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Citation: Kujawski, A.; Nürnberg, M.
Analysis of the Potential Use of
Unmanned Aerial Vehicles and
Image Processing Methods to
Support Road and Parking Space
Management in Urban Transport.
Sustainability 2023,15, 3285. https://
doi.org/10.3390/su15043285
Academic Editor: Jingxu Chen
Received: 1 December 2022
Revised: 26 January 2023
Accepted: 6 February 2023
Published: 10 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Analysis of the Potential Use of Unmanned Aerial Vehicles and
Image Processing Methods to Support Road and Parking Space
Management in Urban Transport
Artur Kujawski * and Mariusz Nürnberg
Faculty of Engineering and Economics of Transport, Maritime University of Szczecin, Wały Chrobrego 1-2,
70-500 Szczecin, Poland
*Correspondence: a.kujawski@pm.szczecin.pl
Abstract:
Progressive urban density affects city centers especially and results in growing congestion,
lack of parking spaces, and increasing environmental costs of transportation, causing increased air
pollutant emissions and noise. These phenomena reduce the attractiveness of the city and result in a
degradation of the quality of life for its residents. In light of these phenomena, there is a clear need
for intelligent management of urban space using new technologies that would be complementary to
existing intelligent transportation systems. Expanding information resources obtained from mobile
cameras will have a positive impact on increasing the efficiency of transportation management and
use of limited space in city centers. It will also have an impact on reducing external transport costs
and increasing the quality of logistics services provided in the city. The main aim of the paper is to
develop a concept of a transport management system in cities using mobile vision systems mounted
on unmanned aerial vehicles. The model will concern the cases of lane occupation by freight vehicles
and the analysis of parking spaces in the city in order to improve their management. The results of
the developed model will contribute to the automation of the parking space management process
and increase the efficiency of the use of city parking space resources.
Keywords:
city logistics; transport management; intelligent transportation system; unmanned aerial
vehicle; urban space; image processing
1. Introduction
The city is a system of social, legal, functional and physiognomic structures that
together form its spatial structure. All functions of the city—residential, commercial or
recreational—are carried out in space. It is easy to notice that the processes of implementing
the city’s functions are associated with the constant movement of people and material goods
in the urban space, necessary for the course of these processes. Thus, they generate streams
of flow of both people, information and products. The physical layout of the functional
structures forces their users to make efforts in terms of relocation and its associated costs.
The efficiency of the city’s functioning in the real sphere is related to the degree of route
complexity, travel methods and the efficiency of traveling through space and is one of the
dimensions of the residents’ quality of life [
1
]. Proper functioning and development of
cities is influenced by the condition of the transport system and the level of amenities for
residents and visitors. Keeping the balance between an efficient transport system and the
comfort of living in the city is becoming a growing challenge.
Emerging technologies have a major social impact, resulting in changes in many as-
pects of everyday life. A growing worldwide trend is the use of innovative solutions using
Internet of Things (IoT) technology for public services. An extension of the IoT concept,
proposed and developed since the early 2000s, is applying this technology to means of
transport. We are then referring to the so-called Internet of Vehicles (IoV) concept, which is
a development of the earlier Vehicular Ad-hoc Networks (VANETs) system. IoV technology
Sustainability 2023,15, 3285. https://doi.org/10.3390/su15043285 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 3285 2 of 14
significantly extends the VANET concept by solving some of the main problems found
in traditional networks. These improvements include improved coordination between
different vehicles that move at a distance from each other, scalability, availability of infor-
mation, etc. In the IoV concept, each unit in the network can connect wirelessly with the
global Internet as well as roadside devices, other vehicles, drivers, passengers and even
pedestrians. In addition to information exchange, Internet connectivity provides flexibility
in extending the scale of the IoV network [
2
]. Considering how many everyday objects and
devices have access to the global Internet, we can now talk about a new concept that treats
this issue more broadly and includes all these devices and technologies under one name:
the Internet of Everything (IoE). IoE combines existing concepts related to smart devices
connected to the global internet. The increasing number of devices connected to each other
via the Internet or short-range wireless networks produces large amounts of data. This
data contains information from which, with the help of processing algorithms, it is possible
to obtain knowledge about the surrounding environment. This set of information and its
acquisition, storage and processing is known as Big Data. These tools are increasingly being
used in smart cities and this raises a further challenge for city authorities and residents as
to awareness of what purposes this data can be used for. Emerging IoT tools, usually used
independently, dedicated to specific applications should be integrated with each other as
much as possible in the future. The exchange of data between them can add significant
value for the management of smart cities [3,4].
Effective transport management is essential to ensuring high-quality and reliable
transport services, which are an important component of well-functioning smart cities.
Traffic congestion is a serious problem in the transport systems of growing cities, with
negative consequences and a negative impact on the comfort of urban life. Ensuring
continuous and collision-free traffic flow of vehicles requires appropriate management of
both the control infrastructure and the parking space, which is most often located in the
immediate vicinity of roads. These problems have long been known and discussed within
the literature [
5
,
6
] and inadequate management of parking space causes a number of varied
problems, the main of which is lane occupancy, which increases congestion and increases
the risk of collisions with other vehicles trying to avoid a parked delivery truck.
In this paper the concept of extending the existing methods of transport management
included in Intelligent Transport Systems is proposed, along with the analysis of the road
situation of parked and in-motion vehicles, using unmanned aerial vehicles, as well as
image processing and analysis methods.
The structure of the article is as follows: in the second chapter, selected aspects of
urban space management are presented with an emphasis on vehicle parking spaces and
the hazards that result from inadequate management of parking spaces and how this affects
traffic flows. The third chapter presents the concept and the methods of its implementation
with the use of various information and communication technology (ICT) tools and un-
manned aerial vehicles. The fourth chapter contains the results of the experiments carried
out, and the fifth chapter provides conclusions from the current research and planned
further projects in this direction.
2. Selected Aspects of Urban Space Management
Contemporary metropolises are characterized by different degrees of complexity of
infrastructure, which should be effectively developed, modernized and adapted to the
needs of the citizens. Many modern cities use the concept of smart cities for their manage-
ment, which is based on reorganising all spheres of city life in such a way as to manage the
city as efficiently and sustainably as possible through the creation and implementation of
modern information and telecommunications technologies. Along with the development
of these technologies, the number of devices that can supply the city management system
with extended data is growing. This data can be used to collect, process, analyse and
share information for residents. The multitude of wireless technologies in the urban space,
such as GNSS (Global Navigation Satellite System), Wi-Fi, Bluetooth, NFC (Near-field
Sustainability 2023,15, 3285 3 of 14
communication) and RFID (Radio-frequency identification) causes problems in the man-
agement of this heterogeneous data, so the smart city concept must involve the creation of
efficient information management mechanisms based on intelligent ICT systems with the
ability to process large data sets reliably and securely. As statistics show, the number of
people living in cities is increasing. This is associated with additional challenges of effective
management, taking into account sustainable development. This is possible thanks to
the implementation of emerging technologies and intelligent systems. Information and
communication technologies should be used effectively to improve the quality, productivity
and interactivity of city services. In order to reduce the consumption of resources and
the cost of living in cities, it is necessary to create reliable social and communication links
between citizens and government structures [7].
In the literature on the subject, there is no one consistent definition of a smart city;
nevertheless, among several concepts, common features can be found. First, the implemen-
tation of modern communication technologies is the basis of information systems oriented
at providing services to residents. An example is intelligent transport systems, the aim of
which is to “maximize the use of the existing road network through more effective traffic
control and management and its optimization in relation to the strategic goals in the area
of the broadly understood transport system.” [
8
]. Secondly is stakeholder participation in
creating the city through active participation in socio-economic initiatives. An example may
be the Urban Lab—“it is an instrument (. . . ) of cooperation between municipal authorities
and residents (. . . ), enterprises (. . . ) and scientific entities (. . . ), aimed at improving the
quality of life of residents through innovative solutions to identified problems (...) and
generating additional value using municipal resources.” [
9
]. Third is sustainable devel-
opment manifested in low-carbon technologies and rational use of resources. Particularly
noteworthy is the aspect of the high quality of life of the inhabitants and the harmony
between the natural and anthropogenic environment. People living in the city expect their
existence to provide them with satisfaction and thus a better quality of life than outside
the city.
The Smart City Model was developed by a team led by Rudolf Giffinger from the
Vienna University of Technology, presented in the 2007 Smart Cities report. The Ranking of
European medium-sized cities was performed [
10
]. This indicator is based on 31 factors
grouped into six characteristics: smart economy; smart people; smart governance; smart
mobility; smart environment and smart living. Each of the characteristics covers a specific
aspect of the city functioning as the living environment of its dwellers. The aspect of
urban transport is perceived in this model via three factors. The first is local, regional and
international accessibility, expressed in terms of ease of travel and ability to reach particular
destination. The second factor is a transport system that should be safe, sustainable and
innovative, not only for active travellers but also for residents, so that it does not cause
excessive external costs. The third factor is the availability of ICT infrastructure, allowing
inhabitants usage of modern communication methods and decision making [
11
–
14
]. This
factor is crucial for the information society, in which the quality of life is closely related to
access to information.
Becoming a smart city can take many forms depending on the type of metropolis. For
example, smart concepts will be implemented in modern and young cities (e.g., cities of
the United Arab Emirates), but a completely different approach is needed in European
countries, where cities were established in ancient times. One of the concepts of building
a city development strategy is the use of a model to analyse the sentiment of residents
and the implementation of urban space management related to the research results. Local
authorities are constantly innovating in cities, encouraging multinational companies and
individuals to pursue the concept of sustainable development related to renewable energy
and provide products and services for municipalities according to sustainable policy This
applies not only to industry, but also to the broadly understood awareness of individual
users, i.e., energy consumers. Local authorities of many cities encourage the use of urban
transport, in parallel with the introduction onto city streets of fully electric vehicles (EV), or
Sustainability 2023,15, 3285 4 of 14
at least hybrid ones, in order to reduce emissions of harmful substances into the atmosphere
(pollution and noise). Another challenge in this regard is for the authorities of modern cities
to ensure preferential conditions to encourage companies from the energy sector to invest
in cities. Public managers should take into account the multi-criteria decision-making
problems faced by energy companies when deciding on a location in the city. [15,16].
Traffic congestion in urban areas often occurs in areas of intense economic develop-
ment. Transport system planners and managers face the problems of effectively imple-
menting rules of conduct depending on the traffic situation, and for this it is necessary to
properly identify the problems. Availability of information for transport planning is the
main condition for finding any solution. The last few decades have seen the use of technical
solutions to acquire the data needed for transport planning using a number of independent
devices. Many traffic parameters such as traffic flow, vehicle speed, capacity, density, safety
and infrastructure availability have to be taken into account to collect information about
the current traffic situation. All of these factors are required in effective transport planning
to ensure efficient urban space management [17].
In the context of reducing congestion, attention should be paid to the characteristics
of Smart Mobility, especially factors such as local transport accessibility and sustainable,
innovative and safe transport systems. These issues are closely related to the area of
city logistics operations, thus mainly with the problem of supplying retail and service
points and last kilometre deliveries. Last mile delivery aspects are considered in the
context of Sustainable Urban Logistics. This is a concept belonging to Smart Logistics,
defined as a combination of modern technologies in the service of administration and
human activities. It creates the ability to anticipate problems and minimize their negative
impact on a given area of the city. The main advantages of this concept are the effective
coordination of resources and their supplies to achieve the assumed goals by eliminating
communication barriers between all points of the supply chain [
18
]. The large building
density of the city centres, and thus the accumulation of points where mainly commercial
and recreational functions are carried out, causes the concentration of the largest traffic
flows in this area. Paradoxically, allocating a certain part of the road lane along the entire
length of the street for parking spaces leads to the intensification of the phenomenon
of congestion. The mechanism of this phenomenon is as follows—the lack of a parking
space for loading/unloading activities results in the fact that the drivers performing these
activities occupy the right lane, drastically reducing the road capacity and increasing the
risk of an accident. According to Iwan et al. [
19
], unloading bays should be considered
as an environmentally friendly, efficient measure which supports urban delivery systems.
The advantage of this measure is reduction of congestion in city centres. Freight vehicles
parked directly on streets during the unloading activity significantly increase congestion.
Unloading bays are located to support the logistics of goods in cities in this way so as not
to interfere with road traffic. There is another advantage, in that delivery vehicles do not
cause excessive pollution of the environment by driving in search of a place to unload,
resulting in additional fuel or power consumption in ICEVs and EVs, or additional costs
and downtime related to traffic jams.
“Based on the cellular automata simulation, the analysis showed that application of
unloading bays in the studied sections in the city of Szczecin, Poland increased the traffic
fluidity by 8% on average. This allows reduction of pollutant emissions on average by:
•4% in the case of carbon monoxide (CO);
•5% in the case of hydrocarbons (HC);
•4% in the case of nitrogen oxides (NOX).” [19]
Therefore, in almost every medium-sized and large city, there is a shortage of space,
prompting municipal authorities to use tools to limit the intensity of traffic flows. Such
methods are:
Sustainability 2023,15, 3285 5 of 14
•
restriction of entry to the city centre—manifested in activities such as the introduction
of tolls or temporary limitation of accessibility (e.g., setting specific delivery times) or
limiting the type of vehicles allowed for traffic (e.g., clean transport zones);
•limiting the time spent in the centre (e.g., paid parking zones).
The latter solution is commonly used to force vehicle rotation in the limited number
of surface car park lots, due to insufficient space. The city authorities are trying to adjust
the rate for using the parking space so that drivers leave their vehicles for as short a time
as possible. According to the authors, the key is to determine the parameters of road
infrastructure use, such as:
•degree of rotation;
•degree of use of the car park lot (capacity);
•frequency of road lane occupancy.
The determination of these parameters will allow, first of all, better management of
this space, and secondly, it will enable the dissemination of this data among interested
parties, according to the smart city concept. This does not mean that data will be provided
directly to users (data on the number of free parking spaces). The degree of turnover or
occupancy should be seen as an indicator to be used in strategic decisions regarding the
designation of parking zones (differentiated in terms of cost and parking availability),
the degree of progression of parking tariffs (how much more expensive the subsequent
hours of parking are), or traffic organisation, as well as the future planning of new and the
reorganisation of existing parking spaces [20,21].
The layout of parking spaces within the parking lot is equally important. Parking
lots consist of a manoeuvring area—for entry, exit and siting the vehicles—and parking
space designated for stoppage or charging electric vehicles. It is recommended to place the
parking of trucks parallel to the axis of movement, while parking of passenger cars mainly
should be arranged according to an oblique angle of 45–60
◦
[
22
]. In city centres, usually
with congeste buildings, this type of parking space is applied, mostly due to the fact that
parking lots are located along streets between the traffic lane and the pavement.
3. Materials and Methods
Despite the variety of different types of sensors and devices included in intelligent
transportation systems, there is no such system for evaluating the parking situation of vehi-
cles in different parts of the city to continuously analyse this situation and lane occupancy
of delivery trucks. This paper proposes the concept of an automatic analysis system using
unmanned aerial vehicles, for the purpose of evaluating the rotation of cars in the parking
zone in the study area and the time of lane occupancy during the unloading and loading of
goods, as part of urban logistics. An additional advantage of such a system is that it can be
flexibly adapted to current needs without the need to interfere with existing infrastructure.
Figure 1shows a schematic of the proposed system. The system integrates several
information and communication technologies for video data transmission and storage,
information processing and automatic situation assessment using image processing algo-
rithms. This concept focuses on using existing image processing methods widely reported
in the literature, both static and moving camera methods mounted on UAVs [
23
–
29
]. It
should be noticed that most existing detectors based on image processing are fixed or
mounted on vehicles.
Sustainability 2023,15, 3285 6 of 14
Sustainability 2023, 15, 3285 6 of 14
Figure 1. Scheme of automatic analysis of lane occupancy and parking space management. Own
study.
Two main scenarios were conducted to study the traffic situation and its impact on
current vehicle traffic flows. The first concerned lane occupancy by delivery trucks and
how the average speed of vehicles on a road section changed before and during lane oc-
cupancy, and the second scenario concerned parking space occupancy in selected parts of
the city and the rotation of cars at different times of the day. Additionally, each of the
mentioned scenarios contained several subcategories. This was intended to show the pos-
sibility of using UAVs flexibly for current needs and involved only the application of ap-
propriate image processing algorithms.
The concept is to be able to automatically launch, inspect after predefined points and
land the UAV. For the purpose of continuous monitoring, it is necessary to have a docking
station for inductive charging of the drone and a minimum of two drones that will per-
form alternate inspections. It should be noted that there are restrictions on the perfor-
mance of flights, defined by the aviation law specific to particular countries. In the case of
Poland, drone operator licenses and notification of the operation to PANSA (Polish Air
Navigation Services Agency) are required to perform flights. Conducting the analysis was
preceded by preliminary research on how to fly over urban roads. The influence of UAV
flight altitude on the correctness of image recognition was determined. It was shown that
correct results are obtained for heights from 40 to 60 m above the ground. This gives the
possibility to analyse up to 10 lanes simultaneously [30].
Scenario 1. Lane occupancy detection and vehicle speed estimation.
It is a common practice to occupy a lane for loading/unloading of goods. Often this
happens when there is space on the roadside. The reason for this is that there is a tacit
acceptance of such practices and the operators, knowing that they will not face any con-
sequences, repeatedly practice such actions. This is highly dangerous to the on-going traf-
fic, where at the moment of loading/unloading operations, two lanes suddenly become
one, without any prior warning to the drivers. An additional hazard is lane blocking in
close proximity to intersections and pedestrian crossings.
During the study, lane occupancy was observed for several to over ten minutes, up
to 5 times per hour at different locations on the same study street.
The algorithms used to detect lane occupancy and study vehicle behaviour were im-
plemented in the Visual Studio environment using the Open Computer Vision library—
OpenCV [31]. The types of transformations used are illustrated in the diagram shown in
Figure 2.
Figure 1.
Scheme of automatic analysis of lane occupancy and parking space management.
Own study.
Two main scenarios were conducted to study the traffic situation and its impact on
current vehicle traffic flows. The first concerned lane occupancy by delivery trucks and how
the average speed of vehicles on a road section changed before and during lane occupancy,
and the second scenario concerned parking space occupancy in selected parts of the city
and the rotation of cars at different times of the day. Additionally, each of the mentioned
scenarios contained several subcategories. This was intended to show the possibility of
using UAVs flexibly for current needs and involved only the application of appropriate
image processing algorithms.
The concept is to be able to automatically launch, inspect after predefined points and
land the UAV. For the purpose of continuous monitoring, it is necessary to have a docking
station for inductive charging of the drone and a minimum of two drones that will perform
alternate inspections. It should be noted that there are restrictions on the performance of
flights, defined by the aviation law specific to particular countries. In the case of Poland,
drone operator licenses and notification of the operation to PANSA (Polish Air Navigation
Services Agency) are required to perform flights. Conducting the analysis was preceded by
preliminary research on how to fly over urban roads. The influence of UAV flight altitude
on the correctness of image recognition was determined. It was shown that correct results
are obtained for heights from 40 to 60 m above the ground. This gives the possibility to
analyse up to 10 lanes simultaneously [30].
Scenario 1. Lane occupancy detection and vehicle speed estimation.
It is a common practice to occupy a lane for loading/unloading of goods. Often
this happens when there is space on the roadside. The reason for this is that there is a
tacit acceptance of such practices and the operators, knowing that they will not face any
consequences, repeatedly practice such actions. This is highly dangerous to the on-going
traffic, where at the moment of loading/unloading operations, two lanes suddenly become
one, without any prior warning to the drivers. An additional hazard is lane blocking in
close proximity to intersections and pedestrian crossings.
During the study, lane occupancy was observed for several to over ten minutes, up to
5 times per hour at different locations on the same study street.
The algorithms used to detect lane occupancy and study vehicle behaviour were
implemented in the Visual Studio environment using the Open Computer Vision library—
OpenCV [
31
]. The types of transformations used are illustrated in the diagram shown in
Figure 2.
Sustainability 2023,15, 3285 7 of 14
Sustainability 2023, 15, 3285 7 of 14
Figure 2. Algorithms used in video image processing and analysis. Own study.
Figure 3 shows the video processing steps from the original image through binariza-
tion, background subtraction, morphological filters, blob finding and the result of vehicle
motion analysis. This method provides the ability to detect objects in motion in a specified
area and to detect objects that are treated as obstacles in the path of vehicle motion. It is
possible to estimate the speed of vehicles and the number of passing cars. The discussion
of the test outcomes is presented in Results.
(1). Original video frame (2). Gray scale (3). Thresholding / Gaussian blur
(4). Background subtracting (5). Dilating/Eroding (6). Closing
(7). Convex hulls processing (8). Finding blobs (9). Calculating bounding boxes
Figure 3. Video image processing steps and analysis methods used in the study.
Scenario 2. Parking space occupancy analysis.
Possibilities of using automatic UAV flights for inspection of parking spaces in the
centre of urban agglomeration were investigated (Figure 4). Flights were realized in auto-
matic mode based on predefined points—located in the middle of intersections, the start
and the end of the street along which the parking lot is located. The aim was to determine
the level of utilization of the parking lot (number of actually parked vehicles in relation to
the designed capacity) and the level of rotation (exchange of parked vehicles). This type
Figure 2. Algorithms used in video image processing and analysis. Own study.
Figure 3shows the video processing steps from the original image through binariza-
tion, background subtraction, morphological filters, blob finding and the result of vehicle
motion analysis. This method provides the ability to detect objects in motion in a specified
area and to detect objects that are treated as obstacles in the path of vehicle motion. It is
possible to estimate the speed of vehicles and the number of passing cars. The discussion
of the test outcomes is presented in Results.
Sustainability 2023, 15, 3285 7 of 14
Figure 2. Algorithms used in video image processing and analysis. Own study.
Figure 3 shows the video processing steps from the original image through binariza-
tion, background subtraction, morphological filters, blob finding and the result of vehicle
motion analysis. This method provides the ability to detect objects in motion in a specified
area and to detect objects that are treated as obstacles in the path of vehicle motion. It is
possible to estimate the speed of vehicles and the number of passing cars. The discussion
of the test outcomes is presented in Results.
(1). Original video frame (2). Gray scale (3). Thresholding / Gaussian blur
(4). Background subtracting (5). Dilating/Eroding (6). Closing
(7). Convex hulls processing (8). Finding blobs (9). Calculating bounding boxes
Figure 3. Video image processing steps and analysis methods used in the study.
Scenario 2. Parking space occupancy analysis.
Possibilities of using automatic UAV flights for inspection of parking spaces in the
centre of urban agglomeration were investigated (Figure 4). Flights were realized in auto-
matic mode based on predefined points—located in the middle of intersections, the start
and the end of the street along which the parking lot is located. The aim was to determine
the level of utilization of the parking lot (number of actually parked vehicles in relation to
the designed capacity) and the level of rotation (exchange of parked vehicles). This type
Figure 3. Video image processing steps and analysis methods used in the study.
Sustainability 2023,15, 3285 8 of 14
Scenario 2. Parking space occupancy analysis.
Possibilities of using automatic UAV flights for inspection of parking spaces in the
centre of urban agglomeration were investigated (Figure 4). Flights were realized in
automatic mode based on predefined points—located in the middle of intersections, the
start and the end of the street along which the parking lot is located. The aim was to
determine the level of utilization of the parking lot (number of actually parked vehicles in
relation to the designed capacity) and the level of rotation (exchange of parked vehicles).
This type of data can be helpful to identify places with particularly intensive use, which
can be used to properly calculate parking fees and to detect irregularities.
Sustainability 2023, 15, 3285 8 of 14
of data can be helpful to identify places with particularly intensive use, which can be used
to properly calculate parking fees and to detect irregularities.
Figure 4. Two selected example frames of an image undergoing the SURF (Speeded Up Robust Fea-
ture) algorithm to automated feature finding in parking empty space inspection. Own study.
It should be pointed out that the algorithm was not used to detect individual charac-
teristics of particular vehicles, so the method is not designed to control parking fees. Con-
trol of parking fee payment is possible based on data from terminals located by the park-
ing lot or payment systems in applications. This method, however, allows an assessment
only in quantitative terms, while the drone image allows for quantitative (comparison of
the planned number of vehicles to the number of parking fee payments) and qualitative
analysis, for example, considering the location of vehicles and the spaces between or next
to them, which is particularly important when parking parallel to the direction of traffic.
In addition, it allows assessment of rotation at the parking lot, i.e., the frequency of replac-
ing vehicles. In other words, UAV images allows spatial analyses of parking lots.
Only the occupancy and rotation of vehicles parking in an area were detected. Two
inspection methods were investigated. The first assumed continuous monitoring based
on a moving video image, while the second method consisted of taking pictures periodi-
cally and comparing them with each other, to detect the turnover in a given space and the
percentage occupancy of the entire parking area. The moving image method failed with
background subtraction algorithms and even with optical flow and panoramic back-
ground subtraction. One more drawback of this solution is the problem that roads and
parking lots are not in perpendicular alignment to the camera view. Therefore, it was de-
cided to use a sequence of images, which were then aggregated and plotted on a real map
of the area. This gave the possibility of measuring the total area of the parking lots and,
with the help of object feature detection algorithms, investigation of the empty areas. Fig-
ure 4 shows a sample of the image sequence and a measurement of the parking area on a
selected portion of the curved road using object feature finding algorithms associated with
the parking lot surface.
4. Results
To simulate the concept of using UAVs in both scenarios, the following simplifica-
tions were used. Due to the lack of access to a docking station to charge the UAVs, con-
tinuous drone operation was simulated by alternating two flying units. A total of over 4
h of video footage was collected. It was also decided to use the least computationally com-
plex image processing and analysis algorithms, so that inspection would be possible with-
out involving high-performance graphical workstations. Therefore, the use of artificial in-
telligence and deep learning methods was not considered.
In scenario 1, vehicle parking situations directly affecting traffic flows were investi-
gated on a selected road section. Table 1 shows the results of automatic sectional vehicle
speed measurements in the cases of with lane occupation and without lane occupation.
The studied road section was 80 m long and had two lanes in one direction.
Figure 4.
Two selected example frames of an image undergoing the SURF (Speeded Up Robust
Feature) algorithm to automated feature finding in parking empty space inspection. Own study.
It should be pointed out that the algorithm was not used to detect individual character-
istics of particular vehicles, so the method is not designed to control parking fees. Control
of parking fee payment is possible based on data from terminals located by the parking
lot or payment systems in applications. This method, however, allows an assessment
only in quantitative terms, while the drone image allows for quantitative (comparison of
the planned number of vehicles to the number of parking fee payments) and qualitative
analysis, for example, considering the location of vehicles and the spaces between or next
to them, which is particularly important when parking parallel to the direction of traffic. In
addition, it allows assessment of rotation at the parking lot, i.e., the frequency of replacing
vehicles. In other words, UAV images allows spatial analyses of parking lots.
Only the occupancy and rotation of vehicles parking in an area were detected. Two
inspection methods were investigated. The first assumed continuous monitoring based on
a moving video image, while the second method consisted of taking pictures periodically
and comparing them with each other, to detect the turnover in a given space and the
percentage occupancy of the entire parking area. The moving image method failed with
background subtraction algorithms and even with optical flow and panoramic background
subtraction. One more drawback of this solution is the problem that roads and parking lots
are not in perpendicular alignment to the camera view. Therefore, it was decided to use
a sequence of images, which were then aggregated and plotted on a real map of the area.
This gave the possibility of measuring the total area of the parking lots and, with the help
of object feature detection algorithms, investigation of the empty areas. Figure 4shows a
sample of the image sequence and a measurement of the parking area on a selected portion
of the curved road using object feature finding algorithms associated with the parking
lot surface.
4. Results
To simulate the concept of using UAVs in both scenarios, the following simplifications
were used. Due to the lack of access to a docking station to charge the UAVs, continuous
drone operation was simulated by alternating two flying units. A total of over 4 h of video
footage was collected. It was also decided to use the least computationally complex image
Sustainability 2023,15, 3285 9 of 14
processing and analysis algorithms, so that inspection would be possible without involving
high-performance graphical workstations. Therefore, the use of artificial intelligence and
deep learning methods was not considered.
In scenario 1, vehicle parking situations directly affecting traffic flows were investi-
gated on a selected road section. Table 1shows the results of automatic sectional vehicle
speed measurements in the cases of with lane occupation and without lane occupation. The
studied road section was 80 m long and had two lanes in one direction.
Table 1. Sample measurements in scenario one, divided into with and without lane occupancy.
Lane Occupancy Without Lane Occupancy
No. type Fv t [s] s (m) v (km/h) type Fv t (s) s (m) v (km/h)
1car 210
7.0000
80 41.1429 car 150
5.0000
80 57.6000
2
freight
280
9.3333
80 30.8571 car 220
7.3333
80 39.2727
3car 262
8.7333
80 32.9771 car 178
5.9333
80 48.5393
4car 261
8.7000
80 33.1034
freight
240
8.0000
80 36.0000
5car 218
7.2667
80 39.6330 car 250
8.3333
80 34.5600
6
freight
215
7.1667
80 40.1860 car 205
6.8333
80 42.1463
7
freight
262
8.7333
80 32.9771 car 150
5.0000
80 57.6000
8car 265
8.8333
80 32.6038
freight
200
6.6667
80 43.2000
9car 233
7.7667
80 37.0815 car 190
6.3333
80 45.4737
10 car 237
7.9000
80 36.4557 car 200
6.6667
80 43.2000
11 car 222
7.4000
80 38.9189 car 180
6.0000
80 48.0000
12 car 240
8.0000
80 36.0000 car 200
6.6667
80 43.2000
13
freight
390
13.0000
80 22.1538 car 175
5.8333
80 49.3714
14
freight
460
15.3333
80 18.7826 car 180
6.0000
80 48.0000
15 car 410
13.6667
80 21.0732
freight
270
9.0000
80 32.0000
16 car 435
14.5000
80 19.8621 car 180
6.0000
80 48.0000
17 car 290
9.6667
80 29.7931 car 220
7.3333
80 39.2727
18 car 270
9.0000
80 32.0000 car 245
8.1667
80 35.2653
19 car 260
8.6667
80 33.2308
freight
220
7.3333
80 39.2727
20 car 400
13.3333
80 21.6000 car 205
6.8333
80 42.1463
MS 31.5216 MS 43.6060
Where: Fv—Number of frames vehicle visibility; t—time of visibility; s—distance; v—sectional speed measure-
ment; MS—Mean Speed of vehicles.
For the purpose of determining vehicle movement parameters, technical image param-
eters such as the number of displayed frames per second (Fv) and time of vehicle presence
in the image were used. On this basis, an estimated segmental speed measurement (v) was
automatically obtained.
The lane occupation in this case occurred at a short distance from the intersection
and pedestrian crossings. The average speed of vehicles forced to evade the delivery
truck was about 31.5 km/h, while the speed was about 43.6 km/h when vehicles were
moving smoothly. The number of vehicles recorded in these two cases differed by about
23% in favour of the lane-block free situation. In addition to the obvious inconveniences
associated with the occupation of the lane, it should be mentioned that the risk of collision
of vehicles trying to bypass the van standing on the road during unloading increases
at this point, which also poses a threat to it and especially to the person handling the
loading/unloading processes.
Sustainability 2023,15, 3285 10 of 14
The distribution of traffic flows when a lane is occupied shows a large accumulation of
cars within the obstruction and a significant reduction in vehicle speed. Figure 5illustrates
vehicle flow situations during lane occupation and free flowing vehicles. The points
were read based on the pixel data of the centres of the quadrilaterals, determined by the
moving object recognition algorithm. The measurement points were collected at equal time
intervals, hence a smaller distance between the points means a reduction in the speed of
vehicle movement. A reduction in speed in the immediate vicinity of an obstacle, i.e., a
delivery truck during loading and unloading, can be clearly observed.
Sustainability 2023, 15, 3285 10 of 14
Figure 5. Visualized traffic flow of 20 selected vehicles; top figure shows flow without lane occupa-
tion; bottom figure shows flow with lane occupation. Own study.
In scenario 2, parking space occupancy measurements were implemented using ob-
ject feature detection algorithms and a geographic information system georeferencing al-
gorithm (Figure 6). Initially, areas of interest need to be defined, so that during image
processing there is no situation where an unwanted area is analysed that may distort the
final results. Samples of the parking lot pavement are necessary to start the analysis. The
more diverse the samples, the more accurate the test results. Drone flights were conducted
over a selected 2-week period from Monday to Sunday. Sampling (video data collection)
took place from 6 am to 9 am, from 2 pm to 4 pm with an interval of 30 min, and from 5
pm to 10 pm with an interval of one hour. Each drone flight lasted about 1.5 min and
collected about 20 high-resolution images each time, which were then processed and an-
alysed.
Figure 6. Automated parking space inspection. Own study.
Similar to scenario one, two drones were used to simulate continuous operation. It is
possible to inspect the terrain of parking spaces assuming that the surveyed area is well
imaged beforehand for subsequent automatic analysis. For a selected section of the city,
the occupancy rate of parking spaces was investigated, which averaged 78% occupancy
during the weekday between 6 am and 4 pm and had high vehicle turnover. In contrast,
between 5 pm and 10 pm, the average occupancy rate reached 93% with very low vehicle
turnover. It was observed that inspecting the area after dark with standard video cameras
is practically impossible. Figure 6 illustrates an example of the performance of the parking
lot surface feature detection algorithm during a continuous flight, which was used to cal-
culate the free space between vehicles.
The situation on public holidays showed a low turnover of vehicles and a very high
occupancy percentage of approximately 94% in the measured time. Figure 7 summarises
Figure 5.
Visualized traffic flow of 20 selected vehicles; top figure shows flow without lane occupation;
bottom figure shows flow with lane occupation. Own study.
In scenario 2, parking space occupancy measurements were implemented using object
feature detection algorithms and a geographic information system georeferencing algorithm
(Figure 6). Initially, areas of interest need to be defined, so that during image processing
there is no situation where an unwanted area is analysed that may distort the final results.
Samples of the parking lot pavement are necessary to start the analysis. The more diverse
the samples, the more accurate the test results. Drone flights were conducted over a selected
2-week period from Monday to Sunday. Sampling (video data collection) took place from 6
a.m. to 9 am, from 2 p.m. to 4 p.m. with an interval of 30 min, and from 5 p.m. to 10 p.m.
with an interval of one hour. Each drone flight lasted about 1.5 min and collected about
20 high-resolution images each time, which were then processed and analysed.
Sustainability 2023, 15, 3285 10 of 14
Figure 5. Visualized traffic flow of 20 selected vehicles; top figure shows flow without lane occupa-
tion; bottom figure shows flow with lane occupation. Own study.
In scenario 2, parking space occupancy measurements were implemented using ob-
ject feature detection algorithms and a geographic information system georeferencing al-
gorithm (Figure 6). Initially, areas of interest need to be defined, so that during image
processing there is no situation where an unwanted area is analysed that may distort the
final results. Samples of the parking lot pavement are necessary to start the analysis. The
more diverse the samples, the more accurate the test results. Drone flights were conducted
over a selected 2-week period from Monday to Sunday. Sampling (video data collection)
took place from 6 am to 9 am, from 2 pm to 4 pm with an interval of 30 min, and from 5
pm to 10 pm with an interval of one hour. Each drone flight lasted about 1.5 min and
collected about 20 high-resolution images each time, which were then processed and an-
alysed.
Figure 6. Automated parking space inspection. Own study.
Similar to scenario one, two drones were used to simulate continuous operation. It is
possible to inspect the terrain of parking spaces assuming that the surveyed area is well
imaged beforehand for subsequent automatic analysis. For a selected section of the city,
the occupancy rate of parking spaces was investigated, which averaged 78% occupancy
during the weekday between 6 am and 4 pm and had high vehicle turnover. In contrast,
between 5 pm and 10 pm, the average occupancy rate reached 93% with very low vehicle
turnover. It was observed that inspecting the area after dark with standard video cameras
is practically impossible. Figure 6 illustrates an example of the performance of the parking
lot surface feature detection algorithm during a continuous flight, which was used to cal-
culate the free space between vehicles.
The situation on public holidays showed a low turnover of vehicles and a very high
occupancy percentage of approximately 94% in the measured time. Figure 7 summarises
Figure 6. Automated parking space inspection. Own study.
Sustainability 2023,15, 3285 11 of 14
Similar to scenario one, two drones were used to simulate continuous operation. It is
possible to inspect the terrain of parking spaces assuming that the surveyed area is well
imaged beforehand for subsequent automatic analysis. For a selected section of the city,
the occupancy rate of parking spaces was investigated, which averaged 78% occupancy
during the weekday between 6 a.m. and 4 p.m. and had high vehicle turnover. In contrast,
between 5 p.m. and 10 p.m., the average occupancy rate reached 93% with very low vehicle
turnover. It was observed that inspecting the area after dark with standard video cameras is
practically impossible. Figure 6illustrates an example of the performance of the parking lot
surface feature detection algorithm during a continuous flight, which was used to calculate
the free space between vehicles.
The situation on public holidays showed a low turnover of vehicles and a very high
occupancy percentage of approximately 94% in the measured time. Figure 7summarises
the parking occupancy percentages for the study period by hours between 6 a.m. and
10 p.m. The inspection carried out during the selected hours provides an opportunity to
designate hours during the week with lower occupancy in order to potentially designate
time-segregated loading and unloading zones for freight vehicles.
Sustainability 2023, 15, 3285 11 of 14
the parking occupancy percentages for the study period by hours between 6 am and 10
pm. The inspection carried out during the selected hours provides an opportunity to des-
ignate hours during the week with lower occupancy in order to potentially designate
time-segregated loading and unloading zones for freight vehicles.
Figure 7. Automated parking space inspection. Own study.
The site selected for the survey in no case showed occupancy below 55% of the avail-
able space regardless of the hour of measurement on working days and 75% on non-work-
ing days.
The data obtained should not be considered as a source of dynamic information di-
rected to drivers, e.g., in the form of a presentation of the number of available or occupied
parking spaces. The reason for this is the aforementioned limitation of UAVs such as flight
time or weather conditions. The possibility of using drones as tools to collect kerbside
parking statistics at selected time intervals to build a parking space management strategy
should be considered. Instantaneous, dynamic information on the number of vacant
spaces requires continuous observation, so this study may allow the selection of sites for
future ITS devices and fixed cameras for continuous monitoring.
The experimentation with the use of unmanned aerial vehicles and image analysis
algorithms has proven that it is possible to apply them in a changing and dynamic envi-
ronment and, in particular, in places not equipped with other devices and methods from
the ITS field. The use of mobile cameras and the use of fit-for-purpose image processing
and analysis methods can assist in parking space management and, in the long term, ur-
ban space management, in particular during land-use decision-making processes and de-
cisions on future investments.
5. Discussion
The proposed concept of using unmanned aerial vehicles to analyse selected urban
traffic situations showed that it is possible to use drones as mobile video camera trans-
porters in places that are inaccessible to other Intelligent Transport Systems devices. It has
also been shown that, using appropriate image processing and analysis methods, it is pos-
sible to automatically analyse the congestion status of roads and parking spaces and their
impact on other traffic participants.
The results of the two independent research scenarios carried out provide the oppor-
tunity to draw out a number of interesting insights and provoke thought about the utili-
tarian use of combined unmanned aerial vehicles technologies, image processing algo-
rithms and traffic flow investigation methods. The results of scenario one concern the
analysis of lane occupation by other vehicles (mainly trucks during the process of unload-
ing goods) and the impact on traffic flows of other road users. A reduction in the speed of
vehicles avoiding congestion on the road is shown, as well as an increase in vehicle con-
gestion where the vehicle traffic stream is narrowed to one lane. Numerical information
Figure 7. Automated parking space inspection. Own study.
The site selected for the survey in no case showed occupancy below 55% of the
available space regardless of the hour of measurement on working days and 75% on
non-working days.
The data obtained should not be considered as a source of dynamic information
directed to drivers, e.g., in the form of a presentation of the number of available or occupied
parking spaces. The reason for this is the aforementioned limitation of UAVs such as flight
time or weather conditions. The possibility of using drones as tools to collect kerbside
parking statistics at selected time intervals to build a parking space management strategy
should be considered. Instantaneous, dynamic information on the number of vacant spaces
requires continuous observation, so this study may allow the selection of sites for future
ITS devices and fixed cameras for continuous monitoring.
The experimentation with the use of unmanned aerial vehicles and image analysis
algorithms has proven that it is possible to apply them in a changing and dynamic envi-
ronment and, in particular, in places not equipped with other devices and methods from
the ITS field. The use of mobile cameras and the use of fit-for-purpose image processing
and analysis methods can assist in parking space management and, in the long term, urban
space management, in particular during land-use decision-making processes and decisions
on future investments.
5. Discussion
The proposed concept of using unmanned aerial vehicles to analyse selected urban
traffic situations showed that it is possible to use drones as mobile video camera transporters
in places that are inaccessible to other Intelligent Transport Systems devices. It has also
Sustainability 2023,15, 3285 12 of 14
been shown that, using appropriate image processing and analysis methods, it is possible
to automatically analyse the congestion status of roads and parking spaces and their impact
on other traffic participants.
The results of the two independent research scenarios carried out provide the opportu-
nity to draw out a number of interesting insights and provoke thought about the utilitarian
use of combined unmanned aerial vehicles technologies, image processing algorithms and
traffic flow investigation methods. The results of scenario one concern the analysis of lane
occupation by other vehicles (mainly trucks during the process of unloading goods) and the
impact on traffic flows of other road users. A reduction in the speed of vehicles avoiding
congestion on the road is shown, as well as an increase in vehicle congestion where the
vehicle traffic stream is narrowed to one lane. Numerical information obtained from the
image data of mobile cameras allows automatic analysis of traffic situations.
Measurements of parking space occupancy and car circulation require continuous
monitoring and the results can be used for enhanced parking space management. One
scenario for the use of UAV measurements is to assess the need to designate unloading
bays available at certain hours of the working day (e.g., between 7 a.m. and 4 pm), and at
other times of the day and on non-working days these bays would become regular parking
spaces available to all residents. It has been observed that there is a need for flexibility
in the treatment of urban spaces according to needs that change dynamically during the
day and the week. The methods used to measure parking space occupancy can serve as a
complement to existing methods included in Intelligent Transport Systems or can be used
in locations without accessible ICT infrastructure. The advantage of the presented concept
is that there is no need to interfere with existing road infrastructure and there is a high
degree of mobility and flexibility in the selection of the analysis location.
6. Conclusions
The study was conducted to analyse the possibility of using unmanned aerial vehicles
as a concept for a system for automatic analysis of traffic situations, taking into account bad
parking practices that affect the flow of vehicle traffic, as well as the safety of all road users.
During the research, a number of conclusions and recommendations were made that would
need to be implemented in future work to develop the proposed concept. First, the concept
of automatic analysis of vehicle parking situations is possible to implement assuming the
following limitations resulting from the technologies used. The operating time of UAVs on
a single battery, which is about 25 min, forces the use of platforms that allow charging the
aircraft batteries between inspections or replacing them. Continuous human supervision to
operate such a drone is also necessary, for reasons of flight safety and the safety of other
airspace users. It is necessary to adapt the survey scenarios to the applicable aviation laws
of the location and, if necessary, to obtain the appropriate authorization to control the UAVs.
Secondly, the optimal flight ceiling (40–60 m) is above the tree crowns, which makes the
use of this method impossible in locations with a high density of trees. Similarly, in case of
bad weather conditions—rain or strong wind—the use of a drone is impossible
In model terms, the proposed concept extends the possibilities of studying traffic flows
in a flexible way without interfering with the existing infrastructure. The way parking
spaces are used strongly influences the actual traffic situation in cities. Unloading bays,
mentioned earlier, are an effective solution to the problem of unloading in the right lane,
provided that the rules of use are respected by users. Aerial inspection may contribute to
better control of compliance in this area.
An important element of the presented research is image processing and analysis
algorithms. Since the early 1980s, numerical methods have been developed in the field
of computer graphics and computer vision, so nowadays there are technologies available
on the market that provide all possibilities for analysing vision images in real time. The
plethora of available techniques can be a problem to be aware of when deciding which
methods to use. The presented concept and previous studies show that the use of simple
methods can allow the analysis of traffic parameters such as lane occupancy, vehicle speed,
Sustainability 2023,15, 3285 13 of 14
number of vehicles, area of the study region, etc. For these purposes, simple algorithms of
background subtraction and finding characteristic features of objects are sufficient, although
it is known that, in order to increase the reliability of recognizing objects in moving images,
it would be necessary to use available databases of various types of vehicles and to apply
at least Haar classifiers for reliable analysis. For years, the problem in the field of image
analysis associated with working under changing lighting conditions has been known.
Sudden changes in lighting or shading of objects by other objects introduce measurement
inaccuracies that must be taken into account.
In the case of transport systems, forecasting is most often concerned with estimating
the volume of future traffic, passenger or freight traffic in an existing or planned transport
networks. There is a need to take into account the problems of parking space occupancy
and related impediments when planning transport networks and deciding on changes
in traffic organisation, as well as planning temporary traffic organisation due to current
difficulties. The above-mentioned problems may be solved with the help of innovative
solutions proposed in this article, i.e., the application of a research approach consisting of
the use of ICT tools, which enable flexible conducting of analyses with a different level of
detail in relation to different transport infrastructure objects. Existing services that are part
of the broadly defined Intelligent Transport Systems, using devices such as traffic detectors,
scanners, radars and inductive loops, enable the provision of extensive data sets that can
serve as input to the development of models for planning and managing transport networks,
which are an integral part of well-functioning smart cities. The proposed concept extends
these capabilities with additional data collected in real-time using mobile devices with
mounted cameras. The information obtained through image processing can complement
data from stationary sources, especially in dynamic situations such as changes in traffic
organisation, street reconstruction and others.
Author Contributions:
Conceptualization, A.K. and M.N.; methodology, A.K. and M.N.; soft-
ware, A.K.; validation, A.K. and M.N.; formal analysis, A.K. and M.N.; investigation, A.K. and
M.N.; resources, A.K. and M.N.; data curation, A.K.; writing—original draft preparation, A.K. and
M.N.; writing—review and editing, A.K. and M.N.; visualization, A.K.; supervision, A.K. and M.N.;
project administration, A.K. and M.N. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by EEA and Norway Grants: PL-Applied Research-0017.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
This research outcome has been achieved under the GReen And SuStainable—
kNowledge EXpanded freight Transport in cities project financed under the Norwegian Financial
Mechanism 2014–2021. PL-Applied Research-0017.
Conflicts of Interest: The authors declare no conflict of interest.
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