CSIS Discussion Paper.105

Scenarios approach on the evaluation of a sustainable urban structure for reducing
carbon dioxide emission in Seoul City

bySohee Lee a* and Tsutomu Suzuki ba*Corresponding author. Center for Spatial Information Science, University of Tokyo

5-1-5 Kashiwano-ha Kashiwa-shi Chiba, 277-8568, Japan
Email: shlee@csis.u-tokyo.ac.jpbGraduate School of Systems and Information Engineering, University of Tsukuba

1-1-1 Ten-nodai Tsukuba Ibaraki, 305-8573, Japan
Email: tsutomu@risk.tsukuba.ac.jp

Abstract
Many researchers have concerned on relationship and interaction between urban spatial
structure and transportation system, moreover, brought to advance debates onenvironmental burden. In this paper, we estimate travel time of automobile and subway
using GIS analysis, furthermore, a modal split model in terms of the modes. For using
these results, we examine the reduction effect of carbon dioxide emission by assessing
several scenarios which are about constructing subway infrastructure and reorganizing
urban spatial structure. The result indicates that the policy direction for drawing up theurban spatial structure in terms of reducing carbon dioxide emission in Seoul City. The
findings hold important implications for the policy debate surrounding sustainableurban system.

1 Introduction
The issue of climate change has become a subject of intense interest all over the world
since the last decade. The primary international policy framework against global
warming is the United Nations Framework Convention on Climate Change (UNFCCC),
specifically the Kyoto Protocol. In terms of the emphatic issue on preventing global
warming is about reducing greenhouse gases. Expressly, it is essential to reduce theemission of carbon dioxide in handling climate change issue notably, because theemission of carbon dioxide is the largest contributing gas to the greenhouse effects(Fong et al, 2009; UN, 1998). Hence, an effort for reduction is necessary to strive in thevariety fields also as a matter of urban system.
The concern of environmental impacts caused by urban activities in urban
system has been identified. Many researchers have concerned on relationship andinteraction between urban spatial structure and transportation system, moreover, brought
to advance debates on environmental burden or transportation energy efficiency. For
example, a first body of literature is concerned with the analysis of commuting distance(or time) to seeking for the relationship between urban structure and change ofcommuting behavior empirically (Aguilera, 2005; Rouwendal et al, 1994; Wachs et al,1993). A second body of literature brings out the concept of excess commuting or
wasteful commuting that represents the difference between actual and minimized
commuting when distributions of jobs and housing is given as the same as present
situation (Frost et al, 1998; Giuliano and Small, 1993; Ma and Banister, 2007;
Merriman et al, 1995; White, 1988). A third body of literature is focused onjobs-housing mismatch in urban area physically to measure job accessibility as anindicator of auto-oriented urban structure (Kawabata and Shen, 2006; Kawabata, 2009).
These studies have generated much discussion about the relationship between
jobs-housing balance and commuting behavior in various ways. Furthermore, thesestudies have tried to verify a hypothesis that a polycentric urban model could contributeto reducing commuting distance (or time) by allowing workers to locate within or close
to their workplace. As the results, commuting could not occurred efficiently all the time,
namely workers do not always make a journey to workplace just reducing theircommuting distance for some reasons. A forth body of literature is to clarify the debate
concerning about the negative environmental and energy effects caused bytransportation system and related urban density (Bertolini and Clercq, 2003; Cervero,
2001; Lee and Suzuki, 2007; Mindali et al, 2004; Newman and Kenworthy, 1989). Alesson from these studies is commonly that energy consumption or environmental
impact of urban transportation is negatively correlated with urban density. However, thisis differently adopted into spatial hierarchy of urban area for example inner area andouter area. Additionally, some of approaches are modeled to integrate the effects ofspeed, acceleration, road grade and network, and also congestion to estimate the fuelconsumption or emissions in relation to greenhouse gases (Frey H C et al, 2007;
Nejadkoorki F et al, 2008; Scott D M et al, 1997). Nevertheless, these studies are notconsistent with urban system and just focused on efficient of road networks.
Road transportation emission of carbon dioxide has received special attention,
because they have been rising constantly. Although, there are methods through which
they can be reduced, such as better transportation infrastructure, advances in vehicle
technology and management systems, stabilizing carbon dioxide emission from roadtransport is likely to be a complicated task due to the rapidly rising traffic needs.
According to the statistics of carbon dioxide emission of Korea in 2001, transportation
section has captured 20 percents of total emission (generation of electric power: 30%,
industry: 34%, domestic and commerce: 14%, and the rest). However, the annual
average of transportation section is about 7 percents (specially, generation of electric
power section is about 12.1 percents) which is compared to the emission ratio in 1990,
and it is high and relatively recorded an increase tendency to exceed an average increase
ratio (5.8%) in comparison with the other sections (KEEI, 2001).
In these perspectives, more detailed planning policy line for future urban
spatial structure with appropriate evaluation approach is necessary. However, there isone more problem that is not simple to obtain proper personal traffic information eventhese data are valuable to utilize. For that reason, the purpose of this study is stated as
two: (1) it is to suggest one of the way to estimate commuting time by modes and modal
share for all origin-destination (OD) pairs of analyzed spatial units in Seoul City whichdata has not investigated but it is a fundamental and essential, moreover, (2) it is toclarify a policy direction to head for planning and organizing urban spatial structurewith a little environmental load for the future in Seoul City.
This paper is organized as follows. The section 2 describes study area and data
set. Then, the section 3 represents the estimation of commuting time and modal shareby automobile and subway. Furthermore, the section 4 explains how to set the several
scenarios, and its results are summarized with discussions. The conclusion includes thefurther works in the section 5.

2 Study area and data set
The chosen study area for this study is Seoul City, Korea. The geographic unit applied isthe smallest of administrative unit, named dong. Seoul City is divided into 522 dongs.
The land area in Seoul City covers 605 square kilometer. In 2005 Seoul City
accommodated 9,820 thousands peoples and that is occupied about a one-fifth of Korea,
respectively, the number of workers is about 4,003 thousands (SK, 2005).
In this study, commuting (not to the other objective trips) is considered to
examine the reduction of carbon dioxide emission by reorganizing residence and work
places. Because, commuting to work is an important reason for travel in daily life, andalso it occupied a big part of all objective trips. The number of worker (commuter)among origin-to-destination (OD) is available from the 2002 Seoul Metropolitan Region

Person Trip Survey (PT Survey) conducted by the Seoul Development Institute (SDI,2002).
As we mentioned, origin-to-destination (OD) travel time is available, but it issample-based, thus travel time for a complete set of OD pairs is generally not available.
Researchers often estimate travel time in various ways when such data are needed forcertain modes (Kawabata and Takahashi, 2005). In this study, as following Kawabata
and Takahashi’s (2005) approach, we use Geographic Information System (GIS) toestimate the travel time and distance for journeys undertaken by automobile and subway.
Many commuters also take the bus in Seoul City; however, the routes of bus lines in
Seoul City and operation times are difficult to analyze because of a lack of data.
Therefore, only automobile and subway are considered as modes. One problem is that
the PT survey is 2 percents sample-based, and about 30 percents of sample is opened tothe public. So that travel times by automobile and subway from the PT survey in 1996
and 2002 is combined to evaluate travel times by automobile and subway. Specifically,
how to estimate travel time and distance illustrates by section 3.
To evaluate travel time and distance, the spatial data that the location of 522
dong offices (point), principle road lines, subway lines and stations are set. The point ofdong offices is extracted from the numerical map of Seoul City conducted by the
National Geographic Information Institute (NGII, 2000). Moreover, the principle roadlines and subway lines and stations are offered by the Seoul Development Institute(SDI). Then, attribute information of subway lines and stations includes waiting time fortrain, transfer time and movement time between stations that is based on the service
schedule of Seoul Metro and Seoul Metropolitan Rapid Transit Corporation are
constructed (SM, 2002; SMRTC, 2002).
To measure the emission of carbon dioxide by riding automobile and subway
among ODs, a unit which is defined as an amount of emission when one person moves
1km according to different transportation mode is adopted. In this study, a unit ofcarbon dioxide emission that we apply for evaluating the reduction of effect is 150.7
g-CO2 per person by 1km for automobile, and 17.9 g-CO2 per person by 1km for
subway, and these figures is referred a report that is issued to the Korea Transport
Institute (Lee et al., 2005).

3 Estimation of commuting times and modal share by automobile and subway
The personal traffic information is utilized as an important data to draw up the future
traffic demand estimation or prediction. It is also important for implementation thetransportation plan and policy. There are two surveys in Korea to investigate thepersonal traffic information that are named the Population and Housing Census and the
Person Trip Survey. However, in the Census data, spatial unit is large and items ofquestionnaire are not delicate. So, bias is possibly affected to the result of any analysis.
In addition, the Person Trip survey is preformed as a sample for population of 2
percents. That is the reason of commuting times by all modes are not available for all
the OD pairs, and also highly precise surveyed data is not offered. So that we estimate
OD commuting times for trips between 522 dong offices. Commuting times byautomobile and subway for all OD pairs (272,484= 522×522 pairs) are modeled using
GIS (Kawabata and Takahashi, 2004, 2005). Moreover, we propose a modal split modelby applying a binary logit model to estimate modal share for all OD pairs.

3.1 Estimating OD commuting times by using GIS
As it was mentioned, the PT Survey is population of 2 percents sample-based, so it is
not able to obtain a reasonable amount of commuting time data for enough OD pairs. To
generate a reliable data set, commuting times of automobile and subway which are
undertaken in 1996 and 2002 is pooled to estimate commuting times for all OD pairs.
We take the following steps in estimating the commuting time of automobile, and all
automobile users only follow the principle road lines will be assumed. As the first step,
the direct distance between each dong office and its point of dong office from thenearest main road are calculated. Secondly, the shortest route by travel distance among
the principle road lines is calculated. Thirdly, total distance among origin-to-destination
is summed up the direct distance for office to the point of main road, the shortest route
along the main road, and the point of main road to office.
We do not consider congestion-related delays because of a lack of data. We
could have estimated the effect of traffic congestion by investigating the average speed;
however, a limitation exists in that we were unable to consider the differences between
central areas and local areas with serious traffic congestion and those areas with little
congestion. Thus, we assume no traffic congestion. If the locations of the analyzed
zones are close to each other, this assumption will lead to bias in the case of serious
traffic congestion among zones; nevertheless, if the zones are located far from eachother, the bias will be minimal because the effects of congestion are offset by parts ofroute with little congestion.
Next, we perform a regression analysis in which GIS-calculated OD
commuting distance is adjusted using the actual commuting times reported in the PT
Survey (using pooled data from 1996 and 2002). To convert the distance traveled byautomobile to a commuting time, we use the following regression:− Doffice
TijPTAuto = 0.016 DiOoffice − MainRoad + 0.003DijNetwork + 0.039 D MainRoad+ 9.902 (1)j(2.106)(2.638)(20.920)(7.744)

where TijPTAuto is the travel time of automobile reported in the PT Survey from zone i tozone j, DiOoffice − MainRoad is the direct distance from origin office to the nearest main roads− Dofficein zone i, and DijNetwork is the shortest route from zone i to zone j, and D MainRoadisjthe direct distance from the destination office to the nearest main roads in zone j.
Adjusted R-square value: 0.552 (The numbers in parentheses indicate t values)

Table 1. Descriptive statistics of the adjusted OD commuting time of automobile
Year
2002

Variables
GIS-calculated OD travel time
PT Survey OD travel time# ofobservations
272,484
14,24914,249Std.
Dev.
50.79 21.70
39.12 18.14
43.42 23.22

Mean

Min.

Max.

0.00 139.85
4.05 131.70
0.00 420.00

The commuting times for all OD pairs were therefore adjusted and the relevant
descriptive statistics are shown in Table 1. The mean commuting time in the PT Survey
is considerably longer than that calculated by GIS; this is because we did not consider
congestion-related delays in calculating the OD travel time. The GIS-calculated OD
commuting time is therefore reasonably reliable in explaining the real world, eventhough the R2 value is a poor fit.
For subway users, three conditions are assumed. First, all subway users are
assumed to walk to the stations. Second, any station within 1 kilometer radius of any
dong office could be chosen to determine the shortest route to the destination because
we assumed only within 1 kilometer (it is about 15 minutes on foot) could be reachedon foot. Finally third, if there is no station is located within 1 kilometer of dong office,the nearest station is selected. Then, we take the following steps in estimating thecommuting time of subway. First, the direct distance between dong offices and stations
is calculated and it is converted to time using a walking speed of 4 kilometers per hour.
Second, the shortest route among stations is calculated which is considered with not
only travel time among stations, and also transfer time and waiting time for train among
stations.
Next, we perform a regression analysis in which the GIS-calculated OD
commuting times are adjusted using the actual travel times reported in the PT Survey.
To compute the commuting time by subway, we estimate the following regression:
TijPTSub = 0.708TijGISSub + 12.943(15.242)(2)(9.061)

where TijPTSub is the travel time by subway reported in the PT Survey from zone i tozone j, and TijGISSub is the shortest route time for zone i to zone j calculated using GIS.
Adjusted R-square value: 0.542 (The numbers in parentheses indicate t values)
The commuting times for all OD pairs are adjusted, and result of descriptive
statistics is shown in Table 2. The mean of GIS-calculated OD commuting time isdecreased. It means that newly constructed subway line contributed to decreasing
commuting time.

Table 2. Descriptive statistics of the adjusted OD commuting time of subway
Year
2002

Variables
GIS-calculated OD travel time
PT Survey OD travel time# ofobservations
272,484
18,05818,058

Std.
Dev.
55.00 15.12
44.99 12.26
45.85 22.29

Mean

Min.

Max.

13.40 118.83
17.61 102.41
0.00 660.00

3.2 Modal split model
A modal split model by applying a binary logit model is purposed to evaluate modal
share. In doing so, we use the commuting times of automobile and subway, as well as
modal share determined from the PT survey undertaken in 2002. And also, the total
number of trips by all modes which is estimated by SDI based on sample-based PT
survey data. Moreover, two assumptions are made; first, only automobile or subway
could be chosen when workers make a journey to their work place, and second, allworkers choose the route to minimize the total commuting time.
The equation of modal split model is as follows:

Pauto =

1(3)

1 + exp{μ (Tauto / Tsub ) + C}

where Pauto is ratio of sharing automobile, then, Tauto is commuting time by automobile
and Tsub is commuting time by subway.
We select OD pairs from the sample-based PT Survey undertaken in 2002
which are satisfied two conditions; first, the number of workers from origin todestination could be more than 5, and second, the ratio of modal share for automobile
and subway could be potentially more than 50 percents. Figure 1 shows the proportionof commuters traveling by automobile plotted against the ratio of automobile to subway
travel time, along with estimated curves. We then undertake a logistic regression
analysis to estimate the parameters α and β . The form of logistic regression
equation is as follows:

Pauto =

1
1 + exp{−(α + βx)}(4)

where α is the constant of the equation and β is the coefficient of the predictor
variable. In comparing equations (3) and (4), α corresponds to C, and βcorresponds to μ . We perform a logistic regression using maximum likelihood
estimation (MLE), in which the dependent variable is the modal share of automobile,and the independent variable is the ratio of travel times by automobile and subway(Table 3).
Table 3. Estimation of the values of α and β
Year

α

β

2002

2.70525-3.01280

Ratio of automobile share (ratio)

1

0.8

0.6

0.4

0.2

0
0

0.5
1
Observed value in 2002 (n=606)

1.5

2
2.5
Estimated value in 2002

Ratio of time by automobile and subway use (ratio)

Figure 1. Descriptive statistics of the adjusted OD travel time of subway

4 Scenarios set and its results
4.1 How to evaluate the reduction ratio of carbon dioxide emission of each scenario
Figure 2 shows the process that how we evaluate the reduction ratio of carbon dioxide
emission. First of all, we set up two parts of case: 1) in the first case, it is evaluatedwhen infrastructure of public transportation is only invested within the same jobs andhousing distribution as the present; 2) in the second case, how much carbon dioxide
emission could be reduced by how to reorganize the distribution of residence and work
places is evaluated.

calculation

outputScenarios set

Total CO2 emission

The flow
volumeEntropy Maximization Model(Objective function: entropy)

Investing in infrastructure
input
Parameter : Total travel timeof public transportation
repeat if not
input
Reorganizing
Commuting Minimization Problem
Minimum
Urban spatial structure(Objective function: time)
travel time
outputIf calculated excess is same as the present, complete the process
If not, set total travel time and calculate again

calculation

ExcessExcess ofthe present
comparison

Figure 2. Flow chart of evaluation scenario approach

For the first case, total carbon dioxide emission is simply calculated to apply
commuting time of subway users which is newly estimated in the set period of 2010 and2020 as the same way that is explained in section 3. However, we assumed that modal
share of automobile and subway is the same as in 2002 for all OD pairs. Minutely,
further explanation will be followed in section 4.2 (scenarios set). For the second case,
we evaluate policy direction for making effective and strong urban spatial structure(distribution of residence and work places) in order to achieving lighten environmental
burden. Total number of workers is not changed, however changing urban spatial

structure, namely reorganized number of workers in the place of residence and work,
would bring on different pattern of commuting behavior. That is why we adopt the
Entropy Maximization Model (EMM) to estimate the number of workers among all OD
pairs. The EMM is one of the spatial interaction models, and it has been used as thefoundation of model development in transportation since Wilson’s paper (for example,
Kapur, 1982; Leung and Yan, 1997; Wang and Holguin-Veras, 2009; Webber, 1976). In
this study, we adopt Wilson’s principle (Wilson, 1967), and the formulation is asfollows:m n

Max S = − ∑ ∑ Q ij ln Q ij
i =1 j =1
{Q }
ij

(5)

n

s.t. ∑ Qij = H i

(6)

j =1

m

∑ Q ij = W j

i =1

(7)

m n

∑ ∑ T ij Qij ≤ T(8)

Q ij ≥ 0(9)i =1 j =1

where i and j are spatial units of residence and work places, Qij is the number ofcommuting trips between i and j, Tij is the commuting time from origin i to destination
j, T is the pre-specified total expenditure (in this study, it is commuting time), then, H iis the total out-flow (newly reorganized total number of housing) at i, W j is the total

in-flow (newly reorganized total number of jobs) at j.
The problem of determination of number of trips ( Qij ) is how to find thatfeasible state which would actually occur. The total number of ways of generating thefeasible state (probability) is given by

S (Qij ) = m

Q!
n∏∏ Q !

(10)

ij

i =1

j =1

The entropy maximization principle is to find all the feasible states which
satisfy all the constraints. Namely, the optimal solution of this model gives the mostprobable states distribution corresponding to the largest number of possible statesassignments. Specifically, the most probable flow of OD pairs is obtained. The
formulation above is composed of one objective function and four constraints.
Statement (5) indicates that the objective is to find the most likely ways to distributecommuting flows. Equation (6) and (7) ensure that there are no residence supply andjobs demand in each zone. In other words, the number of workers in the place ofresidence and work would be same as each scenario that we set. Equation (8) is the total
commuting time constraint. Equation (9) is the nonnegative constraints which mean the
resulting commuting flows should be equal or greater than zero.
The problem in this model is that the total commuting time is needed as a given
factor. One of the ways simply takes the value of observation. However it is unknown
because changing urban spatial structure also induces the different commuting behavior.
In addition, the total time is effected by the variation of spatial distribution, for example,
the jobs and housing distribution would be dispersed if the total cost is set bigger thanthe present cost that we do not know. Therefore, how to predict the total commuting
time is a key point to solve the problem, so that we borrow the concept of excess
commuting.
The excess commuting is concerned with the difference between the observed
amount of commuting and a theoretical minimum amount of commuting time (ordistance). A theoretical minimum is measured by the standard linear programming oftransportation problem is defined as the Commuting Minimization Model (CMM)
which is proposed by White (White, 1988), and the formulation is as follows:m n

Min T = ∑ ∑ Q ij T ij
i =1 j =1
{Q }
ij

(11)

n

s.t. ∑ Qij = H i

(12)

j =1

m

∑ Q ij = W j(13)

Q ij ≥ 0

(14)

i =1

where i and j are spatial units of residence and work place, Qij is the number ofcommuting trips between i and j, Tij is the commuting time from origin i to destinationj, then, H i is the total out-flow at i , W j is the total in-flow at j.
The formulation above is composed of one objective function and fourconstraints. Statement (11) indicates that the objective is to find the distribution ofcommuting flows in order to minimizing the total commuting time. Equation (12) and(13) ensure that no residence supply and jobs demand in each zone, namely it is thesame as the present. Equation (14) restricts the decision variables to nonnegative values,
so found commuting flows should be equal or greater than zero.
In the both of models (EMM and CMM), the commuting time between origin
and destination is calculated by following equation:
T ij = T ij

auto

auto

M ij

+ T ijsub M ijsub(15)

where Tijauto is the commuting time of automobile between i and j, Tijsub is thecommuting time of subway between i and j, then, M ijauto is the modal share ofautomobile between i and j, M ijsub is the modal share of subway between i and j. The
way of evaluating the commuting times and modal share of automobile and subway ismentioned in section 3.
Then, the excess commuting is calculated as a proportion of the average actual
commuting as follows:

Excess =T actual − T min.
T actual(16)

where T actual is the observed average commuting time, and T min. is theoreticalminimum average commuting time. Table 4 presents the total and average commuting
time and the value of excess in Seoul City.

Table 4. Total and average commuting time and excess in Seoul City
# of workers

Total time (minute)
Minimum

Observed

Average time (minute)
Minimum

Observed

ExcessSeoul City
3,213,878
68,239,637
118,245,100
21.23336.7920.42290
Note: the number of workers is sourced from the 2002 Seoul Metropolitan Region Person Trip Survey(PT Survey) conducted by the Seoul Development Institute.

The definition of excess commuting is the additional journey to work travel
represented by the difference between actual average commute and the smallest possible
average commute that is given the spatial configuration of workplace and residentialsites (for example, Frost et al, 1998; Giuliano and Small, 1993; Ma and Banister, 2007;
Merriman et al, 1995; White, 1988). As following studies, excess commuting is wastefulin the sense that all this extra commuting could be eliminated by inducing people toexchange either jobs or houses until all commute-reducing swaps have been carried out.
The implication in the research brings up to date is the enormous potential savings incommuting can be derived from trading residences and workplaces, in fact,
restructuring the physical plans of the cities. However, commuting is bound tightly withurban spatial structure in the sense that excess commuting is caused by multiple factors,
including a imbalance of location of jobs and housing. That means if we assume theonly commuting time is a factor to make a decision where from and to commuters aremaking journeys to work, excess would be closed to 0 or 1, however it could not be
possibly realized in the actual city.

300,000,000)et 250,000,000
u
n
i
m
(
e 200,000,000
imt
g 150,000,000nitum100,000,000moclato 50,000,000
T

Maximized total commuting time:
263,390,803 minutes

Total commuting time:
118,245,100 minutes

Excess: 0.42290

Excess: 0.74092

0
0.0

0.2

0.4

0.6

0.8

1.0

ExcessFigure 3. Relationship between total commuting time and excess

Figure 3 presents the relationship between total commuting time and excess. As
it shows, when the total commuting time becomes large, then the value of excess also
becomes large within the same jobs and housing distribution because commuter makes along journey. So we assume that the value of excess commuting would be calculated
about the same or less comparing to the current value even if the urban spatial structureis changed, and the factors that affect to the commuting patterns are not changed or
fixed as same as the present situation. Consequently, excess commuting would bedecreased if a strategy tends to promote jobs-housing balance. That is the reason that thepresent value of excess is assumed as an evaluation standard even though the urban
spatial structure is reorganized. In each scenario, the total commuting time constraint in
the Entropy Maximization Model is determined by comparing the value of excess
whether it is the same as the present value (=0.42290) or not. If it is not the same, thetotal commuting time is set and calculate again.
After that, the total carbon dioxide emission is calculated by the number of jobs
and housing among OD pairs which is estimated by the Entropy Maximization Model.
Then, the total carbon dioxide emission is compared to evaluate the effect ofreorganizing urban spatial structure which one is more efficient to get rids ofenvironmental burden.

4.2 Scenarios set
Table 5 shows the description and condition of a moving worker for each scenario that
we set. Scenarios are divided into two parts greatly. One part is to improve
infrastructure of public transportation without reorganizing urban spatial structure (SA).
In this study, we only consider subway and LRT as the public transportation. Other partis only to reorganize urban spatial structure with a current transportation condition (SB,SC, SD, SE).

Table 5. Description of scenarios and condition of a moving worker
Description
Investing on
infrastructure

SA1
SA2
SB1

SB2

SB3

SC1

Reorganizing
urban
spatialstructure

SC2

SD1

SD2

SE1

SE2

SE3

Time period in 2010 for investing on subway lines
Time period in 2020 for investing on subway lines
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c

Concentration on urban center

Concentration on urban and suburban centers

Concentration on urban, suburban and local centers

Promotion of residence area in urban center
Promotion of residence area in urban and suburban
centers
Promotion of developing area in near of stationconsidering with jobs-housing balance(Distance to station is within 500m)
Promotion of developing area in near of stationconsidering with jobs-housing balance(Distance to station is within 1,000m)
Concentration on the number of jobs and residence inareas where is both urban and suburban centers, inaddition, near subway station (within 500m)
Concentration on the number of jobs and residence inareas where is both urban, suburban and local centers, inaddition, near subway station (within 500m)
Concentration on the number of jobs and residence inareas where is near subway station (within 500m)

The conditionof a moving
worker (%)
0%
0%
10%
15%
30%
5%
15%
30%
10%
20%
30%
10%
20%
30%
10%
20%
30%
10%
20%
30%
10%
20%
30%
5%
10%
15%
5%
10%
15%
5%
10%
15%

For scenario A, we name it as SA, is the case for investing in construction of
public transportation infrastructure without changing urban spatial structure. It evaluates
the reduction effect of carbon dioxide emission by only maintenance and construction of
the public transportation system. SA is set for the time periods in 2010 and 2020 (SA1and SA2) as the standard criterion when the public transportation infrastructure invested.
Table 6 shows the network of subway and LRT, and its planned service year in Seoul
City, and figure 4 presents its spatial distribution (TSC, 2007). Travel time and modal
share of automobile and subway are estimated by using GIS network analysis. The
process of estimation is the same as in section 3.
For scenarios B, C, D, and E, we name it as SB, SC, SD, and SE, are the casefor reorganizing urban spatial structure only with a current public transportation

condition. Mainly four cases are set up: (1) concentration / dispersion of jobs on urban,
suburban or local centers (SB), (2) promotion of residence area in urban and suburban
centers (SC), (3) promotion of developing area in near of station considering withjobs-housing balance (SD), (4) concentration on the number of jobs and residence inareas where is both urban and suburban centers, and also where is near subway station(SE).

Table 6. The plan of constructing subway lines and LRT in Seoul City
Planned opening year

Name of line

2003

Bundang line

2006

Extended line No. 1

2008

Extended Bundang line

2009

Extended line No. 3

2011

Extended line No. 9

2011

New Bundang line
Dongbuk line
Mokdong line

2017

Light Rail Transit(LRT)

Myumok line
Seobu line
Shinrim line
Wooi line

Note: Airport railway between Kimpo airport and Seoul station that is planned to operate in 2011 is
excluded from the analysis.

We consider reorganization of urban spatial structure with concentration or
decentralization of workers in that area of urban, suburban, or local centers. The
location of those areas is specified as the fundamental idea in 2020 Seoul City
Comprehensive Plan, it shows in figure 5 (SC, 1998). In terms of comprehensive plan,urban spatial structure in Seoul City is intended to be polycentric-dispersion spatial
structure, and also reinforce with a center for each sphere of life (namely local center).
By aiming to such spatial structure, first, commuting will disperse to the suburban
and/or local center for good side of effect. Second, by strengthening functions in a
sphere of life and well equipping public transportation, proximity of jobs and housing
could be induced. It would be directly connected to save travel time or distance. Farther,
it induces to reduce transportation energy and carbon dioxide emission. Nevertheless,the effect of modified urban spatial structure is not quite evaluated quantitatively
somehow, moreover, it is required.

Boundary of 25-ward area
Stations by 2002
Lines by 2002
Stations newly constructed by 2020
Lines newly constructed by 2020

±
0 1 2

4

6

8

km
10

Figure 4. The network of subway and LRT in 2002 and the future

Boundary of 25-ward area
Boundary of smallest administrative unit (Dong)
Urban center
5-suburban center

Wangsimni

15-local center

SangamYeongdeungpo

Yongsan±Yeongdong

km
0 1 2

4

6

8

10

Figure 5. The division of spatial hierarchy by 2020 Seoul City Comprehensive Plan

Table 7. The results of evaluating scenarios
Reducedratio of CO2(%)

Results
Present state
Scenarios set
SA1
Investing on
infrastructure
SA2
SB1

SB2

SB3

SC1

Reorganizing
urban
spatialstructure

SC2

SD1

SD2

SE1

SE2

SE3

-

a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c
a
b
c

-4.5
-6.0
0.3
1.2
2.6
0.7
4.5
9.2
4.8
9.8
14.9
-0.3
-0.5
-0.8
-1.0
-2.0
-3.0
-2.3
-11.7
-13.4
-9.8
-11.2
-12.2
-2.1
-4.1
-6.1
-2.3
-4.5
-6.7
-0.5
-0.9
-1.1

Required
CO2 emission(g-CO2/person)

Requiredcommuting time(min/person)

Requiredcommuting dist.
(km/person)

470.13

36.79

8.442

449.07441.85473.11477.17483.88474.83492.69515.14494.21517.72541.75470.32469.10467.88466.76461.97457.21460.91416.44408.30425.53418.85414.14461.88452.32442.90460.81450.22439.80469.21467.44466.45

37.86
35.68
36.87
37.07
37.41
36.95
37.84
38.96
37.92
39.09
40.30
36.73
36.66
36.60
36.55
36.31
36.06
36.27
34.19
33.79
34.62
34.28
34.04
36.31
35.83
35.35
36.30
35.80
35.32
36.70
36.63
36.61

7.590
7.122
8.280
8.370
8.523
8.313
8.708
9.210
8.754
9.295
9.863
8.216
8.189
8.163
8.140
8.038
7.937
8.034
7.116
6.969
7.299
7.198
7.142
8.074
7.908
7.744
8.069
7.899
7.733
8.274
8.323
8.396

Ratio ofreorganized workers (%)
residencenot changed

workplacenot changed

not changed
not changed

not changed
not changed
1.66
2.39
4.79
0.73
4.18
8.36
5.42
10.84
16.26
0.00
0.00
0.00
0.00
0.00
0.00
1.72
3.44
5.16
3.52
7.03
10.55
5.00
10.00
15.00
5.00
10.00
15.00
5.00
10.00
15.00

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.20
0.39
0.59
0.79
1.58
2.37
1.26
2.52
3.78
2.08
4.15
6.23
5.00
10.00
15.00
5.00
10.00
15.00
5.00
10.00
15.00

Ratio ofmodal share (%)
auto
40.66

subway
59.34

40.66
40.66
39.64
39.55
39.36
39.65
39.28
38.79
39.20
38.61
37.95
39.73
39.76
39.78
39.78
39.85
39.93
39.77
40.32
40.32
40.27
40.22
40.11
39.59
39.50
39.43
39.55
39.38
39.22
39.30
38.86
38.37

59.34
59.34
60.36
60.45
60.64
60.35
60.72
61.21
60.80
61.39
62.05
60.27
60.24
60.22
60.22
60.15
60.07
60.23
59.68
59.68
59.73
59.78
59.89
60.41
60.50
60.57
60.45
60.62
60.78
60.70
61.14
61.63

The detail explanation of each scenario is as follows. First, the reduction ofcarbon dioxide emission is assessed in the case of concentration or dispersion type ofcommercial and business functions (SB). Some portion of jobs is moved to urban,
suburban and/or local centers. The number of jobs is mainly concentrated in urban
center (SB1), and urban and suburban centers are reinforced by moving the number ofjobs (SB2). Then, we consider local centers of sphere as well (SB3). Second, promotionof residence area in urban or suburban centers is evaluated (SC). The number ofresidence is promoted to develop in urban center (SC1), moreover, movement ofresidence occurs not only urban center but suburban centers (SC2). Third case that area
where the use of subway is convenient is promoted to develop with consideringjobs-housing balance is assessed (SD). Administrative zones (dongs) within theboundary of 500m from subway station are targeted to adjust jobs-housing balance(SD1). Moreover, the area within the boundary of 1,000m from subway station is also
concerned to develop lively considering with jobs-housing balance (SD2). Fourth casethat the both of jobs and housing are moved to zones where is in near of subway station
and also the centers is estimated (SE). In detail, urban and suburban centers are focusedon developing as concentration area with sufficient condition of the boundary of 500m
from the station (SE1). Then, urban, suburban and local centers of sphere are focused(SE2). Furthermore, zones where only are within 500m to station are considered (SE3).
The percentage of moved jobs and housing for each scenario is presented in table 5.

4.2 Findings and discussions
First, we present results for the reduction ratio of carbon dioxide emission and ratio of
reorganized workers by the place of work and residence. From this results, we could
evaluate how much of total carbon dioxide emission could be reduced by reorganizing
urban spatial structure. Next, we present results for the change in spatial distribution ofwork and residence places. From this results, we could find out the policy direction that
how to reorganize urban spatial structure to cut down the environmental burden in the
future. Table 7 shows the evaluation result by each scenario. In this study, we evaluateeach scenario as comparing with the reduced ratio of carbon dioxide emission and ratioof moved number of jobs and residence.
In the result of SA, about 4.5% in 2010 year (SA1) and 6.0% in 2020 year(SA2) of carbon dioxide emission compare to the present commuting behavior could be
reduced even we do not reorganize urban spatial structure by newly equipping
infrastructure of public transportation network. In other words, as the current urban
spatial structure, some of carbon dioxide reduction is expected. We assume that the ratioof modal share is same as in 2002, even though the network is newly built.
Consequently, the reduction ratio of carbon dioxide emission could be much larger than
the result.
In the result of SB which is focused on concentration or dispersion type ofbusiness function, the reduction ratio of carbon dioxide emission is calculated greaterthan the present condition. Moreover, the reduction ratio presents higher percentage
when reorganized number of workers became big. The policy to make the strong urban
and suburban centers is potentially to bring out the long journey from work to residence
place, thus it makes the ratio of carbon dioxide emission is increased. In other words,
depending on the commuting behavior could be occurred much worse conversely.

In the result of SC which is focused on promotion of residence area in urban orsuburban centers, the reduction effect of carbon dioxide emission was presented. As theresult of SC2a shows 3% of total carbon dioxide emission decreased when about 2.4 %of workers in the place of residence in urban and suburban centers is reorganized.
Indeed, the reduction ratio of carbon dioxide emission seems to grow big so that the
number of workers in the place of residence becomes large portion. In other words, as
for the policy of the residence promotion in urban center and suburban centers, thereduction effect was provided by shortening commuting to the work place. It could be
proved to compare the change of commuting distance which was about 8.44km in thepresent condition of 2002, and it becomes shorter in this scenario.
The largest reduction of carbon dioxide emission makes an appearance in the
result of SD which is focused on promoting the area in near subway station withconsidering jobs-housing balance. As a result, SD1c shows about 13.4% (the number ofworkers in the place of jobs and residence was about 3.78% and 5.16) and SD2c appears
about 12.2 % (the number of workers in the place of jobs and residence was about
6.23% and 10.55%) of the reduction ratio of carbon dioxide emission. It explains thatthe policy of promoting the area where is much convenient to access subway station(such as Transit-Oriented Development (TOD)) shows good side of effect to reduce
environment impacts. Even though only small portion of number of workers in the placeof residence and workplace is reorganized it provides the big portion of reduction.
Moreover, larger amount of reduction was gained when the area within the boundary of
500m to subway station was intensively considered. It explains that selected area whereis especially good accessibility is desirable to intensively develop as a foothold area.
In the result of SE which is focused on concentration on the number of jobs andresidence in areas where both urban center and near subway stations are, the reduction
effect of carbon dioxide emission was presented. From such a result, the policy of
promoting the areas in near of subway station and in where is urban center or suburban
centers have been also effective to reduce the emission of carbon dioxide. However,comparing the results of SD and SE, such areas are better to establish the plan for mixed
land use while considering jobs-housing balance, not only as the place of residence oreven work place.

Density of jobs
(jobs / sp. km)
0.0 - 40.0
40.1 - 80.0

Urban Center

80.1 - 120.0
120.1 - 160.0
160.1 - 200.0
over 200.0(max. 903.3)±

Yeongdeungpo

kmYeongdong
0 1 2

4

6

8

10

Density of workers(workers / sp. km)
0.0 - 40.0
40.1 - 80.0
80.1 - 120.0
120.1 - 160.0
160.1 - 200.0
over 200.0(max. 220.5)

±
km
0 1 2

4

6

8

Figure 6. The density of jobs and workers in Seoul City

10

Change of jobs (%)(SD1-c)Subway lines in 2002
Stations
below 0.00 (min. -10.15)
0.00
over 0.00 (max. 30.04)

±
km
0 1 2

4

6

8

10

Change of workers (%)(SD1-c)Subway lines in 2002
Stations
below 0.00 (min. -6.66)
0.00
over 0.00 (max. 30.02)

±
km
0 1 2

4

6

8

10

Figure 7. Change of jobs and workers distribution by SD1-c

Next, we present the density distribution of jobs and workers in the place ofwork and residence places in Seoul City, and also the change in spatial distribution of
moved jobs and workers that is the result of SD1-c showed the largest reduction ratio ofcarbon dioxide emission from the set scenarios. To present these maps typically, we
could find out how to distribute spatially jobs and workers to achieve the reduction ofcarbon dioxide emission. Figure 6 shows the density of jobs and workers. As it shows
clearly, Seoul City is distinctive polycentric urban structure. Jobs are concentrated on
the urban center that is the central part of Seoul City and two suburban centers which
are Yeongdeungpo and Yeongdong, however, workers are decentralized. This means thatsometimes the workers make a long journey to work because jobs and housing is notbalanced. That is why scenario that the promotion of residence area in urban center or
urban and suburban center represented a small portion of carbon dioxide reduction
caused by improving jobs-housing balance in urban and suburban center (SC). Figure 7
presents the change of moved jobs and workers in SD1-c. In this scenario, the reduction
ratio of carbon dioxide emission was 13.4 %. A few tendencies show how to rearrange
jobs and workers distribution reduce carbon dioxide emission could be found. First, it isbetter to decentralize the workers in place of workplace where are more handy to use
the public transportation. In other words, promotion of local centers is induced todisperse the commuting and it caused to reduce their travel cost. Moreover, it possibly
makes their journey short (the top side of map in figure 7). Second, it is important tosecure the place of residence in that area where showed the high jobs density (urban andsuburban center). Nevertheless, the area with good accessibility to station is needed topromote as the residential area because it makes easy to commute (the bottom side of
map in figure 7).

5 Conclusion
In this paper, we estimated travel time of automobile and subway by using GIS analysis,
moreover, a modal split model in terms of the modes. For using these results, we
examined the reduction effect of carbon dioxide emission by assessing several scenarios
which are about constructing subway infrastructure and reorganizing urban spatial
structure.
From the results, we could find the meaningful policy direction for drawing up
urban spatial structure in terms of reducing carbon dioxide emission. First, long distancecommuting could be reduced by promoting residential density where employment
density is high. Second, the area where are accessibility to the station is good enoughand especially modal share of subway is high as needed to develop mainly, it is needed
to pick up the foothold area to promote chiefly where is convenient to access the
subway station. In addition, jobs-housing balance of employment and residence isneeded to be considered compositely.
Further research remains to be carried out. First, set scenarios in this study
were quite simple based, because we tried to bring the direction of policy forth that how
we need to draw up urban spatial structure for the future. Moreover, estimation for
quantitative value of development in Seoul City is not simple. We need to consider ofthat with micro analysis. Second, the emission of carbon dioxide was considered as cost
only. However, construction cost of subway infrastructure or cost for changing urban
spatial structure are also important to be considered. All scenarios are needed to beassessed by reducing the total cost to compare the efficiency. In addition, the value fortrade-off between cost and construction time are needed to analyze. Third, for the sakeof simplicity only automobile and subway were considered. However, according toattain a more realistic analysis, it is necessary to consider other modes of transportation,
especially given the high ratio of bus use in Seoul City.
Acknowledgements
Reference
Aguilera A, 2005, “Growth in commuting distances in French polycentric metropolitan
areas: Paris, Lyon and Marseille” Urban Studies 42(9) 1537-1547
Bertolini L, Clercq F, 2003, “Urban development without more mobility by car: lessons
from Amsterdam, a multimodal urban region” Environment and Planning A 35
575-589
Cervero R, 2001, “Efficient urbanisation: economic performance and the shape of themetropolis” Urban Studies 38(10) 1651-1671
Fong W, Matsumoto H, Lun Y, 2009, “Application of system dynamics model asdecision making tool in urban planning process toward stabilizing carbon dioxide
emissions from cities” Building and Environment 44 1528-1537
Frey H C, Rouphail N M, Zhai H, Farias T L, Goncalves G A, 2007, “Comparing
real-world fule consumption for diesel- and hydrogen-fueled transit buses andimplication for emissions” Transportation Research D 12 281-291
Frost M, Linneker B, 1998, Excess or wasteful commuting in a selection of British
cities” Transportation Research A 32(7) 529-538
Giuliano G, Small K, 1993, “Is the journey to work explained by urban structure?”
Urban Studies 30(9) 1485-1500
Kapur J N, 1982, “Entropy maximisation models in regional and urban planning”
International Journal of Mathematical Education in Science and Technology 13(6)693-714
Kawabata M, Takahashi A, 2005, “Spatial dimensions of job accessibility bycommuting time and mode in the Tokyo Metropolitan Area” Theory andapplications of GIS 13(2) 139-148
Kawabata M, Shen Q, 2006, “Job accessibility as an indicator of auto-oriented urban
structure: a comparison of Boston and Los Angeles with Tokyo” Environment andPlanning B 33 115-130
Kawabata M, 2009, “Spatiotemporal dimensions of modal accessibility disparity in
Boston and San Francisco” Environment and Planning A 41 183-198
KEEI, 2001, “Statistical data for greenhouse gases emission in Korea” Korea Energy
Economics Institute, http://www.keei.re.kr/main.nsf/index.html
Lee S, Kwon T, Kwon H, 2005, “The effect analysis on greenhouse gas reduction policyin the transportation sector to prepare for climate change agreement” a report of the
Korea Transport Institute (in Korean)
Lee S, Suzuki T, 2007, “GIS-based analysis of advantageous zones for transit-oriented
development in Seoul” Proceedings of the International Symposium on Urban Planning2007 (ISUP2007), 287-296
Leung Y, Yan J, 1997, “A note on the fluctuation of flows under the entropy principle”
Transportation Research B 31(5) 417-423

Ma, K and Banister D, 2007, “Urban spatial change and excess commuting”
Environment and planning A 39 630-646
Merriman D, Ohkawara T, Suzuki T, 1995, “Excess commuting in the Tokyo
metropolitan area: measurement and policy simulations” Urban Studies 32(1)69-85
Mindali O, Raveh A, Salomon I, 2004, “Urban density and energy consumption: a newlook at old statistics” Transportation Research A 38 143-162
Nejadkoorki F, Nicholson K, Lake I, Davies T, 2008, “An approach for modeling CO2
emissions from road traffic in urban areas” Science of the Total Environment 406
269-278
Newman P, Kenworthy J, 1989, “Gasoline consumption and cities: a comparison of US
cities with a global survey” Journal of the American Planning Association 55
23-37
NGII, 2000, “The numerical map of Seoul” the National Geographic Information
Institute, http://www.ngii.go.kr/
Rouwendal J, Rietveld P, 1994, “Changes in commuting distances of Dutch households”
Urban Studies 31(9) 1545-1557
SC,
1998,
“2020
Seoul
City
comprehensive
plan”
Seoul
City,
http://www.seoul.go.kr/2004brief/2020/city_main.html
Scott D M, Kanaroglou P S, Anderson W P, 1997, “Impacts of commuting efficiency oncongestion and emissions: case of the Hamilton CMA, Canada” Transportation
Research D 2(4) 245-257
SDI, 2002, “2002 Seoul metropolitan region person trip survey”, Seoul Development
Institute, http://www.sdi.re.kr/poup/trff/trff_idx.jsp
SK, 2005, “2005 Population and Housing Census of Korea”, Statistics Korea,
http://www.kostat.go.kr/nso_main/nsoMainAction.do?method=main&catgrp=eng2009
SM, 2002, “Operating timetable of trains and transfer times for line Nos. 1 to 4”, Seoul
Metro, http://www.seoulmetro.co.kr/index.jsp
SMRTC, 2002, “Operating timetable of trains and transfer times for line Nos. 5 to 8”
Seoul
Metropolitan
Rapid
TransitCorporation,
http://www.smrt.co.kr/english_smrt/ndex.jsp
TSC, 2007, “2020 Seoul city transportation plan” Transport Seoul City,
http://transport.seoul.go.kr/
UN, 1998, “Kyoto protocol to the united nations framework convention on climatechange”, United Nations, http://unfccc.int/kyoto_protocol/items/2830.php
Wachs M, Taylor B D, Levine N, Ong P, 1993, “The changing commute: a case study ofthe jobs-housing relationship over time” Urban Studies 30(10) 1711-1729
Wang Q, Holguin-Veras J, 2009, “Tour-based entropy maximisation formulations ofurban commercial vehicle movements” The 18th International Symposium on
Transportation and Traffic Theory, 16-18 July 2009 at Hongkong
Webber M J, 1976, “Entropy maximizing models for the distribution of expenditures”
Papers in Regional Science 37(1) 185-198
White M J, 1988, “Urban commuting journeys are not wasteful” Journal of Political
economy 96 1097-1110
Wilson A G, 1967, “A statistical theory of spatial distribution model” Transportation
Research 1 253-269