pins in total), ܩܰܦ,ͷܸ pin,
and USB port (for code uploading and power supply from the
laptop) have been used in this making of traffic control
prototype.
B. IR sensor
The major components of an IR sensor are Op-amp, a
variable resistor; a LED to indicate output and an IR
transmitter and receiver. An IR sensor is shown in Fig. 2.
Description of these components are as follows:
1) IR Transmitter: It is an IR LED which transmits light
in infrared frequency. This light is not visible to the naked
eye because its wavelength is higher than visible light. IR
LED is white or transparent so that it can emit the maximum
amount of light.
2) IR receiver (photodiode receiver): A photodiode
conducts when light falls on it. Therefore, it acts as an IR
receiver. The photodiode in IR receiver contains a reverse
biased P-N junction semiconductor. When light falls on IR
receiver, it conducts current in the reverse direction. The
photodiode is similar to a LED and is covered with a black
surface so that it can absorb the most amount of light. The
amount of current flow is proportional to the amount of light
absorbed by the receiver.
3) LM358 op-amp: An op-amp is an electronic device
used in circuits with external feedbacks to provide voltage
amplification. This operational amplifier compares the
(a)
(b)
Fig. 1. Arduino Mega microcontroller board. (a) The actual
board,
and its (b) pinout diagram.
Fig. 2. The IR sensor.
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17
threshold voltage and the voltage across series photodiode
resistor. If the voltage drop across the photodiode series
resistor is higher than the threshold voltage, then the output
of the op-amp is high, otherwise low. For high output of op-
amp, the LED turns on, otherwise remains off.
4) Variable resistor: The variable resistor used here is
utilised to set the range of IR sensor at which the object is
detected.
The IR sensor contains three pins, ܸܥܥ,ܩܰܦ, and ܱȀܲ.
ܸܥܥ supplies power to the sensor, ܩܰܦ is connected to the
ܩܰܦ of Arduino, and ܱȀܲ provides output signal as sensed
by the sensor to Arduino Mega.
C. LED
The LED stands for light emitting diode, which is a
semiconductor device. It is quite comparable to a diode except
it emits light when a voltage of correct polarity is applied
across its two terminals, one of which is an anode and the other
is a cathode. The colour of the LED depends on the
semiconductor substance used to manufacture it.
The three colours, red, yellow, and green LEDs are used
as they appear in a traffic light arrangement. A red coloured
LED, and the arrangement of LEDs as per traffic light system
is shown in Fig. 3.
D. Resistor ͳ݇π
The operating voltage of a LED is about ͳǤʹܸ to ͵Ǥܸ
with forward rating current of ͳͲ െ ͵Ͳ݉ܣ. Since the output
current of Arduino digital pins measured using a multimeter
is ͵Ǥͺ݉ܣ and the voltage is ͷܸ, a resistor of ͳ݇π is
connected in series with LEDs so that the potential drop of
͵Ǥͺܸ comes across the resistor, and only ͳǤʹܸ appears across
LED so that it can operate accurately without malfunctioning.
III. THE TRAFFIC LIGHT SYSTEM
The architecture of the four-way road intersection with the
IR sensor module is shown in Fig. 4. Here A, B, C, D represent
the roads, and the arrows in each lane represent the movement
of traffic through that road. The IR sensors named IR 1 and IR
2 are facing with road D, i.e. the IR transmitter and the
photodiode receiver is facing towards road D. Similarly IR 3,
and IR 4 are for road A and IR 5, IR 6 and IR 7, IR 8 are for
road B and C respectively.
When traffic reaches in front of an IR sensor, the output
signal from the sensor is considered as the traffic density. The
more the number of IR sensors in a particular lane detects
traffic, the more is the density of traffic in that road. To give a
green signal to a road, the following rules are executed:
1) If no IR sensor is detecting signal, then each road will
be given a green signal in the following order B, D, C,
A, i.e. opposite road individually for a set amount of
time.
2) Traffic congests near to the inner IR sensors (i.e. IR 2
or IR 4 or IR 6 or IR 8) first, rather than the further IR
sensors (i.e. IR 1 or IR 3 or IR 5 or IR 7) at each road
because it is situated near to traffic light signal. If only
inside IR sensor of any road detects a signal, the
respective road will be provided a green signal until
traffic parameters change.
3) If only further IR sensors detect traffic, then rule 1
executes since the system considers that traffic doesn't
want to move forward.
4) If the internal IR sensors of two or more roads detect
traffic, then one road will open as per the priority
given inside the programming code and will be given
a green signal until the traffic density in that road
decreases. Then only the next road will be given the
green signal if traffic parameters for the other two
roads remain the same.
5) If both IR sensors of one road detect traffic, then that
particular road would be given green signal until the
traffic reduces in it.
6) If both the sensors of two or more roads detect traffic,
then one road will open as per the priority given inside
the code and will be given a green signal until the
IR 1 IR 2
IR 6 IR 5
IR 4
IR 3
IR 8
IR 7
A
B
C
D
Fig. 4. Architecture of four-
way road intersection with the IR sensor
module.
(a)
(b)
Fig. 3.
LEDs and resistors. (a) A red coloured LED, and (b)
arrangement of red, yellow and green coloured LEDs with
ͳ݇π
resistors as per traffic light system.
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17
traffic density in that road decreases. Then next road
will have a green signal if traffic parameters for the
other two roads remain the same.
7) If both the sensors of a road detect traffic, and in
remaining roads, only one sensor detects traffic, then
the road with both sensors will be given a green signal.
8) If both the sensors of two or more roads detect traffic
and in the remaining roads either one or no sensor
detects traffic, then road with two sensors detecting
traffic will be given a green signal as per the priority
mentioned in the code.
The single power supply gives power to the Arduino Mega
development board, Fig. 5. Arduino board supplies power to
IR sensors and traffic signals. The output signals from the IR
sensors and current position of traffic light signals move to the
Arduino Mega development board, which then controls the
normal working of the traffic light system as per the above
mentioned rules.
The control commands given by the microcontroller to set
or reset the traffic signals depends on the code algorithm fed
in it. Code algorithm is written in Arduino IDE software. A
flow chart mentioning the functions of code algorithm is
shown in Fig. 6. This flow chart is based on the rules
mentioned earlier in this section. There is a fixed time to
maintain the green signal in the ON state. After the fixed time
is over, the green signal of other road switches ON as per the
code sequence.
The self–adaptive traffic system model is a closed loop
control system, as given in Fig. 7. The output shows the
current status of the traffic lights. This status is compared
with the traffic density measured by the IR sensors, and error
is feed to Arduino Mega microcontroller board. Based on the
control program in the microcontroller, it provides the control
commands to the plant. The traffic signal is the plant. Plant
changes or retains its output as per the control commands by
Arduino. The status of traffic lights is then again feedbacked
to compare with actual traffic density, and this cycle repeats.
By this way, the decision of giving red or green or yellow
signal is decided. It can be easily comprehended that this
system is controlling the traffic lights automatically based on
the traffic density. Whereas the conventional traffic light
system is an open loop control system in which traffic lights
change based on the time intervals provided in them instead
of traffic density.
Fig. 6. Flow chart of self–adaptive traffic control system. It shows the
structure of the code algorithm fed into the Arduino board for controlling the
traffic lights.
Fig. 7. Closed loop control system of self–adaptive traffic signal
system.
IV. SIMULATION OF SYSTEM
The simulation of self–adaptive traffic signal system is
conducted on the Proteus 8 professional software.
Components chosen for the simulations are one Arduino
Mega 2560 microcontroller board, eight IR sensor modules,
four LEDs of red, yellow and green colour each. Proteus does
not have Arduino boards and IR sensors in its components list,
so the help of external libraries have been taken for this
purpose. Connections of these components are shown in Fig.
8. A set of two IR sensors and one LED of red, yellow, and
green colour each has been taken for showing one road, e.g.
road A. Connections of the road A set of components with the
Start
Are bot h
senso rs of any one road
hig h?
Collect the
data from all
the IR
sensors.
Open that road
.
Are bot h
sensors of two or more
roa ds high ?
Open the roads with
bot h sens ors hi gh as
per defi ned pr iori ty
se qu en ce .
Is one
senso r of two or more
roa ds high ?
Open the roads with
one se nso r hig h as
per defi ned pr iori ty
se qu en ce .
Is only one
senso r high? Open that road
.
Is no
senso r high?
yes
yes
yes
yes
no
no
no
no
Open each road in
orde r B, D, A,
and C
for the se t amount of
time.
yes
Microcontroller
(Arduino Mega)
Plant
(Traffic signal)
error
signal
Control
commands
Output
Feedback
signal
Traffic density
(measured by IR
sensors)
Fig. 5. Power flow and signal flow in self-adaptive traffic light
system.
Power
Supply
Arduino
Mega boa rd
Traffic
signals
IR sensors
Pow er flow
Signa l flow
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1
Arduino are shown with black coloured lines in Fig. 8.
Similarly, connections of road B set of components with
Arduino are shown with red coloured lines. Blue and green
colours are chosen for road C and road D set of components
respectively for better understanding. A Logicstate switch is
connected with every IR sensor module to create traffic
virtually. Logic ͳ in Logicstate shows that a vehicle is present
in front of an IR sensor, and logic Ͳ shows that no vehicle is
present in front of an IR sensor.
IR sensor detects the traffic and sends a signal to Arduino
microcontroller to take action based on the rules set in it.
These rules can be uploaded in Arduino using a hex file. The
hex files are generated using Arduino IDE software. After
successfully running the code algorithm in Arduino IDE, hex
files are generated and uploaded in Arduino Mega and all the
IR sensors. By making the virtual traffic with the help of
changing the positions of Logicstates, all the rules are verified.
It was found that the code algorithm was executing control
commands successfully and in result, was changing the traffic
lights as per rules.
Fig. 8. Simulation circuit of self–adaptive traffic signal system.
Connections of IR sensors and traffic light LEDs with the Arduino Mega
corresponding to a road is shown with unique colours. For road A, B, C, and
D, colours black, red, blue, and green are used respectively.
V. HARDWARE IMPLEMENTATION
In this section, step by step process of connections of
different components is provided in a manner such that
anyone can make this prototype by following this process. It
is difficult to show the connections of all the components in a
single figure, so connections for a single road is shown in Fig.
9. Following this figure, components of other roads can be
connected in the same way. A single road requires two IR
sensors, three LEDs of red, yellow, and green colour, and
some resistors. Step by step process of connection of these
components with Arduino Mega microcontroller board is as
follows:
Step 1. Place an Arduino Mega, two IR sensors, three
LEDs, and three resistors as per the Fig. 9. The bent
leg of LEDs is the longer leg.
Step 2. Make a common bus ground (Bus ܩܰܦ) and a
common bus positive (Bus ݒ݁). Connect Arduino
ܩܰܦ to Bus ܩܰܦ and its ͷܸ to Bus ݒ݁.
Step 3. Connect first IR sensor ܩܰܦ to Bus ܩܰܦ. It's ܸܥܥ
to Bus ݒ݁, and it's ܱȀܲ to Arduino ͷ.
Step 4. Connect second IR sensor ܩܰܦ to Bus ܩܰܦ. It's
ܸܥܥ to Bus ݒ݁, and it's ܱȀܲ to Arduino .
Step 5. Connect the yellow LED ܸ
to Arduino Ͷ. The bent
leg of the LED is the ܸ
. Connect the resistor of the
yellow LED to Bus ܩܰܦ.
Step 6. Connect the green LED ܸ
to Arduino ʹ and it's
resistor to Bus ܩܰܦ.
Step 7. Connect the red LED ܸ
to Arduino ͵ and it's
resistor to Bus ܩܰܦ.
Step 8. Connect Arduino board to the computer using a
USB cable and upload the code algorithm.
Step 9. Open serial monitor in Arduino IDE and change the
position of the onboard trimmer to change the
distance of detection of objects by IR sensor.
Step 10. Check all the rules by the above step. If any error
occurs, then troubleshoot.
Fig. 9. Hardware connections for self–adaptive traffic control system.
This shows the connection of components for one road, e.g., say road A.
Components for other roads can be connected in the same way with the
Arduino Mega.
On the basis of above steps, every connection has been
made for all the four roads. Final prototype of self–adaptive
traffic control system is shown in Fig. 10. This figure shows
the hardware connections of all the components with the
Arduino board. All sensors are placed at a fixed gap of ͳǤͷܿ݉
between them. The road length is kept constant to ͳͲǤͷܿ݉ for
all roads.
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18
(a)
(b)
Fig. 10. Final prototype of self–adaptive traffic control system. (a)
Top view and (b) side view.
VI. RESULT AND DISCUSSION
The design of self–adaptive traffic light system is easy to
realise, and this can be implemented in real-life, integrated
with a manual control system for better reliability. This model
is suitable for intersection with traffic flowing in one direction
in each road. In the practical implementation, eight IR sensors
were placed in total, two sensors for each road to detect traffic
density. Since traffic builds up at sensors near the signal
compared to the sensors away from the signal. The internal
sensors have more priority, i.e. if only the internal sensor is
detecting traffic, then that road is given a green signal. If only
external sensors are detecting traffic, then that road is not
given a green signal. In the practical implementation, many
sensors may be used rather than only two. The sensing time to
detect traffic from a sensor in hardware implementation is set
ͳͲݏ so that each road with highest traffic density is going to
open for ͳͲݏ until the sensor again detects the traffic density
in each road. The core of the project, the Arduino Mega
development board, is found capable of addressing ʹͷܾ݇ for
flash memory and ͺܾ݇ for the bootloader. The CPU speed
was ͳܯܪݖǡ and at each interruption of ͳͲݏ, the controller
reads the signal from sensors. In total 20 digital pins were
used, 12 for LEDs and 8 for sensors, the rest were unused.
VII. FUTURE SCOPE
The prototype model can also be used at an intersection
with more than four roads with easy modification in code. A
system with sound detection for emergency vehicles can also
be introduced so that in case of emergency, the vehicle does
not get stuck in the traffic.
Also, instead of Arduino Mega microcontroller board, a
more advanced board, Raspberry Pi could be used. Raspberry
Pi board is an IOT based device. It can smoothly run multiple
programs at one time, whereas Arduino can run only one
program at a time. Raspberry Pi board would eliminate the
need for wiring due to its wireless communication feature.
Traffic signals at the intersection usually have more than ʹͲ݉
distance between them, and making it wireless would be more
convenient.
This prototype is suitable for one lane or two lane roads.
Considering more lanes per road can be a useful extension of
this paper. More lanes will require more IR sensors to detect
traffic. Or instead of IR sensors, cameras can be used to take
a bigger and better look of traffic. Then, it will require image
processing tools and higher computation capability. It will
incur more cost, but the result will be better.
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