=Paper=
{{Paper
|id=Vol-3723/paper19
|storemode=property
|title=The role of public transport network optimization in reducing carbon emissions
|pdfUrl=https://ceur-ws.org/Vol-3723/paper19.pdf
|volume=Vol-3723
|authors=Yurii Matseliukh,Myroslava Bublyk,Andriy Bosak,Marta Naychuk-Khrushch
|dblpUrl=https://dblp.org/rec/conf/modast/MatseliukhBBN24
}}
==The role of public transport network optimization in reducing carbon emissions==
https://ceur-ws.org/Vol-3723/paper19.pdf
The role of public transport network optimization in
reducing carbon emissions
Yurii Matseliukh1∗,†, Myroslava Bublyk1∗,†, Andriy Bosak1,†, and Marta Naychuk-
Khrushch1,†
1
Lviv Polytechnic National University, S. Bandera Street, 12, Lviv, 79013, Ukraine
Abstract
During the analysis of passenger transportation in a regional city with a population of less than 1
million registered residents and a developed public transport network, it was found that with the
beginning of the pandemic, a decrease in the main indicators of passenger traffic and a slight increase
in the volume of emissions of carbon-containing gases into the atmosphere were observed. As a result
of the analysis and classification of existing conceptual approaches to the optimization of the
organization of public transport networks to reduce carbon emissions, three main approaches were
established: prioritization of public transport, hybridization and electrification of vehicles and the
implementation of IT monitoring. When researching different types of neural networks, it was
proposed to use those that contribute to route optimization and road traffic prediction, namely:
recurrent, convolutional, and deep neural networks. After investigating the methods and means of
existing conceptual approaches to optimizing the organization of public transport networks to reduce
carbon emissions, there is an urgent need to create an attractive alternative to driving in cities,
reducing the carbon footprint of public transport and contributing to the sustainable development of
cities with the aim of implementation of the concept principles of a smart city.
Keywords
public transport, optimization, reducing carbon emissions, information system1
1. Introduction
Every year, the problem of environmental pollution by emissions of carbon-containing
compounds into the atmosphere increases with the growth of the population on the planet, the
number of vehicles that transport them, and the volume of emissions that these vehicles
generate. This problem is acute and sometimes critical in large cities with a multi-million
population. Transport, both passenger and freight, is one of the main sources of greenhouse gas
emissions, which leads to climate change. As a result, many cities around the world have begun
implementing measures to reduce carbon emissions. One of the most effective ways to reduce
carbon dioxide emissions in the transport industry is the implementation of the concept of a
MoDaST-2024: 6th International Workshop on Modern Data Science Technologies, May, 31 - June, 1, 2024, Lviv-Shatsk,
Ukraine
∗
Corresponding author.
†
These authors contributed equally.
indeed.post@gmail.com (Y. Matseliukh); my.bublyk@gmail.com (M. Bublyk); andriy.bosak@lpnu.ua (A. Bosak);
marta.naychuk-khrushch@lpnu.ua (M. Naychuk-Khrushch)
0000-0002-1721-7703 (Y. Matseliukh); 0000-0003-2403-0784 (M. Bublyk); 0000-0002-2944-2166 (A. Bosak);
0000-0001-9796-6546 (M. Naychuk-Khrushch)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
smart city, where one of the key tasks is to reduce carbon emissions by optimizing the network
of passenger transport routes. This substantiates the relevance of these studies.
2. Well-known studies on optimizing the public transport network
in reducing carbon emissions in a smart city
Scientists from various fields around the world have been working on solving this problem
for decades. The work of well-known scientists both in Ukraine and abroad is devoted to
the development of the theoretical and methodological foundations of the analysis of
passenger transport, the construction of modern information and communication models
and tools for optimizing the processes of the organization of passenger transport. The
scientists [14-28] whose scientific works caused significant changes in theoretical views
and practical approaches to the principles of building passenger transport analysis systems
based on the concept of a smart city, should be singled out in the following research.
Boz Y., and Cay T. [14] consider the main characteristics of a smart city and their importance
in transforming cities into smart ones and assign a key role to smart passenger transportation
services among sustainable city services. Based on the collected information, the authors
suggest that cities with a population significantly lower than large metropolises successfully
provide services for the transportation of passengers in public transport, which can be linked
to the concept of a smart city.
A significant role in the development of smart cities is played by information technologies,
which are rapidly developing since the beginning of the 21st century. So, Boreiko O., Teslyuk
V. [12, 15], Bushuev S. [12], Vanli T., and Akan T. [16] investigate the tools and means of
organizing passenger transportation within the concept of smart cities, where innovations,
business complexity, information and communication and transport infrastructure are key
factors of a smart city. Lytvyn V. et al [17], and Bublyk M. et al [18] substantiate the ways of
implementing the concept of smart specialization to transform both the entire Ukrainian
economy, in general, and the transport industry, in particular, into a circular one, where the
source of economic income of transport companies is not only the results of their economic
activity but also the reduction of emissions into the atmosphere of carbon-containing
compounds and waste processing, which pose a threat to all mankind.
Wang H., and Wang Y. [19] consider approaches to smart urban planning, where a critical
area of urban development is the development of passenger transportation aimed at creating a
sustainable, efficient and livable urban environment. The authors [12] conducted a
comprehensive analysis, identifying places with reduced carbon dioxide emissions, optimizing
the allocation of resources for economic efficiency and increasing aesthetic appeal for the
satisfaction of the community, and also proved the effectiveness of the proposed approach to
creating environmentally sustainable, economically efficient and aesthetically attractive urban
spaces. Wolniak R. [20], Guenduez A., et al. [21], and Jonek-Kowalska I. [22] study the means
of transforming cities into smart ones, where among the basic tools smart transport plays a key
role, in which the city authorities can effectively manage transformation and changes in in the
context of smart cities, creating new ones, destroying old ones and maintaining effective
institutional mechanisms. Dai Y. [11] examines the general basis of the transformation process
of a smart city and its transportation system, describes the role of stakeholders for cities at
various stages of transformation towards a smart city, singles out both information and
scientific and technological technologies as a tool for engaging stakeholders for transformation
to a smart city, and also offers four alternative scenarios of transformation to a smart city, where
smart passenger transport plays a significant role.
Nguyen, H. et al. [23] explore the essence of the IoT-enabled smart city concept, which
consists of many different areas, such as smart transportation, healthcare, and agriculture,
where AI advances are most likely to drive adoption growth. Internet of Things. The authors
[23 – 25] also presented the concept of a smart city based on the Internet of Things, the
prerequisites for its development and its components, where special attention was focused on
the study of the latest developments of smart cities with the support of the Internet of Things
and breakthroughs with the help of artificial intelligence technologies to highlight the current
stage, main trends and outstanding issues of implementation of artificial intelligence-based
Internet of Things technologies to create smart cities.
Chen Y., et al. [26], Chen Z., et al. [27], and Chen C., et al. [28] substantiate ways of increasing
the environmental and economic efficiency of a smart city, which is achieved through the
effects of technology, transport infrastructure, and energy conservation. The authors [26-28]
consider the smart city as an accelerator both for increasing economic efficiency and for
improving the environment due to the reduction of emissions of carbon-containing compounds
into the atmosphere and also prove that the efficiency of a smart city depends on the urban
transport structure and the characteristics of its components.
Tang J., et al. [7] investigate the mechanisms of interaction of smart energy and their impact
on carbon emissions in smart cities as a single and integrated system, justifying with
quantitative estimates the effects of smart energy on carbon emissions in a smart city, proposing
to use the approach of synthetic difference in differences and a spatial difference-in-difference
approach in models to estimate the impacts of smart energy on carbon emissions in a smart city
and demonstrate significant reductions in carbon emissions by accounting for the effects of the
spatial distribution of smart energy within a smart city.
Researchers [29 - 32] establish connections between smart cities and sustainable
development goals, which are not yet sufficiently studied. A. Sharifi and co-authors [29] identify
three main sustainable development goals of SDG 6, SDG 7 and SDG 11, to achieve directed
transformation of smart city technologies, even though there is a bias in reporting the benefits
of smart cities. The goals of sustainable development have been approved during the last twenty
years in the resolutions of international conventions of the UN General Assembly [33], in
Ukrainian state programs, resolutions, decrees and national reports [34-38]. The goals of
sustainable development such as SDG 6, SDG 7, SDG 11, SDG 12, and SDG 13 [33-38] recognize
the fundamental directions of the development of society, among which the provision of access
to clean water and proper sanitary conditions, availability and purity of energy, as well as
sustainable development of cities and communities, responsible consumption and the fight
against climate change. Accordingly, the management of passenger transportation in smart
cities is designed to create conditions for improving the quality of living in cities and reducing
emissions of carbon-containing compounds into the atmosphere [22, 39-42]. This requires
unprecedented efforts in all sectors of society, where the government and business must show
partnership to succeed in this process of transformation to smart cities, where access to a clean
environment without emissions, discharges and waste is ensured.
The concept of technosoliton was developed as the basis of innovative models for reducing
emissions of carbon-containing compounds, which assigns a significant role to the assessment
of damage and losses in highly polluting sectors of the economy to determine priorities in all
sectors of the economy, social development and environmental protection [8, 43]. Reducing
emissions into the atmosphere from the transport system has a significant impact on the
formation of a circular economy, improving the quality of life and achieving the goals of
sustainable development in smart cities [44 - 47].
Summarizing the research of the problem and the existing methods of developing passenger
transportation in smart cities, we see that today, a previously unsolved part of the general
problem is the search for effective approaches to optimize the system of organizing public
transport networks, which contribute to the reduction of carbon dioxide emissions into the
atmosphere for the construction of smart cities. Insufficient attention is also paid to the analysis
of passenger transport in cities and the identification of existing problems and prejudices that
prevent the introduction of the concept of a smart city into the city's transport system in
general, and passenger transport in particular. This indicates the need for scientific research in
the indicated direction, namely, the analysis of passenger transportation in cities and the
identification of existing effective approaches to optimizing the organization of public transport
networks, which contribute to the reduction of carbon dioxide emissions into the atmosphere
for the construction of smart cities, which is the goal of this work.
3. Results
Optimizing the system of organizing public transport networks, in general, has a positive effect
on improving the economic efficiency and economic results of all transport networks in the
settlement, however, this does not mean that this will contribute to the reduction of carbon
dioxide emissions into the atmosphere, the achievement of the goals of sustainable development
and development of the principles of smart cities.
3.1. The statistics analysis of passenger transportation in a city
For the study of passenger transportation in a regional city with a population of less than 1
million registered residents, data [49] were selected. According to the database [50], passenger
transport by public transport was carried out within the city in the amounts shown in Fig. 1. As
can be seen from the given graph (Fig. 1), the volume of passenger transportation by public
transport has a downward trend. The data are not monitored due to the introduction of martial
law [51]. This is due to the introduction of restrictions on the publication of open data during
the war. A similar downward trend is observed when studying the dynamics of passenger
transportation by bus public transport (Fig. 2), trolleybuses (Fig. 3) and trams (Fig. 4). According
to the data of the Main Department of Statistics in the Lviv region [49], an increase in the
volume of passenger traffic in electric public transport was observed. The sharp decline in
passenger traffic in 2020 is due to quarantine restrictions during the SARS‑CoV‑2 pandemic.
Table 1 shows data on the number of low-floor vehicles on routes, the share of low-floor
cars on routes, the total number of road transport carriers, compensation for repayment of
benefits to carriers by road transport, and the average annual cost of travel in communal road
transport.
The last update of the vehicle fleet took place in 2019 even before the start of the
SARS‑CoV‑2 pandemic, so we have an increase in the average age of passenger vehicles with
each subsequent year, which in 2023 was 13 years.
300.0
250.0 y = -0.0195x3 + 0.5873x2 - 11.436x + 264.56
R² = 0.8324
200.0
150.0
100.0
50.0
0.0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
Figure 1: Transportation of passengers by public transport compiled based on materials [49-
51].
160.0
140.0
y = 0.2979x2 - 11.082x + 161.56
120.0 R² = 0.8861
100.0
80.0
60.0
40.0
20.0
0.0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Figure 2: The passenger transportation by bus transport compiled based on materials [49, 52].
40 000 70 000
35 000 60 000
30 000 50 000
25 000
40 000
20 000
30 000
15 000
y = 7.2401x4 - 235.5x3 + 2498.5x2 - 8608.3x + 20 000 y = -519.33x2 + 7087.2x + 30354
10 000 32167 R² = 0.3138
5 000 R² = 0.8439 10 000
- -
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
Figure 3: The passenger transportation by Figure 4: The passenger transportation by
trolleybuses [49, 52] trams [49, 52]
Table 1
The main indicators of the passenger transportation by bus transport and their dynamics [52]
Years Number of Share of Total Compensation for Average annual
low-floor low-floor number of repayment of cost of travel in
vehicles on cars on road benefits to carriers communal road
routes, routes, % transport by road transport, transport, UAH
units carriers, unit a thousand UAH
2010 24 2.86 23 1014.5 1.75
2011 24 2.86 23 802.8 2
2012 138 22.73 5 - 2
2013 138 22.26 5 2491.6 2
2014 118 19.03 5 9011.3 3
2015 80 12.50 5 9250.8 4
2016 143 23.68 5 9850.01 4
2017 137 21.30 5 14989.7 4
2018 130 20.90 5 110368.0 5
2019 237 46.70 5 61313.2 7
2020 240 48.19 5 - 7
2021 240 54.55 5 192465.0 10
2022 194 49.36 5 104336.0 15
2023 222 52.11 5 208263.699 15
The volume of passenger traffic in bus transport in 2021-2022 has almost recovered to the
indicators at the beginning of the pandemic, although this is ensured by the increase in the
intensity of transportation (increase in the number of flights on the route, overcrowding of
transport, etc.). The decrease in the number of vehicles on the routes in 2022 was due to the
disabling of vehicles as a result of enemy shelling and their use to evacuate the population from
areas of intense hostilities. Therefore, it was decided that such variable data should not be used
when forecasting passenger flows, as this would only lead to unreliable results.
Table 2 shows the main indicators of passenger transportation by urban electric transport,
including the trams and trolleybuses.
Using the methods of linear and non-linear approximation based on the data given in Fig.1,
Fig.2, Fig.3, and Fig. 4, there were interpolated volumes of passenger traffic in public transport,
bus transport, and electric transport in Lviv until 2025. The highest determination indicators
(R² = 0.8324, R² = 0.8861, R2= 0.8439 and R2= 0.3138) for passenger transportation by public
transport, bus transport, trolleybuses and trams, respectively, have different polynomial
functions (1), (2), (3) and (4):
𝑦 = −0.0195𝑥 + 0.5873𝑥 − 11.436𝑥 + 264.56, (1)
𝑦 = = 0.2979𝑥 − 11.082𝑥 + 161.56, (2)
𝑦 = 7.2401𝑥 − 235.5𝑥 + 2498.5𝑥 − 8608.3𝑥 + 32167, (3)
𝑦 = −519.33𝑥 + 7087.2𝑥 + 30354. (4)
Table 2
The main indicators of passenger transportation in urban electric transport and their dynamics
[49-52]
Years Annual need for funds for Compensation for Average annual
repayment of benefits to repayment of benefits to fare in electric
electric transport electric transport transport (tram,
companies, thousand UAH companies, thousand UAH trolleybus)
2010 27772.13 48997 1
2011 33800.95 58688.1 1.25
2012 42001.57 65770.2 1.5
2013 35851.66 78664.5 1.5
2014 32540.3 81379 2
2015 32151.3 111674 2
2016 103625.7 29198.04 2
2017 122562.1 50000.0 3
2018 163000.0 55000.0 4.33
2019 277565.8 179070.0 5
2020 206000.0 158939.82 6.5
2021 260000.0 105000.0 7
2022 274925.0 183725.0 8.6
2023 452992.32 322404.3 8.6
70 000
y = -97.572x2 + 1591.4x + 44031
60 000
R² = 0.3260
50 000
40 000
30 000
20 000
y = 179.05x2 - 2722.4x + 36758
R² = 0.4301
10 000
-
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Transportation of passengers by trolleybuses, thousands of people
Transportation of passengers by trams, thousands of people
Polynomial trend by trolleybuses
Polynomial trend by trams
Figure 5: Dynamics of passenger flows in electric transport in Lviv and their calculated
values until 2025
The volume of passenger traffic in the electric transport of Lviv during the pandemic in 2021
did not restore its values to the indicators of its beginning (Table 2), although there was an
increase in the number of vehicles (there were 3 more trams and 5 trolleybuses running on the
routes of Lviv). Quarantine restrictions did affect the operation of electric transport since the
passenger flow is ensured not only by the increase in the number of vehicles but primarily by
the increase in the number of passengers (who live in the settlement and need to move). Despite
the optimism of the obtained values of passenger flows based on the obtained dependencies
(achievement in 2023-2024 of the pre-pandemic level of indicators for the volume of passenger
transportation by trolleybuses and trams), they are calculated as a result of interpolation of
retrospective data, which in no way take into account the decrease in the number of urban
populations as a result of the transition the Russian-Ukrainian war into a full-scale phase of
intense hostilities on a third of the country's territory, nor the suspension of transport
operations during almost constant and frequent air raids, etc. Therefore, the available data sets
are incomplete and do not contain sufficient data on factors important for making effective
management decisions.
Analyzing the existing datasets on emissions of pollutants into the air from mobile sources
in the Lviv region, we can see that the quarantine restrictions in 2020 increased the tendency
to their decrease (Fig. 6). Regarding the dynamics of carbon dioxide emissions by mobile sources
in the Lviv region, we have two different trends in two periods (ones from 2006 to 2016 and
others from 2016 to 2021) (Fig. 7) caused by a change in calculation methods. Thus, since 2006
the methodology has taken into account data on emissions from road, railway, aviation and
water transport, to which since 2007 emissions from production equipment (mobile) have been
added, and since 2016, the methodology takes into account data on the final use of fuel by bus
transport, which are obtained from the energy balance of all Ukraine. Since emissions of carbon
dioxide into the atmosphere from mobile sources of pollution in the Lviv region increased with
a slight increase in passenger transportation by bus vehicles (Fig. 7), the share of public
transport in the total amount of fuel consumed by transport should be taken into account.
160.0 2
Emissions of carbon dioxide,
140.0 1.8
1.6
Pollutant emissions,
120.0
1.4
thousand tons
million tons
100.0 1.2
80.0 y = -4.0336x + 145.69 1 y = 9E-06x6 - 0.0004x5 + 0.0084x4 -
R² = 0.9537 0.8 0.0743x3 + 0.318x2 - 0.6097x + 2.2043
60.0
R² = 0.4726
0.6
40.0
0.4
20.0 0.2
0.0 0
2007 2009 2011 2013 2015 2017 2019 2021 2006 2008 2010 2012 2014 2016 2018 2020
Figure 6: Dynamics of emissions of pollutants Figure 7: Dynamics of carbon dioxide emissions
into atmospheric air from mobile sources of into atmospheric air from mobile sources of
pollution [49-52] pollution [49-52]
Carbon dioxide emissions by the transport system according to international methods [48,
53-55]. As shown in [9, 56 - 60], light, medium, and heavy vehicles that carry out cargo
transportation make up 45% of emissions from the total volume of emissions. The sources of
emissions into the atmosphere caused by the operation of the transport system also include
passenger cars, minibuses, buses, trolleybuses, trams, funiculars, motorcycles, water, rail and
air transport and others. The same share of emissions (44%) is caused by passenger cars. This
shortcoming of the automobile market is critical today because according to experts [54],
emissions from light trucks will double compared to standard passenger cars in the next 5 years
(173 g carbon dioxide per mile vs. 251 g carbon dioxide per mile respectively) [45]. The mobile
sources that generate emissions into the atmosphere of carbon-containing compounds also
include aviation transportation – 6.8%, transportation by rail transport – 1.9%, water transport
– 1.8%, and motorcycles and buses – 1.9% [53]. Therefore, public transport accounts for less
than 4.8% of all emissions of carbon-containing compounds into the atmosphere. Based on the
data of Fig. 7, it can be calculated that, concerning all emissions, about 83 thousand tons on
average are generated by bus vehicles of public transport.
Therefore, the analysis of passenger transportation in a regional city, which is a typical
example of a settlement with a population of less than 1 million registered residents and a
developed network of public transportation, indicates the need to find effective approaches to
reducing atmospheric pollution by carbon-containing compounds by optimizing the
organization of public transportation networks based on the concept of smart cities.
3.2. Optimizing the public transport network within the concept of a smart
city and its approaches
As a result of the study of the achievements of modern scientific theories, presented in the
works of scientists [1-7, 11, 12, 14-31, 42, 61, 62], among the known directions of optimizing
public transport network within the concept of a smart city, we will single out three main
approaches: 1) giving priority to public transport on the road; 2) transition to electric and hybrid
vehicles; 3) implementation of monitoring information technologies (Fig. 8).
The approach to the priority of public transportation
The approach of hybridization and electrification of vehicles
The approach of implementing IT monitoring
Figure 8: Conceptual directions for optimizing the public transport network within the smart
city concept
In the first approach, in a smart city, reducing the number of vehicles on the road is achieved
by giving priority to public transport, reducing the attractiveness of its alternative - driving a
car. When public transport is faster and more convenient than driving, when public transport
is frequent and cheaper, more and more people will use it. Such a problem has recently become
more acute with the growth of migration flows in large European cities [63-65]. This
streamlining of traffic rules also contributes to sustainable urban development, as it increases
the availability of public transport and connects the outskirts of cities to the centre, which in
turn reduces the need to own a car and the need for an adequate number of parking spaces.
This increases the area for other uses, such as green areas or bike paths [24]. Overall, such a
process can help reduce urban sprawl and promote more sustainable models of urban
infrastructure development. As a result, this will enable the achievement of sustainable
development goals by reducing the number of cars on the road and the associated emissions of
carbon oxides into the atmosphere.
According to the second approach, changes in the smart city are focused on the structural
transformation of the fleet of vehicles in the direction of increasing the share of electric and
hybrid buses, which leads to the optimization of the types of vehicles themselves and can
significantly reduce the carbon footprint of public transport. Switching to electric or hybrid
buses will significantly reduce carbon dioxide emissions compared to traditional diesel buses.
In the third approach, a key role is played by information technologies that enable real-time
tracking of vehicles, monitor traffic on the road, predict the route, and reduce fuel consumption
by reducing both the length of routes and the time vehicles are on the road. Such optimization
also contributes to reducing the waiting time of passengers for the vehicle and their stay in the
vehicle.
We will analyze existing methods and means designed to optimize the functioning of
passenger transport systems in smart cities from the point of view of reducing carbon emissions.
They include the following (Fig. 9):
1. Intelligent transport systems.
2. Electric vehicles.
3. Transport sharing networks.
4. Smart applications and information systems.
5. Innovative payment systems.
6. Unmanned vehicles.
7. Information boards and announcement systems.
8. Networks of bicycle paths and equipped sidewalks.
9. Environmental monitoring systems.
We will consider in detail each of the methods of optimizing the functioning of the network
of passenger transport systems in smart cities from the point of view of reducing carbon
emissions (Fig. 9). Each tool has its characteristics and requires specific practical measures for
its implementation [2, 7, 11, 15, 16, 19, 21 - 29, 39, 42, 44, 45, 47, 69]. In the process of
implementing the concept of a smart city, of course, these tools should be adapted to the specific
circumstances and characteristics of the city's transport network to be successful.
Intelligent transport systems Electric vehicles Transport sharing networks
•C ensors •Electric cars •Carsharing
•Communication networks •Electric scooters •Bike-sharing
•Analytical algorithms •Electric bicycles
Smart applications and Innovative payment systems Unmanned vehicles
information systems •Contactless •And tired cars
•Mobile no app •Electronic •And tired buses
•Functional IS •Mobile applications •Drones
•Visualizers
Information boards and Networks of bicycle paths and Environmental monitoring systems
announcement systems equipped sidewalks •S tan u the environment
•In wagons •Wi-Fi access points •Controlling the concentration of
•At stops •Mini charging stations harmful ones substances
•Street spaces •Control of carbonaceous
substances
Figure 10: Methods and means of optimization of networks of transport systems of passenger
transportation
Intelligent transport systems use various technologies, including sensors, communication
networks and analytical algorithms, to ensure optimal management of public transport in the
city. Intelligent transport systems make it possible to reduce the waiting time of passengers for
public transport as much as possible, simplify payment and ensure safety for passengers.
Electric vehicles include electric cars, electric scooters, and electric bicycles, which ensure
zero emissions of harmful substances, minimize fuel costs, and ensure greater environmental
friendliness of the entire transportation system of a smart city.
Each country is developing its transport-sharing networks. The most famous in the world
are Carsharing and Bike-sharing networks. They provide an opportunity to use the vehicle as
needed, instead of owning it, which reduces the costs of its maintenance and contributes to its
more efficient use.
Networks of dedicated bicycle lanes and pedestrian-equipped sidewalks in smart cities create
a special transport environment where dedicated separated lanes and sidewalks for cyclists and
pedestrians ensure safety and convenience for their users.
With the rapid development of information technologies and mobile devices, smart
applications and functional information systems have appeared. They help passengers find
information about existing routes, the exact time of arrival of transport and the amount and
method of payment for services. Smart apps are also equipped with functions to ensure
compliance with the schedule, visualize routes and recommend a list of possible routes. This
not only increases the efficiency of using public transport but also reduces traffic jams.
Information boards and announcement systems provide passengers with the necessary
information about routes, existing schedules and their changes due to traffic delays directly at
stops and in vehicles. This contributes positively to the system of effective travel planning and
promotes the comfort of passengers during their stay in transport and at stops.
The introduction of information technologies into the banking system contributed to the
development of electronic payment systems. Today, it is customary to pay with contactless
cards, through mobile applications and other payment systems, which significantly simplifies
the process of paying for the use of transport, ensures speed and convenience for passengers,
and reduces waiting time at stops and being on the road.
Unmanned vehicles are becoming increasingly popular, including autonomous cars,
autonomous buses, and even drones. They are designed to ensure the efficiency and safety of
the transport system and to help reduce the number of accidents on the roads, no matter how
fantastic it looks.
The spread of the environmental monitoring system in cities provided an opportunity to
monitor the state of the environment and to control the concentration of harmful substances in
the air that enter there due to emissions of pollutants from transport and various production
processes in industry and utilities. The collected information is systematized by environmental
inspections and allows leaders of self-government bodies and the public to make relevant and
effective management decisions to reduce emissions and improve air quality in cities.
4. Discussion
The analyzed methods and means are only some of those possible methods and means that are
designed to optimize the functioning of passenger transport systems in smart cities from the
point of view of reducing carbon emissions. Their list was also made taking into account the
need to ensure the efficiency and convenience of the transport system for passengers.
4.1. The successful projects of optimizing the public transport network
Having analyzed the modern scientists [53, 57, 60, 61], among the successful projects of
optimizing the public transport network, which is used to create smart cities, we will single out
the following.
1. Several cities around the world have successfully optimized their public transport network
to reduce carbon emissions. For example, in Paris, the city government introduced a new public
transport plan that prioritized bus and bike lanes, reducing the number of cars on the road and
increasing public transport ridership. This plan resulted in a 14% reduction in carbon emissions
from transportation in the city [57].
Another example is the city of Bogotá, which introduced a bus rapid transit (BRT) system
that reduced travel time and improved connections in the city. This system reduced carbon
dioxide emissions by more than 300,000 tons per year, which is equivalent to taking 60,000 cars
off the road [53]. Bogotá's Bus Rapid Transit (BRT) system is one of the most successful public
transport network optimization projects in the world.
The system, known as TransMilenio, was first introduced in 2000 and has since expanded to
cover more than 112 kilometres of bus-only lanes, serving more than 2.6 million passengers
every day [53]. One of the key features of the TransMilenio system is the use of articulated
buses that can carry up to 160 passengers at a time. These buses run on dedicated lanes, avoiding
traffic jams and reducing travel time for passengers. The system also uses a prepaid smart card
system to speed up boarding and reduce delays. TransMilenio has succeeded in reducing carbon
emissions in Bogotá. By providing a faster and more efficient public transportation system, the
city has reduced the number of cars on the road, resulting in reduced air pollution and
greenhouse gas emissions. According to the World Resources Institute, the system cuts carbon
emissions by more than 300,000 tons per year, the equivalent of taking 60,000 cars off the road.
In addition to reducing carbon emissions, the TransMilenio system has also had a significant
social and economic impact in Bogotá. By improving connectivity and accessibility, the system
has increased mobility and economic opportunity for residents, especially those living in low-
income areas. The system has also contributed to the revitalization of public spaces, with many
bus stations designed to serve as community gathering places. Despite its success, the
TransMilenio system has faced some challenges, including peak-hour overcrowding and
maintenance issues. However, the city government continues to invest in the system, expanding
it to new areas and introducing new technologies to improve performance.
In general, similar to the TransMilenio system in Bogotá is the system of electric Australian
transport of the future (Electric Australian Transport Systems) [54], which is a clear example of
how optimization of the public transport network can reduce carbon emissions and improve
the quality of life of residents. Indeed, this success has inspired governments to implement
similar projects in other cities around the world, demonstrating the potential of public transport
to play a critical role in addressing climate change and promoting sustainable urban
development.
Several route optimization methods can be used to achieve the goal of this work. One of the
methods is the use of routing algorithms. These algorithms help to find the shortest route
between two points on the map, taking into account various factors such as traffic jams and
other obstacles. If such an algorithm is used for every route of public transport in Lviv, it can
help solve the problem of delays and reduce the time it takes passengers to move.
Another method is the analysis of data on the use of public transport in Lviv. Collecting data
on traffic, passenger flow and delays can help identify the most popular routes and destinations,
as well as identify problem areas on the route. This data can be used to optimize routes and plan
new routes that will meet passenger needs.
It is also possible to use information technologies to increase the efficiency of public
transport. For example, the installation of GPS systems in buses and trolleybuses will allow
monitoring of the movement of transport in real-time and provide passengers with accurate
information about the time of arrival of the transport at the stop.
Geographic Information Systems (GIS): allow us to determine the shortest route, taking into
account various constraints, including road conditions, public transport schedules and other
factors.
Passenger flow forecasting systems: allow forecasting of passenger flows at different times
of the day and on different routes, which helps improve transport efficiency and reduce waiting
time for passengers.
Using data from passengers' smartphones: allows us to monitor the movement of passengers
in real time and use this data to optimize transport routes.
Analysis of travel history data: allows us to identify the most popular routes and schedules
of public transport, which helps to optimize routes and reduce waiting times for passengers.
The use of smart stops and the "smart regulator" system: allows for the coordination of the
movement of various types of transport on the road section, which allows to reduce the
downtime of transport.
To optimize the route of each passenger, we can use machine learning algorithms that will
work based on real-time traffic data, availability of free seats in transport and other parameters.
For this, the intelligent route optimization system must have access to such data, in
particular, provided by GPS trackers in transport, open sources of traffic data and other
developed solutions.
In addition, to provide the optimal route for each passenger, the system must take into
account his personal preferences. For example, if a passenger wants to get to their destination
quickly, the system will offer routes that will allow them to do so. If the passenger is more
concerned about the comfort of the trip, the system will offer routes with fewer transfers or
with more comfortable transport.
Thus, an intelligent route optimization system should be able to collect, analyze and use a
variety of data to solve the problem of providing optimal routes for each public transport
passenger in real-time.
If a traffic jam appears on the road or the location of public transport changes, the intelligent
system should dynamically correct routes. To do this, it must have access to current information
about traffic, congestion and the location of public transport, for example, with the help of GPS
modules and a network of sensors placed on the roads.
When the intelligent system receives updated information about the state of the road and
the location of public transport, it must decide whether to leave the passenger on the current
route or offer an alternative route to reach the goal with maximum speed and comfort.
In case of a traffic jam or a change in the location of public transport, the intelligent system
should quickly make calculations and offer the optimal route. Information about traffic jams
and other obstacles should be displayed on the board in the cabin of public transport and on the
passenger's mobile device so that the passenger can make his choice regarding the optimal
route.
4.2. Types of neural networks for optimizing the public transport network
Different types of neural networks can be suitable for the task of improving the process of
route optimization and road traffic prediction, depending on the accuracy and speed required
for the system [66-70]. Here are some of them:
1. Recurrent Neural Networks (RNN) - These networks are used to work with sequences
of data such as time series. They can be useful for predicting road traffic based on
historical data.
2. Convolutional Neural Networks (CNN) - These networks are used for image processing,
but can also be useful for traffic prediction based on video streams from CCTV cameras.
3. Deep neural networks (DNN) - these networks are used to solve complex problems, such
as predicting the routes of complicated urban networks.
Neural networks can be used to predict traffic and optimize routes in real time, ensuring
even more accurate and faster operation of the passenger transportation system.
So, the organization of transport systems of passenger transport in smart cities with low CO2
emissions differs from the usual organization of the passenger transport system in several
parameters:
Use of electric or hybrid vehicles instead of cars with diesel or gasoline engines.
Using shared transport instead of private transport. For example, big buses instead of
small cars. This allows us to reduce the amount of traffic on the roads and reduce traffic
jams.
Using the network of high-speed trams or subways. These passenger transportation
systems provide high speed and a level of comfort, thereby becoming an alternative to
private transport.
Use of modern information technologies to optimize routes and monitor traffic flow.
This makes it possible to increase the efficiency of the transport system and ensure a
quick response to changes in the traffic flow.
Use of dynamic ticket pricing depending on vehicle load and service demand. This
makes it possible to reduce the total cost of transport for users and ensure optimal use
of transport.
All these parameters make it possible to improve the level of passenger transportation in
smart cities and reduce CO2 emissions.
Therefore, the use of neural networks contributes to route optimization and road traffic
prediction, which is one of the key tasks of optimizing public transport networks with low
carbon emissions into the atmosphere within the limits of the smart city concept.
Several route optimization methods can be used to achieve this goal.
1. One of the methods is the use of routing algorithms. These algorithms help to find the
shortest route between two points on the map, taking into account various factors such
as traffic jams and other obstacles. If such an algorithm is used for every route of public
transport in Lviv, it can help to solve the problem of delays and reduce the time of
moving passengers.
2. Another method is the analysis of data on the use of public transport in Lviv. Collecting
data on traffic, passenger flow and delays can help identify the most popular routes and
destinations, as well as identify problem areas on the route. This data can be used to
optimize routes and plan new routes that will meet passenger needs.
3. It is also possible to use information technologies to increase the efficiency of public
transport. For example, the installation of GPS systems in buses and trolleybuses will
allow monitoring of the movement of transport in real-time and provide passengers
with accurate information about the time of arrival of the transport at the stop.
Geographic Information Systems (GIS) has allowed us to determine the shortest route, taking
into account various constraints, including road conditions, public transport schedules and
other factors.
Passenger flow forecasting systems have allowed the forecasting of passenger flows at
different times of the day and on different routes, which helps improve transport efficiency and
reduce waiting time for passengers.
Using data from passengers' smartphones allows us to monitor the movement of passengers
in real time and use this data to optimize transport routes.
Analysis of travel history data allows us to identify the most popular routes and schedules
of public transport, which helps to optimize routes and reduce waiting times for passengers.
Using smart stops and the smart regulator system allows us to ensure the coordination of
the movement of various types of transport on the road section, which allows for reducing the
downtime of transport.
The activity diagram of the intelligent route optimization system for each passenger may
include the following stages (Fig. 10):
Collection of data on traffic and boarding/disembarking passengers at bus stops.
Data analysis to determine the current road situation and the load on vehicles.
Determining the most optimal route for each passenger based on their location and
destination.
Calculation of the estimated time of arrival at the destination and development of an
individual travel schedule for each passenger.
Figure 10: An activity diagram of optimization of networks of transport systems of passenger
transportation
Sending information about the optimal route and travel schedule to the passenger using
a mobile application or another form of notification.
Analysis of data on the execution of the schedule and the current situation on the roads
for the correction of the traffic schedule of transport routes.
The system optimizes the route for each passenger:
The system performs the first optimization iteration, choosing the stop closest to the
passenger's starting point and the route to the final stop.
The system checks the arrival time at the final stop and compares it with the time
indicated by the passenger.
If the arrival time is longer than the one specified by the passenger, the system chooses
another route taking into account the traffic schedule and stops that will ensure the
arrival on time.
The system displays the optimal route for each passenger with the specified time of
departure and arrival at the final stop.
The system sends optimal routes for each passenger to their mobile devices.
Passengers use the optimal route indicated by the system to move from the starting point to
the final point.
The system receives data about the routes used by passengers and analyzes this data for
further optimization of routes and planning of traffic schedules.
The system sends data on the use of routes and traffic schedules to the dispatching service
for further coordination and planning of transport operations.
After receiving the recommended route, the system notifies the passenger of relevant
information, including departure time, route and estimated time of arrival at the destination.
The passenger can confirm the recommended route or request another option.
If the passenger has confirmed the recommended route, information about this is transmitted
to the driver of the vehicle or the traffic flow management system.
The driver of the vehicle receives an appropriate message about the recommended route and
navigation information to deliver the passenger to the destination.
The system is constantly updated to take into account new data about traffic flow and vehicle
schedules, which allows it to offer optimal routes even when traffic conditions change.
The diagram shows the main stages of the system: receiving data from the passenger, finding
the optimal route, confirming the passenger's choice of route, and transmitting information
about the route to the driver of the vehicle. The diagram also shows that the system is constantly
updated to take into account new traffic flow data and provide the most optimal route for each
passenger. the priority criteria are the time spent on the road (less is better), the cost of the trip
(less is better) and the comfort of the trip (fullness of public transport cabin with passengers).
Traffic and congestion are also important to consider. To communicate with the passenger, we
can use his mobile device, touch panels in the cabin or other modern devices
The diagram shows the system states and the transition between them. The initial state is
waiting for the passenger to enter the current location and destination. After receiving this
information, the system goes into the "Analysis of the current situation" state, where it collects
and analyzes data about traffic, the location and movement of public transport, the cost of the
trip and the occupancy of the cabin.
Based on this data, the system enters the "Route Optimization" state, where it develops the
best route based on priority criteria. If the found route meets the passenger's requirements, the
system switches to the "Passenger message" state, where it notifies about the optimal route and
displays it on the passenger's mobile device, on the board in the public transport cabin or
through other modern devices.
If the found route does not suit the passenger, the system switches to the "Edit parameters"
state, where the passenger can edit his priority criteria and choose a more suitable route. After
that, the system repeats the process of analyzing and optimizing the route and switches to the
appropriate state.
If there is a traffic jam on the road or the location of the community changes to optimize the
route of each passenger, we can use machine learning algorithms that will work based on real-
time traffic data, availability of free seats in transport and other parameters.
For this, the intelligent route optimization system must have access to such data. In
particular, data provided by GPS trackers in transport, open traffic data sources and other
developed solutions can be used.
In addition, to provide the optimal route for each passenger, the system must take into
account his personal preferences. For example, if a passenger wants to get to their destination
quickly, the system will offer routes that will allow them to do so. If the passenger is more
concerned about the comfort of the trip, the system will offer routes with fewer transfers or
with more comfortable transport.
Thus, an intelligent route optimization system should be able to collect, analyze and use a
variety of data to solve the problem of providing optimal routes for each public transport
passenger in real-time.
If a traffic jam appears on the road or the location of public transport changes, the intelligent
system should dynamically correct routes. To do this, it must have access to current information
about traffic, congestion and the location of public transport, for example, with the help of GPS
modules and a network of sensors placed on the roads.
When the intelligent system receives updated information about the state of the road and
the location of public transport, it must decide whether to leave the passenger on the current
route or offer an alternative route to reach the goal with maximum speed and comfort.
In case of a traffic jam or a change in the location of public transport, the intelligent system
should quickly make calculations and offer the optimal route. Information about traffic jams
and other obstacles should be displayed on the board in the cabin of public transport and on the
passenger's mobile device so that the passenger can make his choice regarding the optimal
route.
In the state diagram, new states are shown as subsets of the Path to Destination state because
congestion or a change in the location of traffic can change the optimal route to the destination
(Fig. 11).
When the state "Traffic jam" or "Transport location change" is active, the system calculates
a new optimal route to the destination based on the current information about traffic and the
location of public transport. After that, the system returns to the "Traffic to destination" state
and continues to perform its main functions.
The deployment diagram displays the architectural structure of the system and the
interaction between its components at the resource level. In the context of optimizing public
transport routes, a deployment diagram includes the following components.
The routing server is a component responsible for optimizing routes and sending route
information to public transport drivers. This component can be installed on a separate server
or in a cloud service.
Database - a component that stores information about routes, schedules and other data used
to optimize routes.
A mobile application is a component used by passengers to obtain information about
schedules, routes and changes in the movement of public transport.
The driver application is a component used by public transport drivers to obtain information
about routes, changes in traffic and other important information.
Figure 11: A state diagram of optimization of networks of transport systems of passenger
transportation
Traffic sensors are a component that collects data about public transport and transmits it to
the routing server to optimize routes.
Internet connection - a component that provides communication between system
components and allows data to be transferred between them.
The notification system is a component that is responsible for sending notifications to
passengers about changes in the movement of public transport or about delays.
For a deployment diagram, it is necessary to indicate on which physical devices the system
is deployed, as well as which external systems interact with this system (Fig.12).
In this case, servers can be deployed on physical devices that ensure the operation of the
information system for optimizing public transport routes, as well as sensors that measure
public transport traffic indicators. External systems can be a fare payment system, traffic
monitoring and traffic congestion control, GPS systems for public transport drivers, etc.
The diagram shows that the route optimization system is deployed on two servers located
in different locations in the city. The system interacts with the smartphones of passengers and
drivers of public transport, as well as with the fare payment system and the traffic monitoring
and traffic jam control system. Public transport drivers use GPS systems that send traffic
information to the system to optimize routes. The collected data is analyzed and the system
calculates the optimal route for each public vehicle in real-time.
In the diagram, we can also use a neural network (RNN, CNN or DNN) to further improve
the process of route optimization and traffic prediction on the road.
Figure 12: A deployment diagram of optimization of networks of transport systems of
passenger transportation
5. Conclusion
As a result of an analysis of passenger transportation in a regional city with a population of less
than 1 million registered residents and a developed public transport network, it was found that
with the beginning of the pandemic, a decrease in the main indicators of passenger traffic and
a slight increase in the volume of emissions of carbon-containing compounds into the
atmosphere were observed. As a result of the analysis and classification of existing conceptual
approaches to the optimization of the organization of public transport networks to reduce
carbon emissions, three main approaches were established: prioritization of public transport,
hybridization and electrification of vehicles and the implementation of IT monitoring. During
the systematization of existing methods and means designed to optimize the functioning of
transport systems of passenger transportation in smart cities, the following groups were
substantiated: smart transport systems; electric vehicles; transport sharing networks; smart
applications and information systems; innovative payment systems; unmanned vehicles;
information boards and announcement systems; networks of bicycle paths and equipped
sidewalks; environmental monitoring systems. During the study of successful public transport
network optimization projects, it was shown that the success of implementing changes to
optimize public transport networks does not depend on the size of cities, but only on the
motivation of the participants in the change process (government, business and residents).
When defining the existing problems, the prejudices that prevent their implementation were
singled out as the most typical obstacle - resistance to changes, the ways of overcoming which
are a completed theoretical and practical task. When researching different types of neural
networks, it was proposed to use those that contribute to route optimization and road traffic
prediction, namely: recurrent, convolutional, and deep neural networks.
Therefore, the optimization of the public transport network plays a crucial role in reducing
carbon dioxide emissions in the transport sector. By providing an attractive alternative to
driving, reducing the carbon footprint of public transport and promoting sustainable urban
development, optimizing the public transport network can help reduce carbon emissions and
mitigate the effects of climate change. Successful projects in cities around the world have
demonstrated the effectiveness of optimizing the public transport network to reduce carbon
emissions, making this strategy critical for governments and cities to fight climate change and
improve the quality of life for citizens.
Further research should be directed towards the development of intelligent systems that will
contribute to the optimization of public transport networks with low carbon emissions into the
atmosphere within the concept of a smart city.
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