An Effective Approach for Smart Parking Management

Abstract

Drivers and motorists get annoyed when it takes a long time to find a vacant space in a parking lot. Looking for parking has become a headache as the number of vehicles in urban cities and the cost of land concurrently increase. There is an urgent need for innovation in smart parking systems. Currently, investors and contractors pay laborers to operate and maintain smart parking systems. Staff duties may include opening and closing gates, giving directions to drivers and motorists, and managing payments associated with the lot. This article proposes a feasible, dependable, and smart algorithm for managing a parking system. This algorithm utilizes image processing techniques to provide real-time data. No labor is required to operate and handle the system. The system itself automatically handles all operations except maintenance. Furthermore, this algorithm is more cost-effective than other similar systems and equally effective. Numerous simulation scenarios were carried out on MATLAB to verify its developed approach. A comparison evaluation juxtaposes the proposed approach with other solutions in the literature. This evaluation clearly indicates that the presented method outperforms other solutions in terms of technologies being used, devices being utilized, and cost.

Full text

An Effective Approach for Smart Parking Management
Tawfeeq Shawly1*, Ahmed A. Alsheikhy2, Yahia F. Said2, Husam Lahza3
1 Department of Electrical Engineering, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah 21589, Saudi
Arabia
2 Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University,
Jeddah 21589, Saudi Arabia
Corresponding Author Email: tshawly@kau.edu.sa
https://doi.org/10.18280/isi.270511
ABSTRACT
Received: 2 June 2022
Accepted: 31 August 2022
Drivers and motorists get annoyed when it takes a long time to find a vacant space in a
parking lot. Looking for parking has become a headache as the number of vehicles in urban
cities and the cost of land concurrently increase. There is an urgent need for innovation in
smart parking systems. Currently, investors and contractors pay laborers to operate and
maintain smart parking systems. Staff duties may include opening and closing gates, giving
directions to drivers and motorists, and managing payments associated with the lot. This
article proposes a feasible, dependable, and smart algorithm for managing a parking system.
This algorithm utilizes image processing techniques to provide real-time data. No labor is
required to operate and handle the system. The system itself automatically handles all
operations except maintenance. Furthermore, this algorithm is more cost-effective than
other similar systems and equally effective. Numerous simulation scenarios were carried
out on MATLAB to verify its developed approach. A comparison evaluation juxtaposes the
proposed approach with other solutions in the literature. This evaluation clearly indicates
that the presented method outperforms other solutions in terms of technologies being used,
devices being utilized, and cost.
Keywords:
smart management system, smart cities,
efficient method, image segmentation,
digital services, automatic services
1. INTRODUCTION
Smart cities were once only imagined in science fiction
movies, but recently, the dream of building smart cities has
become possible with evolving technologies. These smart
cities utilize different technologies to provide civic services
and solve problems. They are data-driven, meaning smart
cities collect and analyze data to create and implement real-
time solutions. Figure 1 depicts a general perception of the
characteristics of a smart city.
Figure 1. Concept of the smart city [1]
Each smart city uses a different framework based on their
data and communication technologies to provide government
services, public services, and municipal services [2, 3]. One
common trait, however, is an intelligent network of connected
devices that allows a city’s function to be data-driven [2, 4, 5].
These connected devices create a sustainable city. The
technologies that resident’s access through smartphones and
hand-held devices can make cost-of-living more affordable,
improve the quality of life, and provide more efficient
solutions and services when properly programmed [6-10].
While some governments, like those in Barcelona and New
York, have implemented numerous technologies to transform
traditional cities into smart ones, others have started building
new cities based on smart city models. Neom, Saudi Arabia is
one such smart city. Figure 2 illustrates a proposed view of
Neom by the end of 2030 taken from the Saudi government’s
Vision 2030. The expected budget for building this city
exceeds 500 billion dollars. Currently, different companies
have initiated their works in Neom, and the infrastructure is
expected to be ready for residents by the end of 2024. The cost
is worth it. In New York City, the authorities save billions of
dollars every year by placing different smart solutions such as
smart grids for electricity, Wireless Network Sensors (WSNs)
for parking lots, and smart garbage solutions. Every smart city
utilizes different technologies to:
1) Improve the safety and security of the residents.
2) Attracting companies to invest in order to increase the
growth of the economy.
3) Make the surrounding environments more efficient.
Ingénierie des Systèmes d’Information
Vol. 27, No. 5, October, 2022, pp. 783-789
Journal homepage: http://iieta.org/journals/isi
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4) Provide numerous municipal services through different
technologies.
Figure 2. Fictional view of Neom city [11]
In big cities such as London, Paris, and New York, finding
vacant spaces to park vehicles is a big problem. Drivers and
motorists are forced to spend a long time searching for a spot
near their offices, hotels, or shopping centers. As the number
of vehicles increases daily, this problem gets bigger and bigger.
Authorities should look for alternative solutions. Many
technology service providers suggest different real-time
solutions to overcome this issue based on several technologies.
One such technology, the Internet of Things (IoT), is depicted
in Figure 3. Practical and consistent smart car parking systems
have become essential and crucial. Various solutions for smart
car parking systems have been developed to utilize all parking
spaces efficiently and successfully while the cost of labor [5-
8]. That said, municipal governments should consider the cost
of systems as well.
Figure 3. Sample of car parking systems using IoT
technologies [12]
This manuscript develops and presents an automated and
efficient smart car parking approach that can be easily
managed and maintained. This algorithm is cost-effective. It
just requires a camera to provide real-time data through images
or video streams of the considered parking area. These images
or video streams are processed to identify the total number of
vehicles that occupy spaces in the parking area and to verify
whether vacant spaces exist or not. After that, the electronic
devices direct drivers or motorists to the vacant spaces
available. Details about the presented and developed system
are provided in Section 3.
The rest of this paper is organized as follows: Section 2
contains related works and Section 3 provides details about the
proposed method. The discussion and results are presented in
Section 4, and Section 5 concludes the paper.
2. RELATED WORK
Dhamane et al. [2] proposed a smart parking system based
on an IoT technology. It used an ultrasonic sensor to detect
vacant spaces and vehicles. A web application was utilized to
display a real-time message about the status of parking lots.
Drivers and motorists could utilize their mobile phones to
search for vacant spaces prior to their arrival. The system
reduced fuel consumption, air pollution, and the time needed
to look for vacant spaces. However, the proposed approach in
this manuscript is more cost-effective than the system
proposed by Dhamane et al. since it requires no human labor
to operate it and utilizes only one camera to provide real-time
data. Additional features such as a metering system are easily
integrated into this paper’s method with no hidden cost.
Waqas et al. [3] developed a smart parking management
system using an image processing technique. This system used
one of the image processing algorithms to mark virtual vacant
spaces in parking lots to extract information about vacant and
occupied spaces. This information was utilized to guide the
incoming drivers or motorists towards vacant spaces. This
method contained two interfaces, one for vehicles and one for
administration. The authors intended to use their system in
areas where there were no such systems. This system
consumed much time processing data since it drew imaginary
lines for a considered parking lot and identified vehicles
whether they were parked correctly or not. The proposed
algorithm herein is faster since it just detects the number of
vehicles in the parking areas and the number of vacant spaces.
Sulthana and Badu [5] developed and implemented a real
smart parking system based on an IoT methodology and on-
site deployment. A Global Positioning System (GPS) and a
Global System for Mobile communication (GSM) were
utilized to track vehicles. An Android application, wireless
network sensors, and an active RFID were used to control and
manage gates. A Raspberry Pi device was used to control all
operations. The different components installed and utilized
make this system quite costly, but our proposed system is very
cost-effective. Its cost is almost nothing since it uses one
camera. No sensors are required. Readers can refer to Sulthana
et al. for additional information.
Chougula et al. [7] implemented an automatic smart parking
and reservation system based on IoT technology. This system
was controlled by an Android application to lower human
intervention. In addition, a web application was developed to
help drivers or motorists book vacant spaces prior to their
arrival using either a PC or a smartphone. Furthermore, an
Arduino microcontroller, a Database (DB) server, an RFID,
and a WIFI module were utilized in this system. These
components render this approach costly, not unlike those
proposed by Sulthana et al. The additional functions it offers
that send notifications and offer a metering module are easily
integrated into our proposed system.
Elsonbaty and Shams [8] implemented a smart parking
management system that depended on some Arduino
components, an Android application, and an IoT methodology.
IR sensors were used to validate vacant space data and
transmit this data through a communication module. An IoT
technology was involved to track available locations through
a wireless connection. This system requires several parts to
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perform the desired functions. One of its drawbacks was that
since its services depended on the internet, the system could
be completely off if no internet connection was detected by the
system. Our presented system requires no internet service as
the captured images or video streams are fed directly to a host
machine through a direct connection media.
Satyanarayana et al. [13] developed an advanced smart
parking system using an IoT technology. The technology
identified vehicles and showed the number of vacant spaces at
an entrance gate of the parking lot. In addition, it provided a
shortest path from the entrance gate to a vacant space and gave
directions to users through a display media. An Optical
Character Reader (OCR) and IR sensors were used to capture
vehicle plate numbers. This approach is similar to our
proposed system. However, the presented method herein is
more cost-effective compared to the implemented system
proposed by Satyanarayana et al. given the use of the single
camera instead of sensors. Interested readers can refer to
Satyanarayana et al. for additional information.
Mangwani [14] developed a smart parking system based on
an IoT method. This system utilized the IoT approach that
enabled drivers to monitor and book vacant spaces. It used a
sensor in every parking slot to send data to a central controller
in a server. It used the internet service to provide real-time
information to drivers. This system could become very costly
if the parking area was huge since every parking designated
space required a sensor to be installed in it. The cost of the
presented approach, in contrast, can be less than $100 for any
size parking lot.
Bhoyar et al. [15] developed a smart parking system based
on an IoT technology and cloud computing as well. This
system claimed to increase the efficiency of an existing cloud-
based smart parking system by developing a new network
architecture using the IoT technology. Using this system,
drivers could search for vacant spaces by utilizing a paid
service. This paid service considered parking lots within a
determined radius from a driver and displayed the number of
available vacant spaces near him or her. Wireless sensor
networks, IoT technology, and an RFID were utilized in the
implemented system. The authors provided no information
about experiments conducted to test and verify their system.
In addition, their system was costly since numerous WSNs,
RFIDs, and IoT technologies were involved along with a cloud
server and a web application. As stated, the proposed system
only requires one camera. No other tools are used unless there
is a need to integrate this approach with additional features or
systems, such as notification delivery for drivers who parked
their vehicles in the parking lots.
Jawad et al. [16] designed and implemented a smart car
parking system. It sent signals to either open or close gates
according to the status and conditions of a considered parking
lot. In addition, it worked based on a piezoelectric sensor,
which detected the weight of vehicles. An Arduino
microcontroller was used to control all operations along with
a seven-segment display. The proposed algorithm is more
cost-efficient than the developed system proposed by Jawad et
al., given its minimal need for equipment.
3. THE PROPOSED SYSTEM
The proposed system uses numerous image processing and
segmentation techniques in its operations. It is smart since it
requires no human intervention at all to run its operations. In
addition, these operations are fully automated. Furthermore,
several filters are utilized as well to either remove the noise or
improve the quality of captured images or video frames from
live video streams. Figure 4 illustrates components of the
proposed and presented system.
Figure 4. Components of the proposed system
The components in Figure 4 can be added to depending on
the requirements and needs of the user. Additional hardware
can be easily added upon request.
The presented algorithm provides several benefits,
summarized as follows:
i. Removes human labor cost.
ii. Reduces air pollution.
iii. Provides a sustainable environment.
iv. Increases the parking revenues.
v. Being environmentally friendly.
vi. Automates all operations.
vii. Enhances drivers’ experiences.
Figure 5 depicts a flowchart of the developed and
implemented system.
Figure 5. Flowchart of the proposed approach
The system starts by receiving real-time images or video
streams from the installed camera, which is directly connected
to a machine through either a coaxial cable or a fiber cable.
The connection medium depends on the requirements of the
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parking lot since the fiber cable provides more data
transmission in less time. If the camera sends images, then
these images are processed one by one. Otherwise, video
streams are processed frame by frame and converted into
frames using a built-in function in MATLAB.
The second step is the preprocessing stage in which noise is
removed and the quality of the captured image or video frames
is enhanced. Then, this data is converted into grayscale. This
process prepares data for the processing and segmentation
techniques.
The next step performs the utilized processing and
segmentation methods in MATLAB using numerous functions.
These functions require some toolboxes to be already installed
and run in a host machine. In this stage, the captured images
or frames are resized based on the size of the parking lot. Then,
a matrix of two dimensions dependent on the size of the
utilized images or video frames is created. Outputs from this
stage are used to determine the number of vacant spaces, the
number of already parked vehicles, and the lanes where these
vacant spaces are located. The processing and segmentation
techniques detect vehicles in the designated parking area,
determine the total number of available spaces, and assign it
to a parameter (a). Then, a threshold parameter (b) is assigned
to the obtained number of detected vehicles in the parking lot.
The equation to compute the number of vacant spaces
parameter (C) in the parking lot is:
C = a – b
(1)
If the value of C is 0, then, it means the parking lot is full.
In this case, the proposed algorithm keeps the entrance closed
and displays a message to inform drivers or motorists about
the status and conditions of the parking area. When C is > 0,
then, the system determines which nearest lane has vacant
spaces available. This lane is given to drivers or motorists
along with directions to that lane. The presented system then
opens the entrance gate.
The determination of the lane is performed using the
processing and segmentation approaches. The number of
vacant spaces in the determined lane is computed using
processing and segmentation methods that determine how
many vehicles are parked and the total number of vacant
spaces in the determined lane. Both values are assigned to
variables (d) and (e) respectively. The following equation is
used to compute the total number of vacant spaces (F).
F = d – e
(2)
After a vehicle enters and parks, the algorithm decrements
the total number of vacant spaces and increments the total
number of parked vehicles as follows:
F = F – 1
(3)
b = b + 1
(4)
The pseudo code of the proposed system is as follows:
Algorithm: Smart Parking and Management System
Input: Real-time images or live video streams.
Output: Number of parked vehicles, nearest lane to the
drivers or motorists, number of vacant spaces and directions
to these spaces.
1. Scan the parking lot every time a vehicle enters or
leaves.
2. If no vacant spaces are available, then:
3. Display a message to inform drivers and keep gate
closed.
4. Else.
5. Remove noise from captured image.
6. Transform the resultant image into a gray image.
7. Determine the dimensions of the parking area.
8. End of Preprocessing phase.
9. Assign the total number of parked vehicles to the
threshold variable.
10. Subtract captured image of current parking area from
the parking map structure.
11. For i = 1: length of parking area
12. For j = 1: width of parking area
13. Do the following:
14. Find resultant image from subtraction process and
compare it with the threshold.
15. If result > threshold, then:
16. Place 1 in the current index,
17. Else.
18. Place 0.
19. End // refers to the if statement.
20. End // refers to the inner loop.
21. End // refers to the outer loop.
22. Check which lanes have vacant spaces to, choose the
nearest lane to direct drivers to it.
23. Compute the total number of vacant spaces in the
determined lanes.
24. Open the entrance gate.
25. Display instructions for them to park.
26. Decrement the total number of vacant spaces.
27. Increment the total number of parked vehicles.
28. End of algorithm.
4. RESULTS AND DISCUSSION
Several simulation experiments were conducted in
MATLAB to validate all of the operations and verify the
correctness of the workflow of the proposed system. These
experiments were carried out on MATLAB R2017b which
runs on any machine that uses Windows as its operating
system. This version of Windows is Windows 11 Pro. This
machine’s specifications include an Intel chip of 8th
generation of i7, 2 GHz of the clock pulse, and 16 GB of RAM.
In addition, it is a 64-bit based system.
A free built-in software in Windows is utilized to draw the
images for the simulation experiments. In these images, a
vehicle is represented by X, as illustrated in Figure 6, which
shows a general parking lot with 3 lanes and 2 gates, one for
entry and another for exit. This parking lot accommodates a
maximum of 40 vehicles. The presented algorithm produces 3
subgraphs when the parking lot reaches its maximum capacity
and 4 subgraphs when vacant spaces are detected.
Two scenarios were conducted using the drawn images
from the software. Scenario 1 represents the parking lot when
40 vehicles are parked in it, meaning it has reached capacity,
and there is no place for a new vehicle. Scenario 2 refers to the
same parking lot when it holds 24 vehicles.
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Figure 6. Considered structure of the parking lot
Scenario 1
Figure 7 depicts the parking lot at full capacity with no
vacant spaces. In this scenario, drivers are instructed to look
for alternative parking areas. This Figure includes 3 subgraphs
that represent the obtained outputs from the proposed system.
The display message is also included in the Figure.
Figure 7. Outputs of the proposed system for Scenario 1
Figure 8. Outputs of Scenario 2
Scenario 2
Figure 8 illustrates the obtained outputs from the proposed
approach. It shows that the parking lot is occupied by 24
vehicles and 16 vacant spaces are available. Figure 9 depicts
the obtained numerical values from the presented method.
For Scenario 3 and Scenario 4, two images of real parking
areas were downloaded from Google, links for which are
available in [17, 18]. Scenarios 3 and 4 demonstrate the
system’s usage in real parking areas with vacant spaces.
Figure 9. Numerical results of Scenario 2
Figure 10. Outputs of Scenario 3
Figure 11. Numerical results of Scenario 3
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Table 1. Comparative evaluation results
Works
Evaluation criteria
Methodology
Accuracy
Utilized equipment
The cost
Dhamane et al.
[2]
IoT
Not
mentioned
Arduino, Radar, GSM sensors, cloud server, Android
application and Wi-Fi module
costly
Waqas et al. [3]
Image segmentation and
preprocessing
Not
mentioned
Camera, web server, DB server, and internet service
costly
Sulthana & Badu
[5]
IoT
Not
mentioned
GPS, GSM, WI-FI module, Raspberry Pi, IR sensors,
ultrasonic sensors, LED lights
costly
The Proposed
System
Image segmentation and
preprocessing
> 96%
One camera only
very
cheap
Scenario 3
A real parking lot with an 18-vehicle capacity is depicted in
Figure 10. Two vacant spaces are shown available. The
obtained results from the presented system are included.
Figure 11 shows the obtained numerical results after applying
the presented algorithm.
Scenario 4
Another real parking lot is shown in Figure 12, Figure 13
refers to the obtained numerical results of Scenario 4.
Figure 12. Second real parking lot
As shown in all 4 scenarios, the proposed algorithm
successfully managed all possible scenarios and produced
perfect results. The presented and implemented system in this
manuscript is very cost-effective since it just needs one camera
to operate and produce its outputs. Other developed works in
the literature discussed above detect vacant spaces with IoT
sensors, which are more costly. In contrast, the presented
method herein costs almost nothing since the CAMs are very
cheap. The expected price for a camera with a high-quality
lens and focus is less than $20. These alternative approaches
also depended on internet or WIFI services to operate and
function properly. This is a drawback: If any of these services
went down for any reason, the operations would be negatively
affected and even unavailable for a period of time. In contrast,
the proposed algorithm in this paper requires no internet or
WIFI service to operate as the CAM is directly connected to
the operating machine. This ensures that there would be no
service unavailability unless the running machine goes down
for unexpected reasons. Even in such an instance, the proposed
method is still more viable than those presented approaches in
the literature. Table 1 demonstrates the comparative
evaluation results between the proposed system in this article
with some works from the literature in terms of methodologies
employed, accuracy, utilized equipment, and the cost.
Figure 13. Numerical outputs of Scenario 4
5. CONCLUSION
The implemented system in this article is highly useful as
proved by the outputs of the presented scenarios. It is easy to
operate, maintain, and utilize anywhere. In addition to the
parking services, a smart metering system can be easily
incorporated into the system to determine a payment rate for
drivers accessing the parking lot. The integration of a
notification system for drivers or motorists is also possible
with some adjustments. MATLAB is used to validate the
proposed method, and its results show that the proposed
algorithm performs as intended and produces accepted outputs.
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