Comparative analysis of housing estates road networks in Hungary

Abstract

One of the most important challenges in urban design is planning an appropriate street network, satisfying the demand of users with different transport modes. Understanding the nature of road networks has been thoroughly studied problem for many years and extensive professional literature is now available in this respect. In this study, five different micro-districts and their road networks have been analyzed in Budapest. Therefore, this paper is aimed at providing a contribution to the knowledge of comparing road networks of different residential estates under different traffic loads. Moreover, significant similarities and differences were identified among urban street layouts in this paper.

Full text

POLLACK PERIODICA
An International Journal for Engineering and Information Sciences
DOI: 10.1556/606.2018.13.1.8
Vol. 13, No. 1, pp. 85–98 (2018)
www.akademiai.com
HU ISSN 1788–1994 © 2018 Akadémiai Kiadó, Budapest
COMPARATIVE ANALYSIS OF HOUSING ESTATES
ROAD NETWORKS IN HUNGARY
1
Andor HÁZNAGY,
2
István FI
Department of Highway and Railway Engineering, Budapest University of Technology
and Economics, MĦegyetem rkp. 3, H-1111 Budapest, Hungary,
e-mail:
1
haznagy.andor@epito.bme.hu,
2
fi@uvt.bme.hu
Received 1 January 2017; accepted 28 January 2018
Abstract: One of the most important challenges in urban design is planning an appropriate
street network, satisfying the demand of users with different transport modes. Understanding the
nature of road networks has been thoroughly studied problem for many years and extensive
professional literature is now available in this respect. In this study, five different micro-districts
and their road networks have been analyzed in Budapest. Therefore, this paper is aimed at
providing a contribution to the knowledge of comparing road networks of different residential
estates under different traffic loads. Moreover, significant similarities and differences were
identified among urban street layouts in this paper.
Keywords: Urban street network layout, Traffic modeling, Urban district, Network analysis
1. Introduction
In the modern age, street networks of settlements have a relationship with human
activities, society and evolution of urban subsets. The urban morphology affects
economic functions, since it provides a framework for interactions, and shapes the
movement of populations and land uses.
One of the most important challenges in urban design is planning an appropriate
street network, satisfying the demand of users with different transport modes [1].
Understanding the nature of road networks has been a thoroughly studied topic for
many years and extensive professional literature is now available in this respect [2], [3],
[4]. Important results have been already reached by researchers, analyzing the road

86 A. HÁZNAGY, I. FI
Pollack Periodica 13, 2018, 1
networks of entire cities, or regions. However, the macroscopic traffic simulations in
districts, sub- or micro-districts of settlements are not studied satisfactorily [5].
In the 1970’s and the 80’s lots of housing estates were built not only in Hungary but
on every corner of Europe to handle housing shortage. Nevertheless, they are many
aspects for planning housing estates in a residential area have appeared with various
shapes of the road network and different parking facilities. Therefore, this paper is
aimed at providing a contribution to the relevant knowledge by comparing different
road networks in residential areas and identifying significant similarities and differences
of these patterns [6]. Moreover, examinations of traffic performance were part of
examination process with variable traffic conditions. In the study, five different micro-
districts and their road networks have been analyzed in Budapest, the capital city of
Hungary. Criteria for selecting micro-districts were the following:
i) Districts were built between 1970 and 1990;
ii) Boundary roads are situated around the micro-districts under scrutiny;
iii) Districts have different layouts. The last one was important to obtain some
similarities in different networks.
During the field survey, the efficiency of traffic calming systems, parking facilities
and the dimensions of cross-sections were measured. Living street areas and 30 km/h
speed limit zones have been used in analyzed micro-districts as traffic calming methods
[7], [8]. These data were used at microscopic models. Next to morphological
investigation, including the statistical data analysis of streets and intersections, the road
networks were also analyzed on the basis of microscopic traffic modeling. The
differences between variants are described by the following parameters: turning
impedances, applied traffic volumes and the capacity of internal links.
2. Methodology
2.1. Model setup
Five different micro-districts and their road networks have been analyzed in
Budapest. The following five sub-urban districts were selected: Gazdagrét (GR);
KaszásdĦlĘ (KD); Pók utca (PK); Békásmegyer I (BMi); Békásmegyer II (BMii). Their
locations are shown in Fig. 1. Their main statistical data i.e. size, population, area and
density are shown in Table I [9].
Appropriate data collection and monitoring were necessary parts of conditional
assessment. The main steps of analyzing methods applied during the examination are
described below. First, data related to urban street networks were collected from
OpenStreetMap.org (OSM) and they have been recorded in the Geographical
Information System (GIS) database. ArcGIS software was used for data storage. This
process ensured that relevant features of urban street networks, i.e. street layout,
hierarchy of roads and location of one-way streets were duly recorded. Monitoring of
urban street layouts was the second step. In this stage, the appropriateness of urban
street network data from OSM was controlled by field surveys.

COMPARATIVE ANALYSIS OF HOUSING ESTATES ROAD NETWORKS 87
Pollack Periodica 13, 2018, 1
Fig. 1. Urban street network of selected sub-districts in Budapest

88 A. HÁZNAGY, I. FI
Pollack Periodica 13, 2018, 1
Table I
Main data of selected micro-districts
Sub-districts
Area
[km
2
]
Resident population
[inhab]
Number of dwellings
[pc]
Population density
[inhab/km
2
]
Housing density
[pc/km
2
]
Number of
dwellings/resident
population
[pc/inhab]
Building period
[years]
BMi 0.898 21020 9403 23416.5 10475.0 2.235 1970-89
BMii 0.589 12451 5573 21127.7 9456.7 2.234 1970-89
GR 0.426 9894 4968 23225.4 11662.0 1.992 1981-90
KD 0.333 7548 3319 22666.7 9967.0 2.274 1981-84
PK 0.704 8978 4532 12752.8 6437.5 1.981 1970-89
During the field survey the efficiency of traffic calming, the amount of parking
facilities and the dimensions of cross-sections were measured. GIS database was
corrected by these measurements.
In everyday transportation, one-way traffic has an influence on traffic flow. It
determines usable routes of private and public transportation modes. In this paper, one
(in the case of one-way street) or two (in the case of two-way street) street segments
were considered between adjacent intersections. This methodology was used instead of
one street segment between every neighboring intersection. Therefore, street segments
have been given a direction.
Number of pieces and types of legal parking spaces were counted with survey and
satellite images (GoogleEarth). In most of the cases, the ratio of residential population
and the current number of parking spaces do not reach the car ownership ratio in Central
Hungary Region (CHR) (394 cars/person) [10]. A shortage of parking spaces is shown
in Table II. Traffic generation points were represented by parking spaces.
As columns F and K in Table II show numbers of legal parking facilities are
currently serious problem in residential areas. Likewise, it seems to be the problem to
compare column D to the current National Urban Development and Construction
Requirements (NUDCR) [11] in force, which requires 1 parking space per dwelling
unit.
During assessment of sub-districts, it was important to analyze the potential
connections of residential street networks to urban arterial roads. These arterial roads
have significant tasks in traffic flow. Approximately length of these roads equals to
20% of entire street networks. Additionally, they carry on the 80% of traffic flow [12].
Therefore, the arterial roads with dense traffic appeared in microscopic models. Actual
traffic conditions of arterial roads were given from Center for Budapest Transport
(CBT). These roads were taking into account as exit roads from analyzed residential
areas.

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Pollack Periodica 13, 2018, 1
Table II
Important details of urban street networks
sub-districts
Number of legal
parking spaces [pc]
Number of dwellings
[pc]
B / C [pc/pc]
Residential population
[inhab]
B / E [pc/inhab]
Length of street
segments [km]
B / G [pc/km]
Area [km
2
]
B / I [pc/km
2
]
F / car ownership ratio
in CHR
A B C D E F G H I J K
BMi 5605 9403 0.596 21020 0.267 37.5 149.5 0.898 6244.0 0.678
BMii 4028 5573 0.723 12451 0.324 23.8 169.2 0.589 6835.0 0.822
GR 3920 4968 0.789 9894 0.396 19.4 202.1 0.426 9211.3 1.005
KD 2127 3319 0.641 7548 0.282 16.8 126.7 0.333 6395.4 0.716
PK 3717 4532 0.820 8978 0.414 32.7 113.7 0.704 5279.8 1.051
2.2. Examined models
The aim of this study was searching connections between the capacity of streets,
types of traffic calming methods and characteristics of the urban street layout at the
whole network [13]. For this purpose, static macroscopic transportation models were
used for traffic assignment. Intersections and street segments are the components of the
street networks. They have a deep influence for the traffic flow and in case of
interrupted oversaturated traffic conditions the regular functions of traffic flow are not
clearly useable most of the time [14], [15]. In this step PTV VISUM software has been
used. Earlier defined shape-files were imported from GIS database system to
microscopic models in every examined case. In traffic assignment, traffic from center
outwards was taken into account. Therefore, trip generation points were represented by
surveyed parking spaces with their locations and capacities. Trip attraction point was
determined as outer point. This outer point situated outside of the examined areas and
the earlier defined arterial roads ensured connections from sub-districts to attraction
point. The traffic assignment process is shown in Fig. 2.
During traffic assignment process, the street networks were loaded with the different
amount of traffic. Furthermore, personal vehicles were only simulated. In practice,
public transport services, pedestrian facilities and location of workplaces are impacted
on traffic [16]. Volumes of generated traffic in the networks were specified as the
combination of the capacity of parking spaces and the car ownership ratio in Central
Hungary Region (394 cars/person). The minimum of generated traffic was 10 percent of
the capacity of parking spaces, the maximum of generated traffic was equaled to the car
ownership ratio relating to the population of examined urban areas. In some cases, the
maximum traffic volume was higher than the car ownership ratio. At every incremental
step, traffic load was 10% higher than former step until volumes of traffic loads reached
the formerly defined maximum of generated traffic. The amount of traffic where mean
speed was suddenly decreased and mean travel time was suddenly increased, further

90 A. HÁZNAGY, I. FI
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intermediate values were analyzed. Generated traffic was higher in some cases than
actual traffic. Time period of traffic analysis was 1 hour in the morning. This approach
provides detailed picture of capacity and characteristics of traffic flow on urban street
networks.
Fig. 2. Urban street layout of selected micro-districts
The traffic lanes of street sections in the macroscopic model were defined with the
current street topology as mentioned earlier i.e. the number of lanes, one-way streets,
applied speed and traffic calming methods. Parking spaces were modeled as inner zones
in VISUM models. Intersections were determined with the Highway Capacity Manual
(HCM) based. Detailed node impedance calculation was applied in node editor (ICA)
module in case of VISUM models. This module ensures that the volume of traffic on
loaded traffic network has an influence on turning time delays at the intersections.
The applied intersections were named based on their locations in this paper as it is
shown in Fig. 2. The major intersection is an at-grade junction at the crossing of
boundary roads. The generated traffic could leave the inner zones through this type of
nodes. The boundary intersection is an at-grade junction at the boundary or arterial
roads. The inner intersection is an at-grade junction at the traffic calming areas.
Intersections in the macroscopic models were defined as follows. Intersections of
residential roads were defined as an all-way stop. Intersections of boundary and
collector roads were defined as a two-way stop. Major intersections and main junctions
of boundary and collector roads were defined as a roundabout, what type of
intersections they have.
Streets are named based on their locations similar to intersections as it is shown in
Fig. 2. The residential street is a traffic calmed street in the analyzed sub-districts. The
boundary street is a street at the boundary of sub-districts. The collector street is a main
street in the sub-districts. The local street is a street outside the sub-districts, and they
were not taken into account during macroscopic modeling. The arterial street is a major
street which ensures the connection with other parts of the settlement [17].
One direct link could be defined between two nodes only in the macroscopic model.
Therefore, the model does not contain any loop streets, and some nodes have only two
arms. They were defined as default. The summary of defined intersections is shown in

COMPARATIVE ANALYSIS OF HOUSING ESTATES ROAD NETWORKS 91
Pollack Periodica 13, 2018, 1
Table III. Moreover, the major intersections were handled in a similar way
independently their actual type (sign or signal intersection). This boundary condition
was important during the analysis process. Outgoing traffic from the sub-districts was
only analyzed. With these boundary conditions, the street networks of analyzed sub-
districts could be comparable based on their parking facilities and residential
population. Highly-detailed picture of diverse urban street layouts are given by this
method.
Table III
Important details of urban street networks
Sub-districts
Number of
major
intersections
(connecting
arterial roads)
Number of
roundabouts
Number of
two-way stops
Number of all-
way stops
Number of
default
intersections
BMi 3 8 55 78 53
BMii 4 4 24 67 16
GR 2 2 42 10 17
KD 4 4 16 46 23
PK 4 4 30 90 16
3. Results and discussions
3.1. Morphology analysis
The following terms were used in the process of data analyzing. These terms have
many variants and they have long term usages [18]. Street Network Density (SND) is
equal to the length of street segments divided by the area of sub-district. Street Segment
Density (SSD) is defined as the number of street segments divided by the area of sub-
district. Intersection Density (ID) is defined as the number of intersections divided by
the area of sub-district. Connectivity Index (CI) is also called as Link to node ratio. It
equals to the number of street segments divided by the number of intersections. Shape
Ratio Index (SRI) is defined as the area of circumscribed circle of sub-district divided
by the area of sub-district. A shape of area has been characterized with this ratio index.
Besides, shape ratio of a square equals to 0.637. If this ratio is lower than 0.637, the area
is oblong. Results of morphological analysis are shown in Table IV.
The analyzed sub-districts are micro-districts with residential land use. Their street
patterns are similar to each other but they have differences in some characteristic terms.
Morphological results are detailed by the following paragraphs. The major differences
in analyzed street networks appear with values of ID, SSD, SND and CI. Directed
streets networks do not contain the banned direction. Based on originally designed street
networks, they could be separated into two groups. BMi and GR are included in the first
group, BMii, KD and PK are included in the second group. Because of this, the
following results have significant differences. KD has the highest values of analyzed
measures. This is due to the shape of micro-district. It is long and thin, and its inner

92 A. HÁZNAGY, I. FI
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street network is dense as it is shown in Table IV. Street network of GR has contrary
results. It has a rare street network based on SSD and ID. Analyzing directed networks,
value of SND is similar to PK and it is higher than values of BMi and BMii. The case of
CI acts similarly. It means, street network of GR is not as dense as others taking the
number of directed street segments into account. But the length of street segments, the
value of SND is higher than BMi and BMii. Therefore, it is denser than them. The street
network values of PK are similar to BMi and GR. Taking SSD into account, the value is
close to the result of BMi. Additionally, CI values are close to values of GR. Some
similarities exist between the street network of BMi and BMii. Similarity is due to
similar building period and their vicinity. BMi has the longest street network. BMii has
the lowest SND value and values of SSD and CI are close to the value of GR.
Intersections have the highest influence on traffic flow next to the road segments.
Districts with lower CI value have fewer road paths and alternative routes. Number and
location of major intersections are very important because traffic could leave the inner
zones through these nodes. The proportional of intersections at the boundary roads to
inner intersections is similarly important aspect. SRI shows the shape of GR and KD is
oblong and the shape of BMi, BMii and PK is almost square.
Table IV
Important details of urban street networks
Sub-district of
Budapest
Area [km
2
]
Number of
intersections[pc]
Intersection density
[pc/km
2
]
Number of street
segments [pc]
Street segment density
[pc./km
2
]
Length of street
segments [km]
Street network density
[km/km
2
]
Connectivity index
[pc./pc]
Shape ratio index
[km
2
/km
2
]
Number of connecting
arterial roads [pc]
BMi 0.898 194 216.0 526 585.7 37.5 41.7 2.711 0.496 3
BMii 0.589 111 188.5 310 526.3 23.8 40.4 2.793 0.607 3
GR 0.426 71 166.7 214 502.3 19.4 45.5 3.014 0.264 2
KD 0.333 89 267.3 285 855.9 16.8 50.5 3.202 0.266 4
PK 0.704 140 198.9 415 589.5 32.7 46.5 2.964 0.499 4
3.2. Results of traffic assignment
Travel time and speed
The following section contains results of the macroscopic modeling process. Time
period was 1 hour in every case, and traffic volume were depended on the population of
sub-districts. Travel time was measured from inner trip generation points (parking
places) to outer trip attraction point in every situation as it is shown in Fig. 2. The
connection between mean travel time versus traffic volume and mean speed versus
traffic volume are shown in Fig. 3 and Fig. 4. In Fig. 3 the mean speed is plotted along

COMPARATIVE ANALYSIS OF HOUSING ESTATES ROAD NETWORKS 93
Pollack Periodica 13, 2018, 1
the y-axis and traffic volume is plotted along the x-axis. In Fig. 4 the mean travel time is
plotted along the y-axis and traffic volume is plotted along the x-axis. Based on the
results, the sub-districts could be separated into two groups. BMi and GR are part of the
first group, and BMii, KD and PK are part of the second group. The number of major
intersections has an important effect on the results and the classification. The first group
has approximately 5 km/h higher mean speed than the second group with initial traffic
volume. Boundary and collector streets are available from inner streets via a few
intersections. Therefore, the vehicles have reached higher mean speed than in other
cases. Around 2000 veh/hr in the case of GR and 3000 veh/hr in case of BMi, the mean
speed is suddenly decreased and travel time is increased by the results. Outcomes are
connected with the number of major intersections. Major intersections of street layouts
are reached their capacity at these traffic conditions, and street networks are
simultaneously oversaturated. Traffic from generated points could be reached attraction
point via few inner intersections. Hence, surround traffic of intersections were
interrupted. This phenomenon appeared also in BMii, KD and PK in a focused way as
the case of BMi and GR.
Fig. 3. Mean speed vs. traffic volume Fig. 4 Mean travel time vs. traffic volume
Results of modeling process could be explained by the number of major
intersections (exits) of the sub-districts. Moreover, they have greater influence on traffic
flow than the types of street segments in urban space. This could explain the unique
behavior of BMii. The behavior of street network is a little bit different from KD and PK
under increasing traffic volume. Simultaneously it is almost part of the first group. It
has 3 major intersections similar to BMi, but its traffic assignment outcome is close to
KD. The slope of the middle part graph in Fig. 3 is not as steep as BMi or GR.
Therefore, traffic volume from approximately 2500 veh/hr mean travel time is gradually
increased and the mean speed is decreased. After a critical traffic volume, mean speed is
constantly low in every case. Urban street networks are oversaturated, and the required
space of vehicles is close to the total length of street networks.
Speed-density and slowness-speed connection
Relationship of speed and density is shown in Fig. 5. This connection is part of
fundamental diagram of traffic flow. That is adequate described in case of street

94 A. HÁZNAGY, I. FI
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segments but not for entire street network. Type and number of intersections and street
segments have a great impact on the results. Further research is needed to describe the
exact connection among parts of street networks.
The slowness [19] (specific travel time) equals to the change in time per unit
distance [s/m] as a function of distance is defined in (1), where t is the travel time; x is
the distance and w is the slowness,
() ()
dx
xdt
xw =
. (1)
The connection between speed and slowness is shown in Fig. 6.
Fig. 5. Mean speed vs. traffic density Fig. 6. Slowness vs. mean speed
The figure shows, due to a fixed increase in speed the savings in travel time
becomes smaller, the greater the value of final speed. In Fig. 4, the slowness is plotted
along the y-axis and the mean speed of vehicles is plotted along the x-axis. The fitted
curves as exponential decay 3 is represented with grey line. Applied form of exponential
decay 3 fit (2) was the following:
321
3210 txtxtx
eAeAeAyy
−−−
+++=
, (2)
where y
0
is the offset; A
1
, A
2
, A
3
are the amplitude; t
1
, t
2
, t
3
are the decay constant. For
every data, only one curve was fitted. The R-square value of this fitted curve is 0.98.
Parameters of exponential decay 3 fit are shown in Table V. It shows that data from
traffic assignments and street networks of neighborhoods are similar to each other.
Points of graphs in Fig. 6 are similar to each other, they overlap one another.
Despite the fact, that layout of street networks and residential population are different.
The exponential fit has similar shape to professional literature [20]. Differences among
sub-districts in travel time and speed are not detectable.

COMPARATIVE ANALYSIS OF HOUSING ESTATES ROAD NETWORKS 95
Pollack Periodica 13, 2018, 1
Table V
Parameters of exponential fit in case of Fig. 6
y
0
value
A
1
value
t
1
value
A
2
value
t
2
value
A
3
value
t
3
value
Statistics
adj. R-
square
-3581.18 3802.58 1153.63 1711.56 4.88 94968.1 0.49 0.978
Delay at intersections
Intersections have an impact on vehicles in travel time delay. These delays depend
on the type and location of intersections and the volume of traversing traffic. The mean
of total time delay at one intersection was calculated by the proportion of the number of
that type of intersections to total time delay at that type of intersection. Results are
shown in Fig. 7. In the plotted graphs the y-axis presents the average of total time delay
at intersections and the x-axis the traffic volume is assigned.
Parameters of exponential and linear fits are shown in Table VI and Table VII.
Measured points at roundabout graphs could be fitted with exponential curves, and
graphs of two-way stop and all-way stop could be fitted with linear curves. Formula of
used exponential fit (3), where y
0
is the offset, A is the amplitude and R
0
is the rate, and
linear fit (4), where a is the intercept and b is the slope. Equations of fits were the
following:
xR
Aeyy
0
0
+=
, (3)
bxay +=
. (4)
Values of y-axis are different in some cases. Outcomes depend on the number and
location of major intersections. That could be explained by appearing traffic volume of
major intersections and intersections of boundary and collector roads are higher than in
case of other types of intersections but their numbers are lower than others.
Table VI
Parameters of exponential fits in case of Fig. 5, R: Roundabout
Intersections at sub-districts y
0
A R
0
R
2
BMi R -6.25 7.51 0.001 0.996
BMii R -0.70 2.73 0.001 0.964
GR R -6.59 10.52 0.002 0.993
KD R -44.03 38.89 0.001 0.994
PK R -58.33 52.00 0.001 0.991

96 A. HÁZNAGY, I. FI
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Fig. 7. Average of total time delay at intersections vs. traffic volume
4. Conclusions
Based on given results, the traffic assignment is affected by shape of area, numbers
and locations of connecting arterial roads, urban street layout and applied traffic
calming systems. The aim of the examination was comparing different urban street
layouts. Hence, morphological analyzes and traffic assignment was used at existing road
networks of five selected micro-districts. The results of examination of street network
could be summarized with following statements. Characteristic values of traffic flow
could be influenced by numbers of major intersections and their locations. Their effects
are stronger than street network patterns. Under increasing traffic volume, exponential

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time delay exists at major intersections, and linear time delay exists at boundary and
inner intersections. Slowness-speed connection represents the quality of traffic. The
graph has exponential function. In the further work, theoretically, grid layout model
with different shape will be used for deeper results.
Table VII
Parameters of linear fits in case of Fig. 5, T: Two-way stop; A: All-way stop; R: Roundabout
Intersections at sub-districts a b R
2
BMi T -0.531 0.005 0.998
A -1.335 0.003 0.978
BMii T -3.787 0.022 0.996
A -4.561 0.001 0.851
GR T -0.122 0.001 0.980
A -2.033 0.023 0.999
KD T -1.670 0.014 0.929
A -0.312 0.002 0.996
PK
T -5.637 0.013 0.962
A -0.529 0.003 0.997
R 62.338 0.072 0.950
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