Assessment of Vilnius city development scenarios based on transport system modelling and multicriteria analysis/Vilniaus miesto pletros scenariju vertinimas, naudojant susisiekimo sistemos modeliavima ir daugiakritere analize.
Jakimavicius, Marius ; Burinskiene, Marija
1. Introduction
The motivation for this research arose from an effort to assess
transportation system performance in the Vilnius city. Most of cities in
Europe struggle with the problems of urban sprawl and traffic
congestion, yet mostly with little success. It is increasingly becoming
clear that market forces will continue to lead to ever more dispersed,
energy-wasteful urban settlement patterns. Land-use policies like the
promotion of higher-density, mixed-use urban forms more suitable for
public transport become necessary. But only a combination of land-use
policies and transport policies promoting public transport and
containing the private automobile can limit further urban dispersion and
free metropolitan areas from their increasing auto-dependency. It is
therefore necessary to develop modelling approaches in which the two-way
interaction between transport and land use is modelled (Alvanides et al.
2001; Drobne 2003; Black et al. 2002).
Sharp bounce in motorization level invokes a lot of transportation
problems. Many researches analyze urban areas development from the point
of transportation system sustainability, which influences economical,
social and environmental implications (Camagni et al. 2002; Grigonis and
Burinskiene 2002; Burinskiene and Paliulis 2003). Other scientists also
indicate political and institutional aspects (Ciegis and Gineitiene
2008).
Chosen urban areas development scenario invokes reorganization of
the transportation system. Urban areas development should not precede
without either adequate existing public transport provision or new
public transport provided in tandem with the development (Anderson 1999;
Siewczynski 2004).
The efficiency of urban transportation modelling is getting more
and more important because of the increasing rate of mobility demand. To
plan, control and organize urban transportation in the most efficient
way, we also need to consider the aspects of land use (Tanczos and Torok
2007).
If sustainability is defined only in terms of energy consumption
and air pollution emissions, the best solution may be more efficient and
alternative fuel vehicles. But these strategies do not help achieve
other planning objectives such as congestion reduction, facility cost
savings, increased safety, improved mobility for nondrivers, or more
efficient land use development; in fact, by reducing vehicle operating
costs, it tends to increase these problems (Litman 2004). When all
impacts are considered, strategies that improve travel options,
encourage reduced driving, and create more accessible land use patterns
are generally more sustainable overall.
Also multicriteria methods could be used for urban areas
development scenarios evaluation. There is a wide range of methods based
on multicriteria utility theory: SAW--Simple Additive Weighting
(Ginevicius et al. 2008a; Sivilevicius et al. 2008); TOPSIS--Technique
for Order Preference by Similarity to Ideal Solution (Zavadskas et al.
2006; Jakimavicius and Burinskiene 2007); COPRAS--Complex Proportional
Assessment (Zavadskas et al. 2007); COPRAS-G--Complex Proportional
Assessment of alternatives with Grey relations (Zavadskas et al. 2008).
When wide range indicators of urban transport system are known, it is
possible to use multicriteria methods for correct urban areas
development scenario estimation (Ruichun 2007). Other researcher's
analyse the idea that the disadvantages of some particular multicriteria
evaluation methods could be compensated by the advantages of others. The
integration of methods will be correct if there is a correlation between
the values obtained by different methods (Ginevi?ius et al. 2008b). A
more thorough analysis reveals that the above methods do not take into
consideration the effect of the components of a particular evaluation
method on the result obtained. This can be achieved only if
multicriteria evaluation is based on graphical-analytical approach
(Ginevicius and Podvezko 2008). Other researcher's investigating
the application of game theory principles to civil engineering
technology and management problems (Zavadskas and Peldschus 2009).
2. Methodology of Vilnius city development scenarios modelling
A common way to produce a transport forecast is to divide the
calculations in the following modelling steps: Trip Generation, Trip
Distribution, Mode Split and Network Assignments.
[FIGURE 1 OMITTED]
In a Trip Generation step, the number of car and truck trips that
start in each zone and the number of trips that end in each zone are
calculated. In the Trip Distribution step, the geographical trip pattern
is calculated, which is determined by the number of car and truck trips
between each pair of the zones. In Auto Assignment step, the car and
truck trips between different zone pairs are simultaneously allocated to
the network and travel times by car between the zones that are
calculated. Trips by public transport are allocated to the public
transport network and travel times by public transport between zones are
calculated. In the last step, fuel consumption is calculated for the
whole network (Fig. 1).
In the Auto Assignment Step, the car and truck trips between zones
are allocated (assigned) to the road network according to the
equilibrium traffic assignment. Also are assigned public transport trips
to a public transport network segments. The behaviour assumption of the
traffic assignment is that each driver tries to choose the route that
takes him/her to the destination as fast as possible. The route travel
time is calculated as the sum of link's travel times along the
route. The link travel times are calculated by using increasing volume
delay functions where the travel time along a route increases with the
number of users. The consequence is that, between each
origin/destination pair, only the routes that have minimal travel time
are used. In the Traffic Analyst Model, the distribution of trips by
routes is performed as a multiclass traffic assignment with generalized
travel cost that is a modification of the traffic assignment based on
plain travel times.
3. Description of Vilnius city development scenarios modelling
Forecasting of changes in land-use across the city is quite
complicated as many factors are involved: policy packages, private
initiatives, infrastructure development and changes in global economics.
Consequently, it was decided to operate with developments that are
targeted and hypothesized in the Vilnius Comprehensive Plan
(Comprehensive plan of Vilnius city 2007).
Hence, other forecast factors were development within Vilnius
transport infrastructure according to the Comprehensive Plan and its
influence on traffic flows. Also transport system scenarios have been
modelled according to infrastructure of the street network (Fig. 2).
Vilnius city transport system development scenarios have been
modelled so that these main new projects of Vilnius city transport
infrastructure would be developed till 2015:
--Equipment of the South Vilnius city bypass.
--Equipment of the West Vilnius city bypass.
--New segment of G. Vilko st. from Mokyklos street to A14 road.
--New two-level crossing at Zalgirio st. and Gelezinio Vilko st.
--New two-level crossing at Ukmerges st.--Ateities st.--Laisves st.
--Equipment of Siaurine st. from new West bypass to Zirmunu st.
--Equipment of Pilaites st. follow-up.
--New connection from Ozo st. at Buivydiskiu st. to Laisves st.
--New two level crossings at Kernaves st.--Ozo st. intersection.
--New connection through Bajorai village from Mokslininku. st.
--New connection from Kernaves st. to Tumo-Vaizganto st. through a
new bridge.
[FIGURE 2 OMITTED]
It is possible to modify car ownership by changing the number of
cars per person. The forecasted car ownership in 2015 is 570 cars per
1,000 inhabitants and ownership in 2025 is 590 cars per 1,000
inhabitants. The total number of car trips in the region is calculated
as a function of changes in the total population and the car ownership
as compared to the base year situation. The consequence is that if the
population and car ownership is unchanged as compared to the base year
situation, but the total number of workplaces increased, then the total
number of car trips will be the same as for the base year situation. If
the workplaces are relocated, for example, to more central areas, it
will have an effect on the trip's pattern for cars, but not on the
total number of trips.
The trip frequencies in the Trip Generation model are estimated and
based on the travel behaviour in 2007. Hence, it is the level of car use
for the base year situation that is included in the model. If there is a
reason to believe that the cars will be used to a higher extent in the
future, then the way to implement that in the Model is to increase the
car ownership slightly in addition to the increase of the number of
registered cars per capita.
Construction of scenarios should reflect expected and desired
aspects of developments. There are many factors that could be changed in
the model, consequently the number of scenarios will strongly and
unreasonably increase. Scenarios were chosen for the comparison, they
are in Table 1. Factors used in the scenarios are explained later.
Street Network 2007--means the current street network and other
infrastructure, i.e. length of streets, number of lanes, modes allowed,
and volume-delay function index.
Street Network 2015--means the development of bypasses and
two-level crossings according to the Comprehensive Plan of Vilnius (see
previous chapter).
Social Data 2007--current situation, i.e. total number of
inhabitants is 554,000, the rough number of workplaces is 310,000.
Social Data 2015--number of inhabitants that increased to 576,000,
the rough number of workplaces will be 409,000. The ratio between
workplace and number of residents are more balanced, as were population
and employment moves to suburban areas, especially in the Northern
direction. The number of inhabitants decreased in the central part of
the city, and most of the residents will have to travel towards the
centre.
Social Data 2025--number of inhabitants that increased to 600,000
and the rough number of workplaces will be 426,000.
Car ownership in 2007--car ownership initially used in model. Such
a rate was used in the model's calibration, and therefore it does
not fully correspond to the real figures (official statistics could be
questioned).
Car ownership 2015--due to economical growth car ownership will
increase rapidly to 570 cars per 1,000 inhabitants and car ownership in
2025 will be 590 cars per 1,000 inhabitants.
4. Discussion results of Vilnius city transport system modelling
scenarios
All scenarios were evaluated by combining different factors and
planning horizons according to Vilnius city urban areas development
strategies. The model produced rational results for a peak-hour and is
presented in Table 2. In order to see an effect of new transport system
infrastructure development according to Vilnius city Comprehensive Plan,
it is necessary to perform a comparison of developments with base
scenario and future scenarios with "the worst future" scenario
is essential. The "worst future" scenario means that there are
no changes in the infrastructure, but car ownership increased and land
use pattern changes and so will influence more trips from suburbs to the
city centre (in 2015 and 2025).
Travel time by car of "the worst future" scenario is
presented in Fig. 3. This figure presents travel time by car in morning
peak-hours of the 3 Vilnius city development scenarios in 2015 and 2025,
when new transport system infrastructure (two-level crossings and new
bypasses) would not be developed.
The next charts shows an average modelled driven distance for one
Vilnius inhabitant and an average fuel consumption in litres for one
driven kilometre in 2015 and 2025 years according different Vilnius city
transport system development scenario (Figs 4, 5).
Fuel consumption and average driven distance have been taken into
account that new projects of Vilnius city transport infrastructure
according Vilnius Comprehensive Plan would be developed till 2015 year.
Driving pattern is a complex phenomenon, which is influenced by
several variables as the drivers' behaviour, the street
environment, the traffic flow and the car type, and the driving patterns
may vary strongly. A large number of the measures must be employed in
order to capture all these sources of variation (Loukopoulos et al.
2004). The aim was to prepare model that would describe a way how to
choose the correct city development scenario according to urban
transport system conditions.
Modelling results clearly show that all infrastructure developments
and changes in land use influence the driven distance and fuel
consumption.
Generally, the exact extent of cause and effect between urban areas
development scenarios and transport system indicators in transport is
not conclusive. Often there is a number of local factors involved,
relating to particular people behaviour and the involved localities. A
combination of complementary land use planning measures and
infrastructure development can provide an integrated package, where each
element reinforces the other towards a more sustainable development.
The current situation (social data 2007, i.e. total number of
inhabitants is 554,000 and the rough number of workplaces is 310,000 and
the street network data for 2007 situation) initially shows that an
average driven distance for one Vilnius inhabitant is 1.56 km. The
"worst future" scenario for 2015 and 2025 year, when street
network infrastructure would not be developed according Vilnius
Comprehensive Plan, indicates a huge increase of time spend in traffic
jams. Modelling results show that an average time for one Vilnius
inhabitant spends in traffic jams without any movement in 2015 would be
about 5 min and in 2025--7 min for Vilnius city concentrated development
scenario. If would be taken into account the prognosis of Vilnius city
automobilization level, the average time spend in traffic jams for
Vilnius inhabitants, having automobiles, would be 7 and 9 min in 2015
and 2025 years.
In order to calculate the effectiveness of fuel consumption in
morning peak hours for each Vilnius city development scenario, according
to streets infrastructure projects which are in Comprehensive Plan, it
is possible to compare modelling scenarios with the urban areas
development scenarios with 2007 year street network (Figs 6, 7).
The biggest difference in fuel consumption in morning peak hours
according to new transport network infrastructure has an extensive
development scenario. Difference in fuel consumption is 11,686 litres
and 15,733 litres according modelling scenarios for 2015 and 2025 years.
Other Vilnius city development scenarios are not so sensitive for
the need of new infrastructure development and the results differs by
1%. Decentralized concentrated development scenario for 2015 year and
without new street network infrastructure has 135,766 litres and 163,762
litres for 2025 year and respectively concentrated development has
123,990 litres for 2015 year and 155,088 litres for 2025. Development of
Vilnius city new street network infrastructure, according to Vilnius
city Comprehensive Plan till 2015, gives 6,374 litres economy of fuel
consumption according to concentrated development and 9,618 litres
according to decentralized concentrated development.
5. Vilnius city development scenarios ranking
Another goal of this paper is to perform urban areas development
scenarios ranking based on SAW multicriteria method. In order to perform
a correct analysis, the urban development scenarios ranking should be
taken into account indicators system which represents social economical
and environmental group sets of indicators (Jakimavicius and Burinskiene
2009).
The best variant of Vilnius city development scenario according to
travel time is the scenario of decentralized concentrated development.
The modelling results of total travel time by car with evaluated new
street network infrastructure are 43,421 h in morning peak hours in 2015
year and 45,125 h in 2025.
Calculation of indicators weights for Vilnius city development
scenarios ranking have been performed using ranking method, the input
data have been collected by performing 28 experts questionnaire.
Experts from Vilnius municipality and from Vilnius municipality
company "Susisiekimo Paslaugos" have filled questionnaires for
evaluating criteria importance. The results of ranking method are
presented in Table 3.
Calculations in order to find rational variant of Vilnius city
transport system development have been performed by computer program
WinDetermination. Variants priority row by SAW method: Decentralized
concentrated development > Concentrated development > Extensive
development = 0.882 > 0.875 > 0.871.
6. Conclusions
1. The problems of correct selection of urban areas transport
system development could be solved by using decision-support system
methods and created indicators group of urban transport system. Created
indicators system could be used for evaluating urban areas development
scenarios according to the sustainability of transport system.
2. Urban transport system analysis model was developed for Vilnius
conditions, and estimates the fuel consumption, average travel distance
and driven time by car in morning peak hours depending on urban areas
development scenario and socio-economic data. This model should be used
when calculating new projects of the transport infrastructure
(by-passes, new bridges) and when evaluating the economic efficiency of
traffic organization projects.
3. The application of model solved several practical problems.
Analysis of 3 different Vilnius city development scenarios would
determine that a decentralized concentrated development scenario has the
lowest fuel consumption per one passenger per kilometre, but it would
also lead to longer (but faster) trips and consequently higher total
fuel consumption than concentrated development scenario. Meanwhile,
reconstruction of current critical intersections will reduce fuel
consumption and reduce pollution in highly populated areas. A more
concentrated and mixed land use is definitely an advantage to lowering
total fuel consumption, but it is not advantage for sustainable
transport system. Concentrated land use increases travel time and time
spend in traffic jams.
4. For evaluating urban areas land use scenarios according
transport system sustainability it could be successfully used the
integrated multicriteria decision support system methods with GIS
software. Also, for urban areas transport system detailed analysis in
order to calculate traffic indicators traffic modelling software could
also be applied.
5. Hence, various developments have strengths and weaknesses.
Reducing dependency of fuel consumption in urban areas, it is necessary
to promote concentrated development in urban areas; however, a
concentrated development has enormous positive spin-offs in the overall
transportation sustainability and liveability of the Vilnius.
DOI: 10.3846/1392-3730.2009.15.361-368
Received 23 Feb 2009; accepted 25 May 2009
References
Alvanides, S.; Openshaw, S. and Macgill, J. 2001. Zone design as a
spatial analysis tool, in Modelling Scale in Geographical Information
Science, London, 141-157.
Anderson, M. A. 1999. Evaluation of models to forecast
external-external trip percentages, Journal of Urban Planning and
Development 125(3): 110-120. doi:10.1061/(ASCE)0733-9488(1999)125:3(110)
Black, J. A.; Paez, A.; Suthanaya, P. A. 2002. Sustainable urban
transportation: performance indicators and some analytical approaches,
Journal of Urban Planning and Development 128(4): 184-209.
doi:10.1061/(ASCE)0733-9488(2002)128:4(184)
Burinskiene, M.; Paliulis, G. 2003. Consistents of cars parking in
Lithuanian towns, Transport 18(4): 174-181.
Camagni, R.; Gibelli, C.; Rigamonti, P. 2002. Urban mobility and
urban form: the social and environmental costs of different patterns of
urban expansion, Ecological Economics 40(2): 199-216. doi:10.1016/S0921
-8009(01 )00254-3
Ciegis, R.; Gineitiene, D. 2008. Participatory aspects of strategic
sustainable development planning in local communities: Experience of
Lithuania, Technological and Economic Development of Economy 14(2):
107-117. doi:10.3846/1392-8619.2008.14.107-117
Drobne, S. 2003. Modelling accessibility fields in Slovene
municipalities, in Proceedings of the 7th Symposium on Operation
Research in Slovenia (SOR'03), 89-96.
Ginevicius, R.; Podvezko, V. 2008. Multicriteria graphical
analytical evaluation of the financial state of construction
enterprises, Technological and Economic Development of Economy 14(4):
452-461. doi:10.3846/1392-8619.2008.14.452-461
Ginevicius, R.; Podvezko, V.; Bruzge, S. 2008a. Evaluating the
effect of state aid to business by multicriteria methods, Journal of
Business Economics and Management 9(3): 167-180.
doi:10.3846/1611-1699.2008.9.167-180
Ginevicius, R.; Podvezko, V.; Raslanas, S. 2008b. Evaluating the
alternative solutions of wall insulation by multicriteria methods,
Journal of Civil Engineering and Management 14(4): 217-226.
doi:10.3846/1392-3730.2008.14.20
Grigonis, V.; Burinskiene, M. 2002. Information technologies in
energy planning of cities and towns, Journal of Civil Engineering and
Management 8(3): 197-205.
Jakimavicius, M.; Burinskiene, M. 2007. Automobile transport
systems analysis and ranking in Lithuanian administrative regions,
Transport 22(3): 214-220.
Jakimavicius, M.; Burinskiene, M. 2009. A GIS and multi
criteria-based analysis and ranking of transportation zones of Vilnius
city, Technological and Economic Development of Economy 15(1): 39-48.
doi:10.3846/1392-8619.2009.15.39-48
Litman, T. 2004. Socially Optimal Transport Prices and Markets iOn
line] VTPI. Available from Internet: <http://www.vtpi.org/tca/>
[accessed 6 Feb 2009].
Loukapoulos, P., et. al. 2004. Car-user responses to travel demand
management measures: goal setting and choice of adaptation alternatives,
Transportation Research 9(4): 263-280.
Ruichun, H.; Yinzhen, L.; Junyi, Z.; Fujiwara, A. 2007. Improved
urban inhabitant travel demand model and its application, Journal of
Transportation Systems Engineering and Information Technology 7(6):
80-84. doi:10.1016/S1570-6672(08)60005-7
Siewczynski, B. 2004. Computer visualisation in urban planning of
highway surroundings, Journal of Civil Engineering and Management 10(1):
61-65.
Sivilevicius, H.; Zavadskas, E. K.; Turskis, Z 2008. Quality
attributes and complex assessment methodology of the asphalt mixing
plant, Baltic Journal of Road and Bridge Engineering 3(3): 161-166.
doi:10.3846/1822-427X.2008.3.161-166
Tanczos, K.; Torok, A. 2007. Linear optimization model of urban
area's operating efficiency, Transport 22(4): 225228.
Turskis, Z.; Zavadskas, E. K.; Peldschus, F. 2009. Multi-criteria
optimization system for decision-making in construction design and
management, Inzinerine Ekonomika--Engineering Economics 1(61): 7-17.
Zavadskas, E. K.; Kaklauskas, A.; Peldschus, F.; Turskis, Z. 2007.
Multi-attribute assessment of road design solutions by using the COPRAS
method, The Baltic Journal of Road and Bridge Engineering 2(4): 195-203.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J.
2008. Selection of the effective dwelling house walls by applying
attributes values determined at intervals, Journal of Civil Engineering
and Management 14(2): 85-93. doi:10.3846/1392-3730.2008.14.3
Zavadskas, E. K.; Zakarevicius, A.; Antucheviciene, J. 2006.
Evaluation of ranking accuracy in multicriteria decisions, Informatica
17(4): 601-618.
Marius Jakimavicius (1), Marija Burinskiene (2)
Department of Urban Engineering, Vilnius Gediminas Technical
University, Sauletekio al.11, LT-10223 Vilnius, Lithuania E-mail: (1)
mjakimavicius@hnit-baltic.lt; (2) marbur@vgtu.lt
Marius JAKIMAVICIUS. Doctor of Civil Engineering, Assistant in
Department of Urban Engineering Member of Association of Lithuanian
Surveyors. Research interests include GIS and GPS systems, GIS based
solutions for transport analysis tasks, intelligent transport systems,
urban transport system sustainable development, GIS and multi-criteria
based decision support systems for evaluating urban development.
Marija BURINSKIENE. Professor, Dr, Head of Urban Engineering
Department and Director of Territorial Planning Research Institute of
Vilnius Gediminas Technical University. She was project manager for more
than 40 national projects from 1983, participated in more than 30 intern
conferences and was involved in 8 Framework 5 and 6 program projects.
The main area of research interest is regularities and specificity of
urban and regional sustainable development, development of urban
transport system, as well as creation of decision-support system for
implementing engineering solutions.
Table 1. Chosen scenarios for Vilnius transport system modelling
Present situation C1 C2 C3 C4 D1 D2 D3
Street Network 2007 2007 2015 2007 2015 2007 2015 2007
Social dat 2007 2015 2015 2025 2025 2015 2015 2025
Car ownership 2007 2015 2015 2025 2025 2015 2015 2025
Present situation D4 E1 E2 E3 E4
Street Network 2007 2015 2007 2015 2007 2015
Social dat 2007 2025 2015 2015 2025 2025
Car ownership 2007 2025 2015 2015 2025 2025
C--Concentrated development
D--Decentralized concentrated development
E--Extensive development
Table 2. Results of Vilnius city development scenarios calculations
Concentrated development
2007 C1 C2 C3 C4
Length of street 1,710 1,710 1,790 1,710 1,790
network, km
Driving time, h 18,250 65,000 45,320 67,215 48,125
Driving 9,925 38,000 24,135 43,512 32,180
downtime, h
Driven 850,230 1151,250 1100,251 1371,250 1280,251
distance, km
Fuel 0.0854 0.1077 0.1069 0.1131 0.1119
consumption
1 aut-km, litr.
Decentralized concentrated
development
2007 D1 D2 D3 D4
Length of street 1,710 1,710 1,790 1,710 1,790
network, km
Driving time, h 18,250 58,000 43,421 62,215 45,125
Driving 9,925 25,451 18,214 29,451 21,451
downtime, h
Driven 850,230 1291,780 1210,631 1459,560 1368,911
distance, km
Fuel 0.0854 0.1051 0.1042 0.1122 0.1109
consumption
1 aut-km, litr.
Extensive development
2007 E1 E2 E3 E4
Length of street 1,710 1,710 1,790 1,710 1,790
network, km
Driving time, h 18,250 59,320 48,901 64,215 47,225
Driving 9,925 23,451 17,254 27,860 20,655
downtime, h
Driven 850,230 1367,780 1270,631 1589,840 1468,700
distance, km
Fuel 0.0854 0.1059 0.1048 0.1129 0.1115
consumption
1 aut-km, litr.
Table 3. Decision support system matrix with criteria importance of
Vilnius development scenarios evaluation
No Criterion name Units Impor- * Extensive
tance development
Quantitative criteria development
R1 Budget for urban area trans- mln. 0.078 - 4,802
port system development Lt
R2 Necessary land use for ha 0.061 - 902
building new streets
R3 Fuel consumption for one 1 aut. 0.071 - 0.1115
automobile kilometer km /l
R4 Total driven distance per km 0.086 - 1468,700
morning peak hours
R5 Total trip by car downtime h 0.086 - 20,655
per morning peak hours
R6 Total driving time per h 0.089 - 47,225
morning peak hours
Qualitative criteria
R7 Possibilities of internal score 0.052 + 3
trip realization
R8 Possibilities of trip reali- score 0.061 + 3
zation out of city area
R9 Possibilities of trip reali- score 0.072 + 2
zation public transport
R10 Possibilities of transport score 0.069 + 2
mobility reduction, in-
fluence on traffic flows
speed, environmental impact
R11 Complicity of urban trans- score 0.031 - 4
port system network deve-
lopment
R12 Loaded traffic flows in score 0.044 - 3
central part of the city
R13 Increase of citizens score 0.051 - 4
mobility
R14 Noise and air pollution in score 0.064 - 3
central part of the city
R15 Motivation of city bypass score 0.056 + 4
need through: Rudamina, N.
Vilnia, Balsiai, Riese,
Grigiskes and Lentvaris
R16 Motivation of rail trans- score 0.029 + 2
port usability
Development scenario
No Criterion name Concentrated Decentralized
development concentrated
Quantitative criteria development
R1 Budget for urban area trans- 4,432 4,535
port system development
R2 Necessary land use for 115 300
building new streets
R3 Fuel consumption for one 0.1119 0.1109
automobile kilometer
R4 Total driven distance per 1280,251 1368,911
morning peak hours
R5 Total trip by car downtime 32,180 21,451
per morning peak hours
R6 Total driving time per 48,125 45,125
morning peak hours
Qualitative criteria
R7 Possibilities of internal 4 4
trip realization
R8 Possibilities of trip reali- 4 3
zation out of city area
R9 Possibilities of trip reali- 4 3
zation public transport
R10 Possibilities of transport 3 3
mobility reduction, in-
fluence on traffic flows
speed, environmental impact
R11 Complicity of urban trans- 2 3
port system network deve-
lopment
R12 Loaded traffic flows in 4 2
central part of the city
R13 Increase of citizens 3 4
mobility
R14 Noise and air pollution in 4 3
central part of the city
R15 Motivation of city bypass 2 3
need through: Rudamina, N.
Vilnia, Balsiai, Riese,
Grigiskes and Lentvaris
R16 Motivation of rail trans- 1 4
port usability
Fig. 3. Driven time by car according to different Vilnius city
development scenarios for 2015 and 2025 years
Concentrated development 2015 65,000
Concentrated development 2025 67,215
Decentralized concentrated development 2015 58,000
Decentralized concentrated development 2025 62,215
Extensive development 2015 59,320
Extensive development 2015 64,215
Note: Table made from bar graph.
Fig. 4. Average driven distance according to different
Vilnius city development scenarios for 2015 and 2025 years
2015 year 2025 year
Concentrated development 1.91 2.134
Decentralized concentrated development 2.102 2.282
Extensive development 2.206 2.448
Note: Table made from line graph.
Fig. 5. Fuel consumption according to development
scenarios for 2015 and 2025 years of Vilnius city
2015 years 2025 years
Concentrated development 0.1069 0.1119
Decentralized concentrated development 0.1042 0.1109
Extensive development 0.1048 0.1115
Note: Table made from line graph.
Fig. 6, Fuel consumption according to Vilnius development
scenario for 2015 year and street network infrastructure
Modeling for 2015 Modeling for 2015
year without new year with new
street network streets network
infrastructure infrastructure
Concentrated development 117,616 123,990
Decentralized concentrated 126,148 135,766
development
Extensive development 133,162 144,848
Note: Table made from line graph.
Fig. 7. Fuel consumption according to Vilnius development
scenario for 2025 year and street network infrastructure
Modeling for 2025 Modeling for 2025
year without new year with new
street network streets network
infrastructure infrastructure
Concentrated development 143,260 155,088
Decentralized concentrated 151,812 163,762
development
Extensive development 163,760 179,493
Note: Table made from line graph.
