Smart Traffic Light Design Based on Histogram of Oriented Gradient

and Support Vector Machine

Yuliadi Erdani, Hendy Rudiansyah and Zahra Dhiyah Nafisa

Politeknik Manufaktur Bandung, Jalan Kanayakan 21, Dago, Coblong, Bandung, Indonesia

Keywords: Traffic Light, HOG, SVM, Webster, NodeMCU.

Abstract: Traffic congestion is one of the frequent problems in big cities, especially at intersections. Congestion occurs

because the setting time of traffic lights installed still using the fixed timing without consider to the ups and

downs of vehicle density that have the potential to cause congestion. To reduce these problems, the traffic

light timing system must be in accordance with the circumstances in each intersection path. In this study, a

traffic light simulation was made using the Histogram of Oriented Gradient (HOG) and Support Vector

Machine (SVM) methods to detect vehicles that would determine the level of vehicle density. Webster method

used to determine the duration of the traffic light based on the parameters of the density of vehicle. The output

of this simulation is in the form in traffic light prototype that is controlled by NodeMCU and monitored by

an application.

1 INTRODUCTION

Traffic congestion is one of the problems that often

occurs in big cities and generally occurs at road

intersections. The number of vehicles that continues

to increase from year to year, the growing population,

the imbalance between traffic demand and

transportation infrastructure, and the inability of

traffic management to control and reduce traffic flow

are one of the main causes of congestion. (Hartanti et

al., 2019). The right way to control traffic congestion

is by using traffic lights. (Mohanaselvi & Shanpriya,

2019). However, the use of traffic lights does not

always solve traffic congestion problems. In one

situation, the traffic light will be very helpful in the

smooth flow of traffic, but in another situation, it will

make the traffic jam worse. (Toar-lumimuut et al.,

2015). A common example is congestion during peak

hours, i.e. in the morning and evening. This

congestion occurs because the traffic light timing

settings used today still apply a conventional or fixed-

cycle traffic light (FCTL) timing system or

fixed/static red and green light durations without

considering real-time road conditions, such as vehicle

density in each lane of the intersection. (Ng & Kwok,

2020), (Siswipraptini et al., 2018). Such timing will

lead to the accumulation of vehicles on one side of the

intersection and is very prone to causing congestion.

With the different density levels at the intersection, a

smart traffic light cycle timing system is needed,

which can adjust the cycle time automatically.

Several studies have been conducted to overcome

these problems. Fibrilianty et.al, have made a traffic

light timing system based on vehicle density

detection using the Histogram of Oriented Gradient

(HOG) method. The output of this simulation is a

Traffic Light prototype that has been designed on

Arduino which is connected to a program that has

been designed in Matlab. Simulation of Trafic light

timings designed to get more efficient system

performance results compared to traffic lights with

automatic timers in general. (Fibriliyanti et al.,

2017). Another study designed an application using

MATLAB 2009a Software and Digital Camera as

processing and input of traffic light images to detect

density using the bwarea method. The results of this

system can determine the length of time the green

light is on based on the density of the road section.

(Toar-

lumimuut et al., 2015). Noval, C. et.al have

conducted

research on traffic light optimization using

the webster method. In this study, the webster

method is able to optimize traffic cycle time based on

vehicle density detection using infrared sensors.

(Noval et al., 2018).

The purpose of this research is to create a

simulation of a miniature traffic light that works

adaptively, namely a traffic light that adjusts the

176

Erdani, Y., Rudiansyah, H. and Naﬁsa, Z.

Smart Trafﬁc Light Design Based on Histogram of Oriented Gradient and Support Vector Machine.

DOI: 10.5220/0011738000003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 176-181

ISBN: 978-989-758-619-4; ISSN: 2975-8246

duration of the green light based on vehicle density

conditions in each lane.

2 RESEARCH METHODS

The system will control the work of the traffic light

by detecting the level of density in each lane of the

intersection, then the results of density detection will

determine the duration of the green light, where if the

lane is in a congested condition, the duration of the

green light will be longer than the free lane.

2.1 System Overview

Figure 1: System Overview.

In Figure 1 above, the general system is explained,

the first stage is image capture at a miniature traffic

light intersection using a webcam camera. The

captured image is then processed by the OpenCV

library programmed on a laptop/PC. The image is

processed to detect the level of vehicle density at the

traffic intersection using the HOG and SVM methods.

The detected vehicle density level will then determine

the length of time the traffic light works. based on the

webster method, and will activate the traffic light

according to the calculated time length and display it

on the application for the monitoring system.

2.2 System Flowchart

Figure 2 shows how the smart traffic light system

works. The camera will start working since the

system starts operating, then the camera will detect

the level of vehicle density using the HOG and SVM

methods. If the detected image is able to determine

the level of density, the system will continue to

Figure 2: System Flowchart.

process it for setting the length of time the traffic light

works based on the level of vehicle density that has

been detected. Next, the system will send the data that

has been obtained to the database. The data stored in

the database will then be read and will activate the

traffic light based on the data read in the database. The

data in the database will also be read for the

monitoring system on Android.

2.3 Domain Spesific Design

This specific domain design is a breakdown of the

specific system design of each domain.

2.3.1 Mechanical Domain Design

The design scheme of the miniature traffic

intersection is made using a two-lane traffic

intersection with a two-phase traffic light

arrangement. There are two cameras installed towards

each lane at the intersection and two traffic light

modules are used as system outputs and a TM1637

module as a display to show the length of time the

green light is on.

Smart Trafﬁc Light Design Based on Histogram of Oriented Gradient and Support Vector Machine

177

Figure 3: Miniature Traffic Light Intersection.

2.3.2 Electric Domain Design

The electrical design will be made of components

according to the needs of the tool. NodeMCU is used

as a microcontroller, traffic light module, TM1637

display module as an indicator to calculate the green

light timer, and LED as an indicator of online and

offline modes.

Figure 4: Electrical Circuit.

2.3.3 Informatic Domain Design

The vehicle density detection algorithm consists of

five parts:

a. Object Detection System Design

The first stage in making this system is character

extraction on the image data that has been collected

using the HOG method. (Pranoto et al., 2017).

Characteristic extraction is performed on an image

measuring 32x32 pixels. The image piece will be

divided into several overlapping blocks, in which

there are several cells, where each cell is composed

by several pixels. Then, in each cell, the horizontal

and vertical image gradient values are calculated

using the formulas in equations (2.1) and (2.2).

𝑓





𝑥,𝑦



𝐼



𝑥1,𝑦



𝐼𝑥1,𝑦 (2.1)

𝑓





𝑥,𝑦



𝐼



𝑥,𝑦1



𝐼𝑥,𝑦1

(2.2)

After that, the magnitude and orientation values

are calculated using the formulas in equations (2.3)

and (2.4).

𝑚



𝑥,𝑦







𝑓



𝑥,𝑦





𝑓



𝑥,𝑦



(2.3)

𝜃



𝑥,𝑦



𝑡𝑎𝑛





𝑓



𝑥,𝑦

𝑓



𝑥,𝑦



(2.4)

Next, normalization is performed using the

formula in equation (2.5), to avoid value differences

due to lighting differences.

𝑉





𝑉





‖

𝑉

‖



∈



(2.5)

From the calculation results, a histogram will be

built by voting the θ value according to the

predetermined bin value (binning process). Then, the

histogram of all cells in one block will be merged.

Figure 5: Flowchart HOG and SVM method.

Then the data that has been extracted will be

trained using SVM by giving the data class a value of

1 for positive images and -1 for negative images.

From the training results, a model in the .npy format

is produced as a model for the vehicle object detection

process.

In this vehicle object detection process using a

sliding window with each window extracting 32x32

pixel characteristics. The window moves from the top

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

178

left corner to the right, and then down by extracting

HOG and SVM process and will produce data in the

form of an image marked with a box indicating that

the boed image is a detected objects/vehicle. The

number of vehicles detected indicates the density

division described in table 1.

Table 1: Density Level Based on Number of Vehicle.

No Number of Vehicle Density Level

1 0 – 3 Low

2 4 – 7 Normal

3 > 7 High

b. Webster Method Timing Design

The Webster method is a concept to determine the

optimal cycle length and calculate the traffic light

time duration based on vehicle density and road

width. Factors that influence the Webster method are

as follows: (Noval et al., 2018)

1) Determine the order and number of phases

2) Determine the saturation flow (s)

Saturation flow (s) is the number of vehicle

departures in the queue when vehicles are at a

constant rate. In Webster's method, the saturation flow

can be estimated based on the road width.

Table 2: Saturation flow by road width.

Road width Saturation flow

3,05 1850

3,35 1875

3,65 1900

3,95 1950

4,25 2075

4,60 2250

4,90 2475

5,20 2700

3) Determine the normal flow (q)

4) Determine the level of traffic flow

5) Calculate the ratio between traffic volume and

saturation flow of each entersection

𝑦𝑖𝑞𝑖/𝑠𝑖

(2.6)

6) Determining lost time

𝐿2𝑛𝑅 (2.7)

7) Determine the optimum cycle time

𝐶0

1,5𝐿5

1𝑌

(2.8)

8) Specifies settings for effective green light

duration

𝐺𝑖

𝑦𝑖𝐶𝑜𝐿

𝑦

(2.9)

c. Database and Output Design

Data from density detection and green light duration

calculations that have been carried out are then sent

to the firebase database in real time. This data is

intended to be read by nodemcu to activate the traffic

light module at the miniature traffic light intersection

and read by the application for the monitoring system.

d. Interface Program Design

Figure 6 is a view of the thunkable application that

functions to monitor the system. The data displayed

is the density of each lane and the duration of the

traffic light. (green light, red light, cycle time length).

For the main page there are also buttons to activate

and turn off the system.

Figure 6: Monitoring application.

3 RESULT AND DISCUSSION

The automatic traffic light testing process is carried

out on a miniature traffic light intersection as an image

capture input. Image input is taken using a webcam.

3.1 Testing the Effect of Light

This detection test was conducted to determine the

effect of lighting on the number of vehicles detected.

Smart Trafﬁc Light Design Based on Histogram of Oriented Gradient and Support Vector Machine

179

Table 3: Test result for the effect of light.

Light

Actual

Number

Objects

Number

Detected

Objects

Difference

Error

10000

0 lux

1 1 0

0,00

2 2 0

0,00

3 3 0 0,00

4 4 0

0,00

5 5 0

0,00

6 6 0

0,00

7 7 0

0,00

8 8 0

0,00

9 9 0 0,00

10 8 2

0,20

Average (%) 2,00

200

lux

1 1 0

0,00

2 2 0

0,00

3 3 0

0,00

4 4 0 0,00

5 5 0

0,00

6 6 0

0,00

7 7 0

0,00

8 8 0

0,00

9 8 1

0,11

10 8 2 0,20

Average (%) 3,10

50 lux

1 1 0

0,00

2 2 0

0,00

3 3 0

0,00

4 4 0

0,00

5 5 0 0,00

6 6 0

0,00

7 5 2

0,29

8 6 2

0,25

9 6 3

0,33

10 8 2

0,20

Average (%) 10,07

From the test result in table 3, it can be sees that

the error on detection is higher in 50 lux light

conditions, which is 10,07%. From these result it is

concluded that the system runs better in light

condition >= 200 lux.

3.2 Density Detection Testing

Testing of vehicle density detection is carried out to

ensure that the level of vehicle density in the

observation area scenario can be detected by the

system. The density level description is L for low, N

for normal, and H for high, S for successful and US

for unsuccessful.

Table 4: Density detection test result.

Actual Condition Detection Result

Note

Number

Objects

Density

Level

Number

Objects

Density

Level

1 L 1 L

2 L 2 L

3 L 3 L

4 N 4 N

5 N 5 N

6 N 6 N

7 N 8 H US

8 H 8 H

9 H 9 H

10 H 7 N US

Accuracy value = (Correct data/Number of data) * 100% =

(8/10) * 100% = 80%.

From table 4, it can be seen that the system is

able to detect the level of density based on the number

of cars detected with an accuracy rate of 80%.

3.3 Green Light Time Testing

This test is conducted to determine the length of green

light time based on the density of vehicles on lane 1

and lane 2 that have been detected and compare it with

the results of fixed cycle time. L1 for lane 1 and L2

for lane 2.

Table 5: Green light duration test result based on density.

Density

Green Light

Duration (sec)

Cycle Time (sec)

L1 L2 L1 L2 Adaptive Fixed

L L 15 15 41 80

L N 17 21 49 80

L P 18 26 55 80

N L 21 17 49 80

N N 25 25 62 80

N P 28 31 71 80

P L 26 18 55 80

P N 31 28 71 80

P P 36 36 83 80

Total 536 720

From the table 5 above, it is known that the

adaptive traffic light timing runs according to the

density conditions of each lane, where lanes with low

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

180

density conditions will get fewer green light than

lanes with normal or high density conditions with a

total cycle time of 536 seconds, less when compared

to the fixed cycle time of 720 seconds.

4 CONCLUSION

Based on the results of design and testing in this final

project, it can be concluded that:

1. The vehicle detection system using the HOG and

SVM methods can work more optimally in

lighting conditions above 200 lux with an average

error of 1.56%.

2. The HOG and SVM methods are able to detect

vehicle density on the path with 80% accuracy.

3. The Webster method is able to optimize the traffic

light cycle time based on the density in each lane.

REFERENCES

Fibriliyanti, Y., Faradila, L. R., & Taqwa, A. (2017).

Implementasi Pengolahan Citra dengan Metode

Histogram of Oriented Gradient (HOG) untuk

Pengaturan Waktu pada Traffic Light Berdasarkan

Deteksi Kepadatan Kendaraan. Prosiding SNATIF, 35–

40.

Hartanti, D., Aziza, R. N., & Siswipraptini, P. C. (2019).

Optimization of smart traffic lights to prevent traffic

congestion using fuzzy logic. Telkomnika

(Telecommunication Computing Electronics and

Control), 17(1), 320–327. https://doi.org/10.

12928/TELKOMNIKA.v17i1.10129

Mohanaselvi, S., & Shanpriya, B. (2019). Application of

fuzzy logic to control traffic signals. AIP Conference

Proceedings, 2112(June). https://doi.org/10.1063/

1.5112230

Ng, S. C., & Kwok, C. P. (2020). An intelligent traffic light

system using object detection and evolutionary

algorithm for alleviating traffic congestion in hong

kong. International Journal of Computational

Intelligence Systems, 13(1), 802–809. https://doi.

org/10.2991/ijcis.d.200522.001

Noval, C., Virgono, I. A., & Saputra, R. E. (2018). Optimasi

Lampu Lalu Lintas Cerdas Menggunakan Metod

Webster Optimization Smart Traffic Light Using

Webster Method. 5(3), 6236–6243.

Pranoto, M. B., Ramadhani, K. N., & Arifianto, A. (2017).

Face Detection System Menggunakan Metode

Histogram of Oriented Gradients ( HOG ) dan Support

Vector Machine ( SVM ) Face Dtection System using

Histogram of Oriented Gradients ( HOG ) Method amd

Support Vector Machine ( SVM ). E-Proceeding of

Engineering, 4(3), 5038–5045. https://openlibrary

publications.telkomuniversity.ac.id/index.php/enginee

ring/article/view/5441

Siswipraptini, P. C., Martono, W. H., & Hartanti, D. (2018).

Reducing a congestion with introduce the greedy

algorithm on traffic light control. Journal of Physics:

Conference Series, 974(1). https://doi.org/

10.1088/1742-6596/974/1/012013

Toar-lumimuut, Tolah, R. E. P., Sengkey, R., & Rindengan,

Y. D. Y. (2015). Perancangan Simulasi Otomatis

Traffic light Menggunakan Citra Digital Studi Kasus.

E-Journal Teknik Elektro Dan Komputer, 4(4), 35–45.

Smart Trafﬁc Light Design Based on Histogram of Oriented Gradient and Support Vector Machine

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