simulated impacts of 3d urban morphology on urban transportation in megacities: case study in...

Simulated impacts of 3D urban morphology on urban transportationin megacities: case study in Beijing

Shuo Liua, Xiangtao Fana, Qingke Wenb*, Wei Liangb and Yuanfeng Wua

aKey Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, ChineseAcademy of Sciences, Beijing, China; bThe Institute of Remote Sensing Applications, Chinese

Academy of Sciences, Beijing, China

(Received 18 November 2011; final version received 18 September 2012)

Urban morphology and morphology change and their impacts on urbantransportation have been studied extensively in planar urban space. The essentialfeature of urban space, however, is its three-dimensionality (3D), and few studieshave been conducted from a 3D perspective, overly limiting the accuracy ofstudies on the relationships between urban morphology and transportation. Theaim of this paper is to simulate the impacts of 3D urban morphologies on urbantransportation under the Digital Earth framework. On the basis of the principlethat population distribution and movement are largely confined by 3D urbanmorphologies, which affect transportation, high spatial resolution remote sensingimagery and a thematic vector data-set were used to extract urban morphologyand transportation-related variables. With a combination of three researchmethods � factor analysis, spatial regression analysis and Euclidean allocation� we provide an effective method to construct a simulation model. The paperindicates three general results. First, building capacity in the urban space has themost significant impact on traffic condition. Second, obvious urban spaceotherness, reflecting both use density characteristics and functional character-istics of urban space, mostly results in heavier traffic flow pressure. Third, nosingle morphology density indicator or single urban structure indicator can reflectits contribution to the pressure of traffic flow directly, but a combination of thesedifferent indicators has the ability to do so.

Keywords: digital city; 3D urban morphologies; simulation of urbantransportation; spatial regression; euclidean allocation; feature factor of 3Durban morphology

1. Introduction

It is expected that by 2050 nearly 70% of the world’s population will live in cities

(United Nations 2007). This indicates that urban space will continue to grow rapidly,

in particular in megacities of developing countries (Khisty 1993; Kenworthy 1995;

Gakenheimer 1999). In the process of rapid urban expansion, urban transportation

problems become more and more serious.

Many factors are claimed to increase transport-related urban problems in these

growing megacities, for example, continuing population growth, skyrocketing

car ownership and weak traffic management. However, the impact of urban

morphologies on urban transportation is a fundamental factor. It is obvious that

*Corresponding author. Email: [email protected]

International Journal of Digital Earth, 2014

Vol. 7, No. 6, 470�491, http://dx.doi.org/10.1080/17538947.2012.740079

# 2012 Taylor & Francis

mailto:[email protected]

http://dx.doi.org/10.1080/17538947.2012.740079

three-dimensional (3D) urban spaces are the main storage space of population and

materials. Both people and material need moving in the urban. The urban

transportation network is the exact linkage path of their movements. 3D urban

spaces, composed mainly of buildings, can be reflected by 3D urban morphologies,

which determine the transportation network’s morphology and condition to a certain

extent. Therefore, a more effective study could be introduced to reduce the negative

traffic effects of urban morphology.Recently, the impacts of urban morphology on transportation have received

much attention. Cao et al. (1998) took the Des Moines metropolitan area as a case

study, demonstrating the relationships between urban population density, travel

patterns, and residential and commercial distribution. They then recommended

further strengthening of the relationship between land use and transportation in

travel planning models. Transportation Research Board’s Transit Cooperative

Research Program Report 74 (Burchell et al. 2002) analyzed the impact of urban

sprawl on resources, including land conversion and local road infrastructure costs,

ending with a discussion of the benefits of sprawl and ways to reduce the negative

effects. At the Canada Centre of Remote Sensing (Zhang and Guindon 2006;

Guindon and Zhang 2007), a Landsat-based Canadian national urban land database

was generated along with a set of urban sustainability indicators as value-added

information to inform transportation energy policies. On the basis of census and

survey data, the Transportation Analysis Simulation System was developed. It is an

agent-based simulation system of simulating the second-by-second movements of allpedestrian and vehicles through a regional transportation network (Nagel and

Rickert 2001; Cetin et al. 2002). Using data acquired by the 1991 Activity�Travel

Survey in Boston, Zhang (2005) analyzed the relationship between urban morphol-

ogy and nonwork travel. He tested the role of spatial accessibility as a composite

measure of urban morphology in explaining individuals’ nonwork activity participa-

tion, travel times, and travel frequencies. The results showed varying effects of

modifying spatial accessibility on nonwork activity participation and travel among

different activity categories. Bhatia (2005) explored a number of spatial character-

istics of existing urban development patterns in Erie County, New York. Through a

cross-sectional analysis, these measures of urban morphology were used to test how

sprawl affects municipal transportation costs. In addition, Li (2006) discussed the

relationships between urban morphology and morphology of transport network,

traffic patterns, as well as traffic structure. Moreover, McMillan (2007) examined the

influence of objectively measured urban morphology on travel mode to school and

also the magnitude of influence urban morphology and nonurban morphology

factors have on children’s travel behavior. In the last two years, the impacts of urban

morphology on urban transportation and transportation-related energy consump-tion have become a research focus. One of the key points is to reveal the relationships

between urban morphology and urban transportation. Using the Census Data of

Ireland 2006 and the Place of Work � Census of Anonymised Records datasets,

Carty and Ahern (2010) intended to examine urban morphology factors in terms of

their influence on a journey to work’s energy consumption. To examine the

hypothesis, the transport energy consumption of different urban morphologies was

found, thus allowing the most sustainable urban morphologies in the Greater Dublin

Area to be identified. Using the 2001 National Household Travel Survey data, Liu

and Shen (2011) empirically examined the effects of urban land use characteristics on

International Journal of Digital Earth 471

household travel and transportation energy consumption in the Baltimore metro-

politan area. The results of regression analysis showed that different built

environment measures lead to substantially different findings regarding the

importance of urban morphology in influencing travel behavior. Chen et al. (2011)

revealed the dynamic distribution of urban land use based on land use classification

by using remote sensing images from 2005 to 2008. The urban land use patterns were

then quantified using a set of landscape metrics, which further served as explanatory

variables in the estimation. The panel data analysis was implemented to estimate the

relationship between urban land use patterns and energy consumption.

In the process of urbanization, more and more urban demands need to be met,

which definitely need more urban space. When the urban is short of or run out of

land resources, the urban will begin to grow higher into vertical space instead of

sprawling larger in planar space. 3D characteristics have become the main features of

urban morphology. But recent studies still have the following limitations: (1) due to

the difficulty of acquiring 3D urban data, current studies on urban problems are still

limited to the planar dimension. There have been few studies of the relationship

between 3D morphology and transportation. (2) Urban morphology and transpor-

tation have been studied extensively on the overall macro level or in the changing

region of its boundary. But few studies have been on the building level (taking each

building as the study unit) to study the relationship between 3D morphology and

transportation. (3) Most of the transportation models have been constructed on the

basis of population data or human behavior data as the main data to analyze the

impacts on urban traffic conditions. Their accuracy, however, is highly affected by

original data uncertainties caused by mobility of the population, which makes

statistics of urban population distribution and investigation of human behavior

extremely difficult.

Examining the case of Beijing, the aim of this paper is to reveal the relationship

between 3D urban morphology and urban transportation through modeling and

spatial simulations to solve the above problem. The study is based on the principle

that population distribution and movement is largely confined by its living space (3D

urban morphology). 3D urban morphologies will be quantified to minimum units

(building and linkage road) by a set of 3D urban morphology indicators, which are

further taken as the explanatory variables for traffic pressure. Spatial data analysis,

factor analysis, spatial regression analysis, and Euclidean allocation will then be

integrated to estimate the impacts of 3D urban morphology on traffic pressure and

urban transportation. The study results could be used in the policy implications of

urban growth management for developing sustainable transportation. This paper

presents the study in five parts. After Section 1, Section 2 introduces the study area

and the data sources. Section 3 presents the research process and methodology.

Section 4 reports the results and accuracy analysis of the implementation of the

Beijing Dongcheng District case. Finally, Section 5 concludes the discussion.

2. Study area and data sources

2.1. Study area

Beijing, the capital of China, was chosen as the study area (Figure 1a) and

Dongcheng District of Beijing as the validation area (Figure 1b).

472 S. Liu et al.

Beijing is the center for national politics, culture, transport, tourism, and

international exchanges of China. The city center of Beijing is located at north

latitude 39856?, east longitude 116820?. The whole city is made up of 14 districts and

2 counties, with an area of 16,410.54 km2 and a population of 19,612,368 as of 2010.

The urban area of Beijing is about 1368.32 km2, and the built-up area reaches

1289.30 km2 (The largest city in China).

Dongcheng District is an urban district in Beijing, which covers the eastern half

of Beijing’s urban core. The southern boundary is the South Third Ring Road; the

northern boundary is the North Third Ring Road; the western boundary is the

central urban axis; and the eastern boundary is the East Second Ring Road. It is an

area combining active politics, highly developed education, science and technology

research, trading services, and cultural tourism with plentiful resources. As one of the

central districts of the capital, Dongcheng District covers several important parts of

Beijing. China’s central government agencies, major political event venues, national

research institutions, Chinese cultural relics (2 world cultural heritages and 26

national key cultural relic protection units), and the most important urban

commercial district of China all belong to its jurisdiction. It is 41.48 km2 in area.

The registered population is 0.919 million (Leading Group Office for Sixth

Nationwide Population Census in Beijing 2011). With the exception of parks such

as the Forbidden City and Temple of Heaven (the area with no building data shown

in Figure 1 has no resident population), population per square kilometer in

Dongcheng district is around 25,000, which is far greater than the average level of

Beijing city by 10,306 people per square kilometer. The seriously high population

density creates heavy pressure on urban traffic, which makes Dongcheng District an

appropriate case region for studying the impact of urban morphology on urban

transportation.

Figure 1. Study area � Beijing, China, and validation area � Dongcheng District of Beijing.


2.2. Data source and data processing

Two aerial images acquired in 2008 and 2009, and one high spatial resolution satellite

remote sensing data (Quickbird) in 2007, were used in this research. The aerial

remote sensing images were georeferenced to Universal Transverse Mercator

projection, resampled to a pixel size of 0.3 m � 0.3 m, and the high spatial

resolution satellite remote sensing images were georeferenced to the same projection

and pixel size. The serial images from 2007 to 2009 were employed to extract spatial

data of buildings and traffic.

The spatial information of buildings has multiple attributes, including building

area, number of floors, center location, and use purposes. Image visual interpretation

method was used to extract the building vector map in 2009, which offers the

building area and center location information. Field survey method obtained the

numbers of floors and use purposes information in 2009.

The spatial information of traffic includes attributes of road length, width, and

road level as well as the traffic flow. Image visual interpretation method was used toextract the spatial distribution map of road in 2009, which offers the road length,

width, and road level attributes by using the spatial analysis.

The traffic flow was composed by vehicles and pedestrians. Image visual

interpretation method was used to extract the contour of the vehicles and human

head. Finally, the geometric center point was calculated using ARCGIS software and

creates the traffic flow point coverage. The traffic flow in 2007, 2008, and 2009 were

interpreted as the basic map to calculate the traffic pressure indicator.

All these spatial data are in vector format with accurate location and topology

relationships at a scale of 1:2000.

3. Methods

3.1. Basic principles and research process

Urban transportation is a network system. Generally, in a network system, the flow

of material and information is always being affected by three factors of the network:

the property and size of the storage body, the form and resistance of the path, and

the property of the flow material (Alexander and Sadiku 2007). For example, the

circuit network, the water supply and drainage network, as well as the communica-

tion network all follow this law.This law is also present in urban transportation networks according to a large

number of observed data and information. Three factors affecting traffic flow in the

urban transportation network involve the function and morphology of the urban

space, the type and morphology of the transportation network, and people’s trip

willingness and trip carrier (Figure 2) (Groeger and Rothengatter 1998; Jiang and

Liu 2009; Qin et al. 2006). People’s trip destination, trip carrier, and path selection

are always determined by which or where or how far the destination space is, so the

two factors, people’s travel behavior and transportation network, are finally driven

by the third factor � the function and morphology of the urban space. Thus, the

manner of people flow and material flow in the city is finally affected by their living

space (3D urban morphology and its inner function) to a great extent. This shows

that the methodology used in this paper, simulating traffic condition by studying 3D

urban morphology, is reliable.

474 S. Liu et al.

Based on the above principle, the numerical simulation and visualization of the

impacts of 3D urban morphology on road traffic conditions were illustrated, taking

city buildings and roads as the smallest unit, by using a high spatial resolution

data-set of Beijing obtained by Earth observation technology. The research process

was as follows. Firstly, a series of 3D urban morphology indicators were selected

preliminarily as factors affecting traffic pressure. In observation sample regions (a set

of typical blocks), the values of their 3D urban morphology indicators werecalculated on the basis of large-scale building vector data of Beijing. And then, factor

analysis was applied to eliminate the multicollinearity between these factors in order

to elect the integrated 3D urban morphology indicators as final factors affecting the

traffic pressure. The traffic pressure level of each road in the sample region was

determined by real vehicles and human flow conditions on the road. Visual

interpretation method was used to extract normally real vehicles and human flow

conditions from multi-temporal high spatial resolution remote sensing images

received in different times of the day. The values of 3D urban morphology indicatorswere subjected to spatial multiple linear regression analysis as independent variables

and traffic pressure level as the dependent variable. Then, the independent variable

coefficients in the multiple regression equation were calculated to construct the

numerical simulation model of the 3D urban morphology’s impacts on road traffic

pressure. By using this model, the rating of the impacts was calculated and then

allocated to each building according to the capacity proportion of each building

among all the buildings in the region. Next, the traffic pressure carried by each

building (the source of pressure) was allocated to the target (the road network)following the Euclidean allocation method in order to get the traffic pressure rating

of the urban road network. Finally, we divided the traffic pressure rating of the

urban road network by road width in order to determine the traffic condition rating

of each road. Figure 3 depicts the research process.

3.2. Characterizing the 3D urban morphology

Buildings are the main component of the 3D urban morphology, so urban

morphology indicators are constructed at the scale of building groups in this study.

Figure 2. Schematic diagram illustrating the study principles.


In 3D urban space, the height and volume of buildings and their spatial distributions

represent the main features of the 3D urban morphology. Meanwhile, varieties of

buildings with different height or volume located together under multiple combina-

tions of ways in urban space also form different 3D urban morphologies (Ge 2009).

Thus 3D urban morphology indicators were selected primarily on consideration of

building distribution features in 3D urban space, including the density indicator,

otherness indicator, composition indicator, and structure indicator. They all havelinear relationships with traffic pressure and are defined as follows:

3.2.1. Density indicator

� Building distribution density (Density1) is defined as the sum of buildings

divided by the area of the buildings’ distribution region.

Density1 ¼Xn

i¼1

i=S (1)

where i is the number of buildings and S is the area of the building’s distribution

region. This indicator reflects the building distribution in the urban space with a

larger Density1 value indicating more building individuals per unit region.

Building area density (Density2) is defined as the sum of individual building areas

divided by the area of the buildings’ distribution region.

Density2 ¼Xn

i¼1

Ai=S (2)

where Ai is the area of building i, i is the number of buildings in the given region,

and S is the area of the buildings’ distribution region. This indicator reflects urban

land occupation with a larger Density2 value, indicating more lands were occupied by

the buildings per unit region.

Height distribution density (Density3) is defined as the sum of each building’s

height divided by the area of the buildings’ distribution region.

Density3 ¼Xn

i¼1

Hi=S (3)

where Hi is the height of building i, i is the number of buildings in the given

region, and S is the area of the buildings’ distribution region. This indicator reflects

Figure 3. Research process.

476 S. Liu et al.

the distribution of buildings with variable heights, i.e. building height distribution in

the urban space. A larger Density3 value indicates higher building height per unit

region.

Capacity distribution density (Density4) is defined as the sum of each building’scapacity divided by the area of the building’s distribution region.

Density4 ¼Xn

i¼1

Ci=S (4)

where Ci is the capacity of building i, defined as building area plus number of

floors instead of height because the number of floors is more correlated to the traffic

pressure; i is the number of buildings in the given region; and S is the area of the

buildings’ distribution region. This indicator represents the vector dimension of

urban space usage density by the building groups in an urban area unit. A largernumber means more vector dimension urban space is used by building groups. It

reflects the land use density.

3.2.2. Otherness indicator

Building otherness is defined as the standard deviation in the height of building

groups divided by mean building height.

Otherness ¼ rH

.(5)

where s is the standard deviation in the height of building groups, and �H is the

mean building height. This indicator reflects the scattering degree of building height.It describes the relative difference in degree of each building’s height in the urban

space. A larger number indicates that building height is scattered more obviously (Ge

2009).

3.2.3. Composition indicator

Evenness is defined as

Evenness ¼ 1�Xn

i¼1

Ai

AT

� Ci

CT

�� (6)

where Ai is the area of building i, AT is the total area of each building, Ci is the

capacity of building i, and CT is the sum capacity of each building. This indicator

refers to how close the cubic shapes of each building in a region are, with a larger

evenness value indicating more closeness (Ge 2009).


3.2.4. Structure indicator

Moran’s I is used as an indicator to measure similarity.

I ¼n�

Pni¼1

Pnj¼1

wij xi � xmð Þ xj � xm

� �

Pni¼1

Pnj¼1

wij �Pni¼1

xi � xmð Þ2(7)

where xi is an attribute for feature i, xm is the mean of the corresponding attribute, n

equates to the total number of features in the corresponding region, and wij is the

spatial weight between feature i and j. In this study, capacity is used as x. When the

distance between i and j is shorter than the threshold, wij equals 1; otherwise, wij equals

0. This indicator evaluates whether the building structure is clustered, dispersed, or

random. A larger index value indicates clustering and a homogeneous structure, while

a smaller index value indicates dispersion and a diverse structure.

3D urban morphology indicators were calculated according to the formulasabove in a set of sample regions selected from the study region as a real data-set.

Together with the rating of the urban traffic pressure, they were then subjected to

spatial multiple linear regression analysis, taking the value of 3D urban morphology

indicators as independent variables and transportation pressure level as the

dependent variable, thus constructing the numerical simulation model of the impacts

of 3D urban morphology on road traffic pressure.

3.3. Construction of independent variables from factor analysis

The independent variables were primarily calculated upon 20 training samples

chosen from the study area � Beijing city � with the same area and shape (Figure 4).

Twenty training samples were selected from a set of randomly generated samples

according to the principle that they should distribute evenly and represent varietiesof urban functions in order to ensure the universality of the spatial distribution and

function of the sample. The sample size was determined by majority area.

The 3D urban morphology indicators of the samples were computed on the basis

of formulas 1 to 7, including Density1, Density2, Density3, Density4, Otherness,

Evenness, and Similarity. The number of vehicles and estimated human flow density

per unit length on the roads of the samples were interpreted from aerial remote

sensing images and high spatial resolution satellite images. The traffic pressure has

tidal changes in one day because different urban spaces have their unique function sothat human’s daily life is active in different urban spaces according to the time of the

day. In order to ignore the daily changes of the traffic pressure, normal condition

of the traffic pressure is used in this study. The average value of the traffic

condition integrated from three images in different daily times is calculated to

present the normal condition of the traffic pressure. According to this transportation

information, the traffic pressure (y) level of each road in the sample region was

scored with a minimum value of 0 and maximum value of 10 by using the following

equation:

y ¼ 10 F � Fminð ÞFmax � Fmin

(8)

478 S. Liu et al.

where F is the number of vehicles and estimated human flow density per unit

length on the roads, Fmin is the minimum number value of F, Fmax is the maximum

number value of F, and y is the traffic pressure (y) level.

The results of the traffic pressure (y) levels are shown in Table 1.

The interrelationships between the seven urban morphology indicators were

analyzed using factor analysis. Factor analysis uncovers the structure of the

variability in data and therefore detects multicollinearity. The mean, standard

deviation and sample number of the seven factors are given in Table 2.

Table 3 shows the correlation matrix of the seven variables.Principal component analysis was used to extract factors. The seven variables

were rotated by varimax with Kaiser normalization, therefore simplifying the factor

loading matrix as shown in Table 4.

Figure 4. Spatial distribution of the sample data.


Let X1, X2, X3, X4, X5, X6, and X7 represent Density1, Density2, Density3,Density4, Otherness, Evenness, and Similarity, respectively. The factor model is

FAC1 ¼ 0:958X1 þ 0:896X2 þ 0:959X3 � 0:227X4 � 0:292X5

� 0:146X6 � 0:284X7

(9)

FAC2 ¼ �0:243X1 þ 0:200X2 � 0:058X3 þ 0:783X4 þ 0:910X5 � 0:881X6

� 0:529X7 (10)

As shown in the factor model, factor one FAC1 indicates density features ofurban space primarily, and factor two FAC2 reflects structure features and

composition features of the urban space. The scores of FAC1 and FAC2 in the

sample region are listed in Table 5.

Table 1. Urban form variables and traffic pressure values in the sample area.

No. Density1(10-2) Density2 Density3 Density4 Otherness Evenness Similarity y

1 0.1457 0.2400 0.0267 1.9620 1.4527 0.0806 �0.01 6.5

2 0.0535 0.2180 0.0040 0.8755 0.8507 0.5057 0.06 5

3 0.0644 0.2127 0.0041 0.9349 0.9707 0.4757 0.02 5.5

4 0.0236 0.2006 0.0033 1.1157 0.4541 0.8575 1.01 4.7

5 0.0244 0.2316 0.0035 2.9211 1.3353 0.5303 �0.02 9.1

6 0.0248 0.1676 0.0071 2.3361 1.2614 0.0628 �0.01 9.6

7 1.6435 0.5387 0.0502 0.6195 0.2209 0.7557 0.01 2.5

8 0.5279 0.3735 0.0176 0.6265 0.5753 0.3484 0.03 3.1

9 0.5149 0.4250 0.0199 1.1848 0.8400 0.3133 0.03 5.6

10 0.2265 0.3077 0.0115 1.1818 1.2618 0.3710 0.07 6.3

11 0.0333 0.2661 0.0083 3.4351 1.1224 0.4893 0.01 7.3

12 0.0333 0.2212 0.0044 1.0811 1.0471 0.6273 0.05 7

13 0.1461 0.3201 0.0105 1.6605 1.3007 0.3903 0 5.7

14 0.1208 0.3752 0.0090 2.4822 1.7316 0.0140 0.03 6.1

15 0.1987 0.3399 0.0121 1.6913 1.2707 0.4087 0.04 6.3

16 0.0122 0.0173 0.0004 0.0173 0.0000 1.0000 0.11 0.1

17 0.2231 0.3336 0.0096 0.8978 0.7799 0.3669 0.03 6.6

18 1.4562 0.5571 0.0460 0.8366 0.3700 0.4626 0.01 3.1

19 0.0968 0.1469 0.0073 1.2102 1.1898 0.6433 �0.15 6.2

20 0.2442 0.4180 0.0127 2.1457 1.4230 0.1611 0.01 8.5

Table 2. Descriptive statistics of the seven selected variables.

Mean Standard deviation Analysis N

Density1 0.0029 0.0046 20

Density2 0.2955 0.1312 20

Density3 0.0134 0.0134 20

Density4 1.4608 0.8568 20

Otherness 0.9729 0.4560 20

Evenness 0.4432 0.2564 20

Similarity 0.0665 0.2276 20

480 S. Liu et al.

3.4. 3D analysis of the impacts of urban morphology on urban traffic pressure frommultiple linear regression analysis

The scores of FAC1 and FAC2 were subjected to multiple linear regression analyses

as independent variables and traffic pressure level of the sample region (y) as the

dependent variable. Then, the regression coefficients of the independent variables in

the multiple linear regression equation were calculated as listed in Table 6.Let x1 and x2 represent FAC1 and FAC2, respectively. The equation expressing

the impacts of 3D urban morphology on urban traffic pressure is

y ¼ 5:74� 0:728x1 þ 1:742x2 (11)

As mentioned above, FAC1 (x1) primarily indicates the density features of urban

space, and factor two FAC2 (x2) reflects urban space structure and composition

features, which reveals that urban space with remarkable density features such as a

large number of buildings, with large area, as well as with large height density, does not

contribute much to traffic pressure. Most of these spaces with high land occupationdensity in the study area are low-rise buildings with small building gaps, fewer floors,

and small capacity, and also have a simple function, so they create relatively smaller

traffic flow nearby, and also exert less pressure on transportation. On the other hand,

the urban spaces with remarkable spatial structure or composition features, especially

capacity density and otherness, contribute more to traffic pressure. The buildings with

high capacity always have plenty of functions and also accommodate more people.

Meanwhile, most of the urban spaces with obvious building otherness have more high

buildings, complex functions, and high capacity. Such urban spaces mostly have heavytraffic flow and also create large pressure on transportation.

Table 3. Correlation matrix of the seven variables.

Correlation Density1 Density2 Density3 Density4 Otherness Evenness Similarity

Density1 1.000 0.796 0.943 �0.354 �0.500 0.110 �0.138

Density2 0.796 1.000 0.789 �0.007 �0.048 �0.316 �0.163

Density3 0.943 0.789 1.000 �0.208 �0.321 �0.078 �0.194

Density4 �0.354 �0.007 �0.208 1.000 0.743 �0.518 �0.161

Otherness �0.500 �0.048 �0.321 0.743 1.000 �0.734 �0.333

Evenness 0.110 �0.316 �0.078 �0.518 �0.734 1.000 0.398

Similarity �0.138 �0.163 �0.194 �0.161 �0.333 0.398 1.000

Table 4. Rotated factor (component) matrix.

Component

1 2

Density1 0.958 �0.243

Density2 0.896 0.200

Density3 0.959 �0.058

Density4 �0.227 0.783

Otherness �0.292 0.910

Evenness �0.146 �0.881

Similarity �0.284 �0.529


3.5. Spatial allocation of traffic pressure to the urban road network and trafficcondition calculation

The traffic pressure level of the region, calculated by Equation (11), was distributed

to each building spatially, according to the capacity proportion of each building

among all the buildings in the region.

The rule of the allocation direction was determined by Euclidean distance from

the center of each building (pressure source) to the nearest road (pressure target),

according to the real phenomenon that people prefer to select the nearest path to

travel, and the building’s exit is always opposite to the nearest road. Obeying the

allocation rule, the traffic pressure carried by each building was divided into the

nearest road (Figure 5). Therefore, each road in the transportation network has its

own traffic pressure score. Finally, the traffic condition level was calculated using the

traffic pressure score divided by the width of the road.

4. Implementation, results, and accuracy analysis

4.1. Results of the simulation model implementing in the validation area

Dongcheng District of Beijing was used for evaluating our method from Section 3.

The validation area was divided into proper size of grids. The cell size was determined

on the basis of the principle that the 3D urban morphology can be represented by the

indicators sufficiently in each cell and also can reduce the bias arising from too

smaller or too larger grid. If the grid is too small to cover the maximum building

occupation area, the height of the space in the grid will be a single value. Similarly, if

the grid is too large, one cell will cover too many building types, volumes, and also too

Table 5. Factor score of each sample.

No. FAC1 FAC2

1 0.1473 1.0831

2 �0.5581 �0.4164

3 �0.5528 �0.2329

4 �1.2069 �2.0215

5 �0.7178 0.7115

6 �0.5922 1.0281

7 2.5733 �1.1606

8 0.6255 �0.3845

9 0.7378 0.1022

10 �0.0702 0.2322

11 �0.5086 0.7889

12 �0.6317 �0.3475

13 �0.1253 0.4869

14 �0.0526 1.6250

15 �0.0226 0.4235

16 �1.1788 �2.2379

17 0.0575 �0.1640

18 2.4388 �0.5402

19 �0.6249 �0.0959

20 0.2623 1.1200

482 S. Liu et al.

Table 6. Coefficients of regression factors as independent variables.

Unstandardized

coefficients

Standardized

coefficients Correlations Collinearity statistics

Model Beta

Standard

error Beta t Significance

Zero-

order Partial Part Tolerance

Variance Inflation

Factor

1 Constant 5.740 0.335 17.125 0.000

FAC2 1.742 0.344 0.767 5.064 0.000 0.767 0.767 0.767 1.000 1.000

2 Constant 5.740 0.299 19.207 0.000

FAC2 1.742 0.307 0.767 5.680 0.000 0.767 0.809 0.767 1.000 1.000

FAC1 �0.728 0.307 �0.321 �2.375 0.030 �0.321 �0.499 �0.321 1.000 1.000

Intern

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urn

al

of

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ital

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83

many different building height, which will obscure the 3D morphological character-

istics of the entire sample space. On consideration of the maximum building

occupation area, the minimum occupation area, and the majority occupation area,

the validation area was divided into grids sized 250m�250m. The extraction results

of the 3D urban morphology indicators are shown in Figures 6�12, including building

distribution density (Figure 6), building area density (Figure 7), height distribution

density (Figure 8), capacity distribution density (Figure 9), otherness (Figure 10),

evenness (Figure 11), and similarity (Figure 12) of the validation area. According toEquations (9�11), the spatial regression was computed to calculate the 3D urban

morphology’s impacts on road traffic pressure. The results are shown in Figure 13.

Then, the urban traffic pressure and conditions were calculated according to the

algorithm in section 3.5. The results are shown in Figures 14 and 15.

4.2. Showing the final results in a 3D digital earth

The simulation data of urban road conditions and the 3D building data of the urban

space were loaded into the Digital Earth Science Platform. The Digital Earth Science

Platform was developed by the Center for Earth Observation and Digital Earth,

Chinese Academy of Sciences and is mainly used to explore, present, and exchange

geoinformation. It visualized the relationship between the 3D urban morphology

and the urban road condition from a 3D perspective (Figure 16).

4.3. Accuracy analysis

The validated roads were selected randomly from Dongcheng District. The realtraffic condition level of the validated road was determined by the number of vehicles

and estimated human flow density interpreted visually from aerial remote sensing

images and high spatial resolution satellite images. The accuracy of the simulation

result was evaluated by comparing the simulated traffic conditions with interpreted

traffic conditions.

Figure 5. Schematic diagram of spatial allocation of traffic pressure to the urban road

network.

484 S. Liu et al.

Figure 6. Building distribution density. Figure 7. Building area density.

Figure 8. Height distribution density. Figure 9. Capacity distribution density.


Figure 10. Otherness. Figure 11. Evenness.

Figure 12. Similarity.

Figure 13. Impacts of 3D urban form on

road traffic pressure.

486 S. Liu et al.

Figure 17 reflects that the validated sample results are generally the same as thesimulated results, with a mean error less than 20%. The errors of sample 9 and

sample 17 are relatively large because they are near a subway entrance. The subway

diverts the ground traffic pressure, and therefore the simulated traffic pressure is

greater than the validated sample’s. This analysis also reveals that the subway plays

an obvious part in reducing ground traffic pressure.

5. Discussion and conclusion

This study has indicated that, among these 3D urban morphology features, capacity

distribution density has the most significant impacts on traffic condition. However,

building distribution density is insignificant. Moreover, capacity distribution density

and otherness are positively correlated to road traffic pressure, while building

distribution density, building area density, height distribution density, evenness, and

similarity are inversely proportional to road traffic pressure. Figure 18 depicts the

trend line of traffic pressure affected by the urban space with each morphology

indicator.The results are supported by the following reasons. Capacity distribution density

reflects the use density as well as the building capacity of planar urban space. The

urban space with high use density and strong building capacity definitely gives more

pressure to the transportation nearby. Moreover, the urban space with obvious

Figure 14. Urban traffic pressure in the

validation area.

Figure 15. Urban traffic conditions in the

validation area.


otherness varies more obviously in building height level. It is likely to be composed of

high building groups intermingled with low-rise buildings, such as commercial

districts, public service districts with social services functions, or residential districts

with dense high-rise apartment buildings. These crowded districts carry more people

Figure 16. 3D visualization of simulated traffic conditions and the spatial urban form.

Figure 17. Comparison of all 20 interpreted traffic pressure data and the traffic condition data

simulated by the model. The expected 1:1 dashed line is shown for reference.

488 S. Liu et al.

and heavier traffic flow that create much higher road traffic pressure. Therefore, the

otherness in 3D urban space is positively correlated to the traffic pressure.

The urban spaces with high building distribution density or high building area

density, in this study, are mostly residential districts with small low-rise buildings, in

which buildings are densely distributed and occupy a large amount of land. This kind

of urban space has fewer big buildings or high-rise buildings and is always used for

personal living with fewer commercial or social service functions. These small

buildings cannot carry as many people or as much traffic, so building distribution

density and building area density are inversely proportional to the road traffic

pressure. Meanwhile, this kind of urban space mostly has dense height distribution

density because the number of buildings in it is always large. Thus, height

distribution density is also inversely proportional to the road traffic pressure,

represented typically by the courtyard blocks widespread in Beijing’s Old City.

In the urban spaces with a greater evenness value, the shape of each building in a

building group is uniform with less diversity. And the urban space with big similarity

value generally has single type of building structure and also groups of clustered

buildings with similar capacity. This kind of urban space is mostly residential with

almost no high-rise buildings but buildings with similar shapes and capacities, such

as low-rise apartment buildings or houses with courtyards. Such districts perform a

single function and also have low population density, so their contribution to road

traffic pressure is less obvious. Otherwise, the urban spaces with a small evenness

value or small similarity value are mostly full of high-rise buildings with large

differences in shape or capacity, such as versatile and crowded commercial districts as

well as multi-functional blocks. Therefore, evenness and similarity of 3D urban

spaces are inversely proportional to road traffic pressure.Although single morphology density indicators or single urban structure

indicators in an urban space cannot reflect their contribution to the pressure of

traffic flow directly, a combination of these different indicators has the ability to

reflect their contribution to the pressure of traffic flow. For example, if the urban

space has small building evenness, as well as small building distribution density and

Figure 18. Schematic diagram of the linear relationship between the urban space with each

form indicator and traffic pressure.


large height distribution density, it could create relatively high pressure on

transportation. The combination of these indicators demonstrates that the urban

space should have all of the following features: more buildings, higher building

height, and an uneven distribution of building entities. This kind of urban space

probably has a certain amount of large buildings intermingled with several small

buildings. It has huge building capacity in total and also performs multiple complex

functions, so the transportation near it is heavy.

Acknowledgements

This research is supported by National Basic Research Program of China (973 Program, No.2009CB723906) and National Natural Science Foundation of China (No. 41001267). Theauthor would also like to acknowledge the anonymous reviewers helped to improve this article.

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