Article citation information:
Jacyna,
M., Żochowska, R., Sobota,
A., Wasiak, M. Decision
support for choosing a scenario for organizing urban transport system with
share of electric vehicles. Scientific
Journal of Silesian University of Technology. Series Transport. 2022, 117, 69-89. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2022.117.5.
Marianna JACYNA[1], Renata ŻOCHOWSKA[2], Aleksander SOBOTA[3], Mariusz WASIAK[4]
DECISION SUPPORT FOR CHOOSING A SCENARIO FOR ORGANIZING URBAN TRANSPORT
SYSTEM WITH SHARE OF ELECTRIC VEHICLES
Summary. This article
presents the issue of using decision support tools to select the variant of
organization of urban transport system. Two scenarios for the use of electric
vehicles were compared, considering not only their emissions and fuel
consumption but also the limited accessibility of conventional vehicles to the
city. The authors assume that the development of urban traffic organization
must go hand in hand with the challenges of planning sustainable urban mobility
and reducing harmful exhaust fumes. Furthermore, decision-makers should be
equipped with simple decision support tools to generate the best option
considering the expectations of transport users. The PTV
VISUM tool was used to analyse and visualize two
different organization scenarios for a selected city in Poland.
Keywords: decision
support, electric vehicles, urban transport system, travel demand model,
pollutant emissions, urban area, limited accessibility of the city, sustainable
urban mobility
1. INTRODUCTION
Currently, the
ways of shaping transport policy, both on the micro and macro scale; depend on
many factors based on a technological, social, environmental, and economic
nature. The phenomenon of global warming and the increasing intensity of
traffic on roads, especially on the streets of large cities, force the need to
care for the natural environment. This applies mainly to city dwellers for whom
daily traffic congestion is becoming increasingly common. In such a situation,
the search for solutions toward reducing pollution and noise and increasing the
safety of life in the city becomes a priority for decision-makers [13, 19, 29, 34]. These authorities are looking for new solutions to be
used in the design of traffic organization or to replace the rolling stock with
emission-free vehicles. The incentives for the use of electric vehicles are
very popular for both individual and public transport [25, 26]. It is also important
to use tools that support making the right decisions in the field of traffic
organization in the city [1].
As reported by
Cartenì et al. [5], the introduction of
electric vehicles in cities is one of the best options to meet both the goals
of sustainable development and mobility needs. The authors proposed a new
approach to e-mobility, replacing the fleet of "old" buses on the
Sorrento Peninsula (Italy) with hybrid diesel buses powered by an additional
photovoltaic system, to both improve the environment and obtain a return on
investment for the private operator managing the transport services. It is
estimated that this new type of bus can reduce greenhouse gas emissions by up
to 23%, with a 10-year payback period if a private investor is involved. Cartenì, on the other hand, in the article [4],
analyzed the benefits of using hybrid electric buses in modified public
transport services. In his analysis, the author considered the total carbon
footprint and not only the local impacts generated by this vehicle technology.
This approach was tested on a new bus line in the medium-sized city of Salerno,
Italy.
Planning
efficient and ecological transport systems, especially in urban areas, requires
the introduction of a transport policy that assumes the future social and
economic benefits resulting from the implementation of new environmentally
friendly systems of organizational and infrastructural investments [22]. The
transport infrastructure in cities is characterized by high density and the
possibilities of its expansion are very limited due to existing land
development. This was noted by Quak [36], Banister
[2], and Rietveld and Bruinsma [37]. Intensive
traffic in the city negatively impacts on the natural environment and living
conditions. It is mainly associated with exhaust emissions and excessive noise
during the day and night, which negatively affects human health. The impact of
pollutant emissions from transport on the environment is discussed in several
studies, including Jacyna et al. [18], Figliozzi [9], Levy et al. [27], and Pyza
et al. [34]. Restrictions on the movement of heavy vehicles are introduced by
various types of solutions [41, 46]. The problem of noise that reduces the
quality of life in the city is described by Tang et al. [42], Fuks et al. [11], and other authors.
Despite the
implementation of advanced solutions in vehicle construction, the high and
constantly growing emission of harmful exhaust gas compounds makes it necessary
to introduce restrictions on access to selected areas of cities for vehicles
with low ecological efficiency. The European Commission is also giving much
attention to this problem. The EU documents on transport policy allow for the
application of different rates of charges, which depend on the emission class of
the vehicle [16].
Many studies
show that formulating appropriate recommendations for shaping an
environmentally friendly transport system - especially in urban areas -
requires a series of complex and time-consuming analyzes [19, 20, 35]. To
improve the research process and conduct an appropriate number of various
analyzes, it is necessary to have a tool that allows simulations in various
boundary conditions and considers the established criteria for assessing the
effectiveness of the transport system.
Proper traffic
organization in the city consists in reducing the ecological costs of traffic,
increasing safety, and improving the quality of life of the inhabitants.
Therefore, a systematic approach to solving these problems is necessary. The
organization of traffic in the city must consider the specific conditions for
the implementation of a wide range of transport services, including individual
transport, collective transport, and city supplies, as well as other types of
services, such as repairs, services, medical care, etc. For example, Visser et al. [44] and Behrends
et al. [3] indicate that significant problems in this respect are variable
travel time, vehicle traffic restrictions due to MPW
(maximum permitted weight), traffic restrictions for vehicles that do not meet
specific exhaust emission standards, etc.
Bearing in
mind the increasing popularity and availability of electric vehicles and the
current pro-ecological trends, we propose a method of analyzing the impact of
the urban traffic structure on the emission of harmful compounds in the context
of the city's availability for various types of vehicles, in particular,
electrically powered vehicles. The method assumes the use of a transport model
built with the use of the PTV VISUM
tool. The HBEFA module (that is, The Handbook of
Emission Factors for Road Transport) was used to perform environmental
analyzes. Individual variants of traffic organization differ in the composition
of the fleet for vehicles. Fuel consumption and harmful emissions are criteria
to assess the scenarios analyzed. The study used the emissions of passenger
cars, trucks, and light commercial vehicles during the morning rush hour and
distinguished inbound and outbound traffic, as well as through traffic.
The following
issues are discussed in the remaining parts of this article. In the second
section, the literature in the research area is reviewed. Particular attention
is focused on the methods and tools used to assess transport emissions and
determine external transport related to the implementation of electric vehicles
and other low-emission vehicles. The next section presents the research
problem. A decision support method was described. It enables scenario analyses
of the different shares of electric vehicles in the fleet composition and the
assessment of harmful exhaust emissions. An important part of this article is a
case study based on the macroscopic transport model of the city of Bielsko-Biała (Poland), in which two experiments were
carried out with different shares of electric vehicles and restrictions related
to the entry into the city for high-emission vehicles. Analyzes were performed
considering the value of exhaust emissions and fuel consumption as assessment
criteria for the scenarios tested.
2. REVIEW OF
THE LITERATURE
The
study of the impact of transport on the environment is the subject of several
scientific studies. This research concerns many problem areas related to:
·
identification of negative effects of transport
activity on the environment,
·
ecological and social aspects of transport system
development,
·
influence of traffic conditions on environmental
conditions,
·
transport-related exhaust emissions,
·
transport-related emission,
·
relations between traffic conditions and quality of
life,
·
the impact of noise emitted by transport on the
quality of life in the city,
·
the assessment of safety and health effects of noise
regarding costs,
·
the impact of mobility
management and implementing restrictions on greenhouse gas emissions.
The
exemplary publications concerning the issues mentioned above are presented
synthetically in Table 1.
Tab.
1
Selected
publications related to the issues of the impact of transport on the
environment
|
Research area |
Publications |
|
identification of
negative effects of transport activity on the environment |
Schreyer, C. et al. [6] Jacyna, M. et al. [17] Wasiak, M. et al. [46] |
|
influence of
traffic conditions on environmental conditions |
Vaitieknas, P. et al. [43] Lu, J. et al. [28] Wang, L. et al. [45] Gundlach, A. et al. [14] |
|
transport-related
exhaust emission |
Proost, S. [33] Merkisz, J. et al. [30] Chamier-Gliszczyński, N.; Bohdal, T. [7] Kholod et al. [23] |
|
relations between
traffic conditions and quality of life |
de Palma, A.; Lindsey, R. [8] Levy, J.I. et al. [27] Qingyu, L. et al. [35] Jacyna-Gołda, I. et al. [20] |
|
the impact of noise
emitted by transport on the quality of life in the city |
Fuks, K. et al. [11] Galilea, P.; de Dios Ortuzar, J. [12] Jakovljevic, B. et al. [21] Filippone, A. [10] |
|
the assessment of
safety and health effects of noise regarding costs |
Müller-Wenk, R.; Hofstetter, P. [31] HEATCO [15] Korzhenevych, A. et al. [24] |
|
the impact of
mobility management and implementing restrictions on greenhouse gas emissions |
Shafiei, E. et al. [39] Shi, F. et al. [40] Schubert, T. et al. [38] |
The
list of scientific papers presented in Table 1 represents only a part of the
rich literature of world science, but the analysis of these publications shows
that many instruments lead to environmentally friendly changes in transport.
Overall evaluations of their effectiveness are also carried out, as well as
discussions on the transfer of negative effects of transport between different
areas of the transport network (for example, from the city centre) to
production and disposal sites of transport equipment and electricity generation
sites. Therefore, the issues of shaping an environmentally friendly transport
system should be individually considered for each area, given local conditions.
3. THE
RESEARCH METHOD
3.1. Notation
To describe the adopted research method, the
following basic notation is introduced:
3.2. General
assumptions
The main objective of
this research was to develop a transport model that would allow simulation
studies to be carried out to shape an environmentally friendly transport
system. This model was built using existing specialized software (for example, PTV VISUM) and is a decision
support tool in the selection of the optimal scenario for the organization of
the urban transport system. The presented form of the model organizes the
process of preparing the simulation environment, as well as the scope and
manner of simulations performed.
Analysis and
evaluation of the functioning of existing or planned systems of various types
require a model mapping of the features of the system that are important for
research purposes. The scenarios of changes included in set
With this in mind, it
was assumed that to assess the impact of electric vehicles in road traffic on
the reduction of harmful emissions in the city or to conduct other analyses of
pro-ecological changes in the urban transport system, it is necessary to have a
transport model for each variant, which must reflect:
·
actual fleet
composition with their characteristics, as well as their composition included
in the
·
the structure of the
real transport network, including its technical, economic, and organizational
characteristics, as well as links to the origins and destinations of the
traffic flow for the
·
the volume of
transport tasks (that is, transport demand) performed in the urban transport
system, given the type of traffic, that is, inbound, outbound and through
traffic for the
·
organization of traffic in
the urban transport system determining the assignment of traffic to the
elements of the transport network, considering changes in the
Given the above, the
model (
The formal
description of the proposed decision model was developed considering the
research described in [18].
In the adopted
approach, the scenarios represent different proposals for changes that lead to
a green and sustainable urban transport system. These changes may be for the
different periods of the analysis.
The composition of
the fleet can be determined considering the transport subsystem, mode of
transport, type of vehicles for the mode and subsystem, type of engine, vehicle
emission standard, type of pollutants from motor vehicles, and path in
transport relation
For scenario analyses
concerning the impact of the share of electric vehicles on the reduction of
exhaust emissions, the types of fleet composition presented in Table 2 were
introduced. The fleet compositions determined in this way constitute the basis
for the development of an appropriate number of demand segments, considering
the detailed structure of vehicles.
Tab.
2
Fleet
composition
|
Name of the fleet
composition |
Designation of the
type of the fleet composition |
Group of vehicles |
|
Urban |
U1 U2 U3 |
passenger car light commercial vehicle mix (trucks, truck trailers, articulated trucks) |
|
Average |
A1 A2 A3 |
passenger car light commercial vehicle mix (trucks, truck trailers, articulated trucks) |
|
Motorway |
M1 M2 M3 |
passenger car light commercial vehicle mix (trucks, truck trailers, articulated trucks) |
|
Electric |
E1 E2 E3 |
passenger car light commercial vehicle mix (trucks, truck trailers, articulated trucks) |
Another important
issue is the emission factors for individual modes of transport. They can be
determined using standard values derived from exhaust emission standards;
however, a much better approach is to use the results of detailed tests of real
road emissions. Such studies show that the amount of pollutant emissions is
also related to the length of the routes. This means that actual emission
factors should be considered following the distribution of route lengths for
homogeneous groups of transport relations.
In the
pro-environmental model of traffic organization, due to the scope of this
conducted research, we introduced restrictions on access to the city. These
restrictions may apply to vehicle types (for example, heavy goods vehicles),
vehicles with certain engine types, or vehicles that do not meet the required
emission standards.
In addition, the
model considers all standard constraints imposed on the traffic flow, that is,
constraints related to the balance of the stream in the nodes of the transport
network, the additivity of traffic and its non-negative nature, as well as the
constraints that guarantee the fulfilment of a specific transport task.
3.3. The
ecological indicator of transport in the city
Exhaust fumes are an
effect of the significant negative impact of transport on the environment. The
emission of harmful substances depends on the type of vehicle and its engine,
including compliance with the emission standard, speed, and distance travelled.
Accordingly, emissions of
(2)
where:
Among
vehicle pollutants caused by the flows in urban areas, the most harmful are
carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC),
particulate matter (PM), and carbon dioxide (CO2). They have
disastrous effects on the environment, ecology, and human health. Carbon (C) is
one of the most influential factors that accelerate global warming. The level
of emissions of these substances depends largely on the speed of the vehicles.
The
impact of the urban transport system on the environment, given the share of
electric vehicles, was estimated based on the air pollutant emission index
·
noise
emission,
·
accidents,
·
congestion,
·
water
and soil pollution.
3.4. The
scheme of the method
A specific approach
is required to support the decision on the choice of scenario to organize the
urban transport system with the share of electric vehicles to reduce exhaust
emissions. The general scheme of the method is shown in Figure 1. It assumes
the implementation of the transport model. However, to perform this type of
analysis, an appropriate level of detail of the model is necessary.
Fig. 1.
General method framework
In addition to the assumptions about the
fleet composition related to the different scenarios, it is necessary to define
the temporal and spatial scope of the study. The period of analysis depends on
the directions of the transport policy. It can cover both the 24-hour and a
selected shorter period, for example, the traffic rush hour. Moreover, analysis
can be performed for the existing state and forecast horizons. The spatial
scope depends on the adopted restrictions on the share of high-emission
vehicles in the entire city or its separate areas, for example, areas located
in central parts of the city.
The simulations made with the use of the
macroscopic transport model are the main part of the analysis. The developed
method assumes the implementation of the PTV VISUM software, which includes a special emission
calculation procedure based on HBEFA (The Handbook of
Emission Factors for Road Transport), that is, a publicly available database of
emission factors for road transport, developed at the request of several
European countries (including Germany, Switzerland, Austria, Sweden, Norway,
and France). It contains emission factor values (hot, cold start, evaporation)
for all regulated and important non-regulated air pollutants, as well as fuel
consumption and CO2 emissions for different vehicle types. It
determines both the desired emissions and the optional excess emissions during
a cold start, given different traffic situations, traffic volume, and fleet
composition [32].
The use of the HBEFA
procedure in the VISUM software enables the determination
of the dispersion of harmful substances emissions on sections of the transport
network divided into appropriate categories. During the environmental traffic
impact assessment process, the fuel consumption for the entire network
(expressed in quantity/g) for a specific demand segment is converted into unit
consumption (expressed in l/100 km) separately for diesel and petrol. First,
the amount of fuel is divided by its density, and then it is related to the
mileage for a specific demand segment. To assess fuel consumption for the
entire vehicle fleet, including electric vehicles, the results are also
presented in [MJ]. The result of the analysis carried out with the VISUM software is the assessment of the emission of harmful
substances and fuel consumption for various scenarios.
In the method developed, it was assumed that
the assessment of the impact of the share of electric vehicles in traffic on
the reduction of harmful substances is based on the matrix of traffic
assignment for individual scenarios. The last stage of the analysis consists in
comparing the results obtained for the individual scenarios of the development
of the urban transport system, that is, W1-Wn, with
the results for the reference scenario W0.
The following measures divided into two main
groups were used for the assessment:
·
Group
1—measures related to the total emission of selected harmful substances,
including:
o carbon dioxide (CO2) reported
total [g],
o carbon monoxide (CO) total [g],
o hydrocarbons (HC) total [g],
o nitrogen oxides (NOx) total [g],
o particulate matter (PM) 10 µm total [g].
·
Group
2—measures related to fuel and energy consumption, including:
o fuel consumption diesel total [g],
o fuel consumption gasoline total [g],
o fuel consumption electric total [MJ],
o fuel consumption total [g],
o fuel consumption total [MJ].
The
measure values are obtained for the whole city in the analyzed period (the
morning rush hour).
4. CASE STUDY
4.1. Research
area
The macroscopic
transport model built for the city of Bielsko-Biała
was used to assess the impact of introducing electric vehicles into circulation
for the reduction of exhaust gas emissions. Bielsko-Biała
is located in the southern part of Poland in the Silesian Voivodeship
near the border of the Czech Republic and Slovakia. Its area is approximately
124.5 km2. The location of Bielsko-Biała against the background of the Śląskie Voivodeship is
shown in Figure 2. The city of Bielsko-Biała
is the seat of the Bielsko poviat
authorities, as well as the main city of the Bielsko
agglomeration and central to the Bielsko Industrial
District. It also constitutes a significant centre of the southern sub-region
of the Silesian Voivodeship, which is one of the four
areas of the development policy of the voivodeship.
Fig. 2.
Location of the city of Bielsko-Biała
Bielsko-Biała is
one of the economically well-developed cities in Poland. It serves as the main
administrative, industrial, commercial and service, academic, cultural, and
tourist centre of the Silesian border area. From year to year, the city is also
becoming an increasingly important centre of modern technologies, where dynamic
development is visible, especially in the IT industry. Due to the proximity of
the Upper Silesian conurbation and Kraków, as
well as the Czech Ostrava and Slovak Žilina, Bielsko-Biała is an important centre of cross-border
development.
The dynamic development of the
city is also favoured by its location at the crossroads of international and
national transport corridors, which makes it an important road and rail
junction not only in the Śląskie Voivodeship but also nationwide. Due to the convenient
location of the city in the network of national and provincial roads and the
existing system of these roads, supplemented with county and municipal roads,
the city is characterized by a high level of accessibility from other
municipalities of the province and the country. They ensure the connectivity of
the city area and its accessibility. Geographic and demographic indicators of
road density are, respectively, 4.38 [km/km2]
and 31.41 [km/10,000 residents]. There are 1,237 intersections on the road in
the city, including 51 (that is, 4.1%) intersections with traffic lights. The
average length of the section between the intersections is approximately 479 m.
The population of Bielsko-Biała in December 2020 amounted to 169,756
people and has shown a downward trend since 2010. Variability values in this
respect are shown in Figure 3. The largest share of the population, ranging
from 64 to 56%, is of working age. Since 2010, this share has been decreasing
annually by about 2%. On the other hand, a systematically growing number of
people of post-working age can be observed, which in 2020 amounted to 44,000
and constituted over 26% of the city's population. With the share of people of
pre-working age at a similar level (the change in this regard has not exceeded
1% since 2010), it means that the society of the city of Bielsko-Biała
is ageing. However, since 2017, smaller changes can be noticed in this regard
(below 3%).
a)
b)
Fig. 3.
Demographic indicators in the city of Bielsko-Biała:
a) variability in the number of inhabitants, b) annual dynamics of changes in
the number of inhabitants
The number of vehicles registered
in the city of Bielsko-Biała is constantly
increasing. This applies to both cars and trucks, as well as motorbikes. Figure
4a shows the motorization rate values for each of
these groups of vehicles. Age is also an important aspect related to the composition
of the vehicle fleet in the urban transport system. Figure 4b
shows the structure of passenger cars older than 8 years in the city of Bielsko-Biała in the years 2015-2020.
In 2015, a comprehensive travel survey of the
city and its immediate surroundings was conducted. The results indicated that
the mobility of active transport people, that is, those who made at least one
trip a day, was approximately 2.2 trips/day. However, given the entire
population of the city, it turned out that the mobility of an average
inhabitant of Bielsko-Biała was equal to 1.59
trips/day. The highest average number of trips made by active transport
residents of the city of Bielsko-Biała was
related to optional purposes, such as social meetings, sports, recreation, or
official matters. A significant number of trips were also made for work-related
purposes. Trips related to school (education) were less important. The data
collected were used to build a macroscopic transport model, which is a
simulation tool that supports scenario analysis.
a)
b)
Fig. 4.
Motorization indicators in the city of Bielsko-Biała:
a) variability in the motorization rate, b) variability in the structure of
passenger cars older than 8 years
The proposed method assumes the need to
distinguish between inbound, outbound, and through traffic. The average volume
of car traffic in the morning rush hour is:
·
for
inbound traffic: 14,403 [E/h], that is, 47.79%,
·
for
outbound traffic (the city is the origin of the trip): 5,698 [E/h], that is,
18.91%,
·
for
outbound traffic (the city is the destination of the trip): 8,159 [E/h], that
is, 27.07%,
·
for through
traffic: 1,880 [E/h], that is, 6.24%.
To build scenarios for the development of the
urban transport system, sections were defined for trucks, truck trailers, and
articulated trucks in through traffic.
4.2.
Assumptions concerning the scenarios
The
analysis covers two scenarios for introducing restrictions on the accessibility
of the city for high-emission vehicles:
·
Scenario W1: The entry of high-emission vehicles from outside the
city is not allowed,
·
Scenario W2: The entry of all high-emission vehicles is not allowed.
In both
scenarios, through traffic is allowed.
To assess
the impact of the share of electric vehicles in traffic on the reduction of
harmful emissions in the city, we built a transport model using the PTV VISUM software with an
appropriate level of detail due to the type structure of vehicles for the
selected area of analysis. This made it possible to conduct scenario analyses
considering at least:
•
types of
traffic, that is, inbound, outbound, and through traffic;
•
types
of fleet composition (as specified in Table 3).
The share
of particular types of vehicles in each of the types adopted in the fleet
composition is presented in Table 3. It shows only the types of vehicles that
constitute a significant share in the composition of the fleet (more than 5%).
Considering the
composition of the fleet, we define the vehicle structures in the transport
model for both the reference scenario W0 and the
scenarios W1 and W2, which
are presented in Table 4.
Tab. 3
Emission
concepts in the fleet composition under study
|
Emission concept
and its share in the fleet composition |
|||
|
U1 |
PC petrol Euro-2
(7.83%); |
PC petrol Euro-4
(16.21%); |
PC diesel Euro-2 (10.07%); |
|
PC diesel Euro-3
(17.99%); |
PC diesel Euro-4
(13.73%); |
PC diesel Euro-4 (DPF) (12.42%); |
|
|
U2 |
LCV diesel M+N1-I Euro-3 (6.23%); |
LCV diesel M+N1-I Euro-4 (8.93%); |
LCV diesel N1-II Euro-3 (8.42%); |
|
LCV diesel N1-II Euro-4 (12.08%); |
LCV diesel N1-III Euro-2 (5.43%); |
LCV diesel N1-III Euro-3 (17.01%); |
|
|
LCV diesel N1-III Euro-4 (24.40%); |
|
|
|
|
U3 |
Rigid Truck 7,
5-12 t diesel Euro-III (7.92%); |
Rigid Truck
>14-20 t diesel Euro-III (7.57%); |
Rigid Truck >20-26 t
diesel Euro-III (13.02%); |
|
Rigid Truck
>20-26 t diesel Euro-V (6.37%); |
|
|
|
|
A1 |
PC petrol Euro-2
(7.83%); |
PC petrol Euro-4
(16.21%); |
PC diesel Euro-2 (10.07%); |
|
PC diesel Euro-3
(17.99%); |
PC diesel Euro-4
(13.73%); |
PC diesel Euro-4 (DPF) (12.42%); |
|
|
A2 |
LCV diesel M+N1-I Euro-3 (6.23%); |
LCV diesel M+N1-I Euro-4 (8.93%); |
LCV diesel N1-II Euro-3 (8.42%); |
|
LCV diesel N1-II Euro-4 (12.08%); |
LCV diesel N1-III Euro-2 (5.43%); |
LCV diesel N1-III Euro-3 (17.01%); |
|
|
LCV diesel N1-III Euro-4 (24.40%); |
|
|
|
|
A3 |
Rigid Truck
>20-26 t diesel Euro-III (5.41%); |
TT/AT >34-40 t
diesel Euro-III (12.93%); |
TT/AT >34-40 t diesel Euro-V (20.78%); |
|
M1 |
PC petrol Euro-2
(7.83%); |
PC petrol Euro-4
(16.23%); |
PC diesel Euro-2 (10.07%); |
|
PC diesel Euro-3
(17.99%); |
PC diesel Euro-4
(13.73%); |
PC diesel Euro-4 (DPF) (12.41%); |
|
|
M2 |
LCV diesel M+N1-I Euro-3 (6.23%); |
LCV diesel M+N1-I Euro-4 (8.93%); |
LCV diesel N1-II Euro-3 (8.42%); |
|
LCV diesel N1-II Euro-4 (12.08%); |
LCV diesel N1-III Euro-2 (5.43%); |
LCV diesel N1-III Euro-3 (17.01%); |
|
|
LCV diesel N1-III Euro-4 (24.41%); |
|
|
|
|
M3 |
TT/AT >34-40 t
diesel Euro-III (19.11%); |
TT/AT >34-40 t
diesel Euro-V EGR (5.42%); |
TT/AT >34-40 t diesel
Euro-V SCR (30.73%); |
|
E1 |
PC BEV electric
(100%); |
|
|
|
E2 |
LCV BEV M+N1-I electric (33.33%); |
LCV BEV N1-II electric (33.33%); |
LCV BEV N1-III
electric (33.34%); |
|
E3 |
TT/AT BEV
electric (25.00%); |
Rigid Truck BEV
≤7.5 t electric (25.00%); |
Rigid Truck BEV >7.5-12 t
electric (25.00%); |
|
Rigid Truck BEV
>12 t
electric (25.00%); |
|
|
|
Tab.
4
The
fleet compositions in the transport model adopted for the reference scenario W0 and the scenarios W1 and W2
|
Type of Traffic |
Scenario W0 |
Scenario W1 |
Scenario W2 |
|
Inbound traffic |
U1, U2, U3 |
U1, U2, U3 |
E1, E2, E3 |
|
Outbound traffic |
U1, U2, U3 |
U1, U2, U3 |
E1, E2, E3 |
|
Outbound traffic |
A1, A2, A3 |
E1, E2, E3 |
E1, E2, E3 |
|
Through traffic |
M1, M2, M3 |
M1, M2, M3 |
M1, M2, M3 |
From the designation of the type of fleet
composition (urban, average, motorway, and electric), three types of vehicle
groups were distinguished, that is, passenger car, light commercial vehicle,
and mix (trucks, truck trailers, articulated trucks). For each type, the
emission concept and its share in the fleet composition were presented. In the
group of passenger cars, diesel vehicles that meet the EURO III emission
standard are of the largest share. This does not apply to the vehicles in the
electric fleet composition. In the group of light commercial vehicles, diesel N1-III vehicles with the EURO IV emission standard have the
largest share. In turn, in the group of mixed cars, diesel (> 34-40 tonnes) that meets the Euro-V emission standard has the
highest share.
4.3. Results
of the analyses
The results of the scenario analysis of the
reduction of exhaust emissions by introducing electric vehicles are presented
in Table 5.
The degree of reduction in
harmful substances in each of the scenarios of the development of the urban
transport system about the reference scenario is presented in Figure 5.
Considering the environmental criterion, the W2
scenario is much more favourable.
According to the information presented in Figure 5,
there is a noticeable reduction in harmful substances for the development of
the urban transport system between the two scenarios analysed. For each of the
measures (total CO2 reported [g], total CO [g], HC total [g], NOx
total [g], and PM (10 µm) total [g]), the solutions used in the W2 scenario are more favourable. The highest emission
reductions occurred for CO total [g] and HC total [g] (more than 80%).
Tab.
5
The
values of the measures to assess the impact of the share of
electric vehicles in traffic on the reduction of exhaust emissions for
the reference scenario W0 and the scenarios W1 and W2
|
Measure of
Assessment |
Scenario W0 |
Scenario W1 |
Scenario W2 |
|
Group 1 |
|||
|
CO2 reported
total [g] |
42,343,255.82 |
30,980,307.60 |
12,793,777.41 |
|
CO total [g] |
184,107.10 |
157,676.72 |
28,837.32 |
|
HC total [g] |
29,186.26 |
26,295.47 |
3,917.20 |
|
NOx total [g] |
194,904.37 |
140,482.04 |
54,408.70 |
|
PM (10 µm)
total [g] |
7991.16 |
5968.66 |
2092.81 |
|
Group 2 |
|||
|
Fuel consumption diesel
total [g] |
9,504,016.51 |
6,868,988.59 |
2,628,879.40 |
|
Fuel consumption
gasoline total [g] |
5,076,530.12 |
3,798,898.96 |
1,782,903.60 |
|
Fuel consumption
electric total [MJ] |
21.96 |
51,183.13 |
128,239.83 |
|
Fuel consumption
total [g] |
14,580,702.09 |
10,668,006.20 |
4,411,871.06 |
|
Fuel consumption
total [MJ] |
616,895.76 |
502,408.14 |
314,809.19 |
a)
b)
Fig. 5.
Reduction in harmful substances for the urban transport system development scenarios:
a) for scenario W1, b) for scenario W2
The fuel consumption for the scenarios examined for
the urban transport system regarding the reference scenario is shown in Figure
6. The results of the analysis confirm the positive impact of the changes introduced
in traffic organization for the W2 scenario. Figure 6a shows that the reduction in fuel consumption for diesel
and gasoline vehicles in scenario W2 exceeds 64%. On
the other hand, Figure 6b shows that the solution
presented for the W2 scenario will increase the
consumption of electric fuel by more than 584,000%.
The analyses also include
indicators of total fuel consumption, expressed in [g] and in [MJ], which also
considers electricity consumption. Figure 7 shows the increase in total fuel
consumption for the urban transport system development scenarios W1 and W2 and the reference
scenario W0, expressed in [kg] and [MJ].
a)
b)
Fig. 6. Change
in fuel consumption for the urban transport system development scenarios:
a) reduction for diesel and gasoline, b) increase for electric total
The results presented in Figure 7
also confirm the positive environmental impact of both the solutions used in
scenario W1 and scenario W2.
Fuel consumption values expressed in [kg] apply only to vehicles with diesel
and gasoline engines, while the values expressed in [MJ] include electricity
used in vehicles. Both scenarios are favourable in economic and environmental
terms. Fuel consumption values are more than 26% lower than the reference
scenario for high-emission fuels and by more than 18% for all types of fuels.
It is also worth noting that scenario W2 is more
favourable (regarding scenario W0) than scenario W1 by more than 42% for vehicles powered by high-emission
fuels and more than 30% for all vehicles. This is a reduction of all the
characteristics examined (that is, total fuel consumption in [kg] and [MJ]) by
more than 2.5 times for the W2 scenario compared to
the W1 scenario.
5. DISCUSSION
Improving
the quality of life in cities is one of the greatest challenges facing society
today. An important aspect of this problem is the effort to minimize the
environmental pollution resulting from the development of civilization that
leads to increasing human interference in the ecosystem. Various studies have
been conducted to identify sources of pollution, among which transport plays an
important role. Scientific research conducted to find solutions aimed at
reducing the negative impact of transport on the environment focuses on the
development of electromobility.
This
article presents the results of a research carried out using the example of a
large city in Poland (Bielsko-Biała). The
results are the values of the measures to assess the impact of changes in the
accessibility of the area and the related traffic organization for users of
electric cars and conventionally powered vehicles, presented in three groups.
Analyses were performed for two scenarios that were compared with the scenario
for the existing state. The research results indicate that, regardless of the
scenario considered and the area for which the analyses were performed, the
highest values of assessment measures of Group 1 were for CO2
reported total [g], and the lowest for PM (10 µm) total [g] and HC total
[g]. In turn, the measures of Group 2 were related to the generic structure of
the traffic and should not be compared in this way.
a)
b)
c)
d)
Fig.
7. Reduction in total fuel consumption for the urban transport system
development scenarios: a) and b) expressed in [kg] and [%], c) and d) expressed
in [MJ] and [%]
From
the results obtained for the entire city, it should be noted that for each of
the scenarios analysed, there was a decrease in the values of all evaluation
metrics, except for total electrical fuel consumption [MJ]. This is
particularly evident in Figure 5, which illustrates the reduction in pollutants
for the scenarios studied relative to the reference scenario W0. The characteristic with the least reduction (positive
impact) is HC total [g] for scenario W1. However, the
highest profit was observed using the solution developed for the W2 scenario regarding HC [g] at the level of more than 86%.
Similar conclusions can be drawn for fuel consumption. The reduction in fuel
consumption expressed in [kg] and [%] for the W2
scenario is more than 69%, and for consumption expressed in [MJ], more than 48%
given the existing state.
6. CONCLUSIONS
The
introduction of electric vehicles in cities is one of the priorities of
decision-makers. The method presented in this paper can be applied in cities
and areas of different sizes to parameterize the composition of the desired
municipal transport fleet and traffic organization.
The
accessibility scenarios of the city of Bielsko-Biała
for vehicles with different fleet compositions for inbound, outbound, and
through traffic were analysed. The greatest reduction in pollutant emissions
can be achieved by introducing restrictions for all users of individual
transport in the city.
Two
scenarios (W1, W2) were
developed restricting entry into the city for high-emission vehicles. The
results were compared with the reference scenario W0.
It was observed that for both scenarios W1 and W2, the reduction of harmful substance emissions was
obtained. However, the W2 scenario, in which all
high-emission vehicles were prohibited from entering the city, is more
advantageous regarding both emissions of harmful substances and fuel
consumption.
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Received 02.07.2022; accepted in
revised form 19.09.2022
Scientific Journal of Silesian University of Technology. Series
Transport is licensed under a Creative Commons Attribution 4.0
International License
[1] Faculty of Transport, Warsaw
University of Technology, Koszykowa 75 Street, 00-662
Warsaw, Poland. Email: marianna.jacyna@pw.edu.pl. ORCID: https://orcid.org/0000-0002-7582-4536
[2] Faculty of Transport and Aviation
Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland. Email:
renata.zochowska@polsl.pl. ORCID:
https://orcid.org/0000-0002-8087-3113
[3] Faculty of Transport and Aviation
Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland. Email:
aleksander.sobota@polsl.pl. ORCID: https://orcid.org/0000-0002-8171-7219
[4] Faculty of Transport, Warsaw
University of Technology, Koszykowa 75 Street, 00-662
Warsaw, Poland. Email: mariusz.wasiak@pw.edu.pl.
ORCID: https://orcid.org/0000-0002-6173-7001