The effects of high-density suburban development on commuter mode choices in Seoul, Korea

Cities 31 (2013) 230–238

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The effects of high-density suburban development on commuter mode choices in Seoul, Korea Myung-Jin Jun a, Jae Ik Kim b,⇑, Jin Hwi Kwon b, Ji-Eun Jeong a a b

Department of Urban Planning and Real Estate, Chung-Ang University, Republic of Korea Department of Urban Planning, Keimyung University, 1000 Shindang-dong, Dalseo-gu 704-701, Republic of Korea

a r t i c l e

i n f o

Article history: Received 17 May 2011 Received in revised form 7 May 2012 Accepted 20 June 2012 Available online 17 July 2012 Keywords: Automobile usage New suburban development Logistic regression model

a b s t r a c t This study empirically assesses the effects of high-density suburban development on commuter mode choices in Seoul, Korea. We separated the impacts of densification and new suburban development on mode choice through an examination of interaction effects between population density and the sizes of newly developed suburban areas in logistic regression models. As expected, population and employment densities are positively related to public transit use, while new suburban residential and nonresidential developments are positively related to automobile use. However, the interactive logistic model empirically confirms that a one-unit increase in development density of the new suburban residential increased automobile usage by 27% in 1996 and 17% in 2006 (13% in 1996 and 16% in 2006 for new nonresidential developments), indicating that suburbanization and density are not at odds, but rather are corroborative in encouraging automobile use in the Seoul metropolitan area (SMA). One of the primary explanations for this finding is the strong housing preference for suburban high-rise apartment buildings among SMA residents. Ó 2012 Elsevier Ltd. All rights reserved.

Introduction Urban planners are concerned about the undesirable consequences of urban sprawl, such as high levels of automobile-associated congestion and air pollution. As a reaction to unfettered outward low-density development, Smart Growth advocates suggest increasing development densities, encouraging mixed land use and investing in public transit (Downs, 2005). The underlying assumption of Smart Growth is that a positive relationship exists between dense mixed land uses and public transit ridership. Urban sprawl contributes to automobile dependence because the separation between land uses in low-density development makes driving inevitable, while mixed land use and densification reduce automobile dependence and promote transit and use of non-motorized modes (Cervero, 2002; Cervero & Gorham, 1995; Frank & Pivo, 1994; Friedman, Gordon, & Peers, 1994; Handy, 2005; Kitamura, Mokhtarian, & Laidet, 1997; Nelson, 1995 chap. 3; Newman & Kenworthy, 1989, 1999; Pushkarev & Zupan, 1977; Seskin & Cervero, 1996). Unlike existing studies, which have focused primarily on cities in North America and Europe, in this study we empirically analyze the relationships between land use patterns and commuter mode choices in Seoul, the capital of South ⇑ Corresponding author. Tel.: +82 53 580 5278; fax: +82 53 580 5775. E-mail addresses: [email protected] (M.-J. Jun), [email protected] (J.I. Kim), [email protected] (J.H. Kwon), [email protected] (J.-E. Jeong). 0264-2751/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cities.2012.06.016

Korea. Development density, levels of mixed land use and public transit share are much higher in the Seoul metropolitan area (SMA) than in Western cities, already reaching beyond the levels suggested by Smart Growth proponents. In addition, Seoul has experienced high-density suburban development in the last several decades, which provides a good test bed for investigating how the two contradictory factors, one encouraging automobile use (suburbanization) and the other promoting transit ridership (density), affect commuter mode choice. Seoul1 is unique in terms of development density, mixed land use patterns and provision of public transit compared to Western cities. First, the population of Seoul, known as one of the fastest growing metropolises in the world, has almost quintupled over the last half-century, from 5.2 million in 1960 to 24.5 million in 2007 (Korean Statistical Information Service: http://kosis.kr). Thus, the core city of Seoul is one of the most densely urbanized areas in the world with 52,500 inhabitants per square mile, a figure 8.5 times higher than the Los Angeles metropolitan area as of 2000.2 In addition, Seoul is noted for its rapid suburbanization. Massive suburban residential development, including five new towns, has attracted approximately 3.3 million residents from the core city of Seoul, as

1 The Seoul metropolitan area (SMA) consists of three administrative bodies covering 12,446 km2: City of Seoul, City of Incheon, and Kyunggi Province. 2 http://demographia.com/rac-seoul.pdf.

M.-J. Jun et al. / Cities 31 (2013) 230–238

well as from other parts of Korea, to the Seoul suburbs in the last 20 years (Korean Statistical Information Service: http://kosis.kr). Second, although Seoul first introduced zoning in 1934, Seoul’s land-use pattern is a mixture of residential and nonresidential uses, in part because high-density residential development makes it possible to attract small- and medium-size businesses, mostly within walking distance, and in part because of flexible zoning laws that allow commercial buildings to be located within residential areas. In addition, Seoul’s zoning laws do not include minimum lot size, which is believed to be partly responsible for low-density suburban development in the US. The separation between largescale low-density residential development and shopping malls in suburban Seoul, which are popular elements in major US metropolitan suburban areas, is difficult to detect. Third, massive investments in public transit have been made to connect newly developed suburban areas and downtown Seoul, focusing on a heavy rail system. The SMA subway system is one of the most extensive and heavily used transit systems in the world. The SMA subway system has 11 lines and handles more than 10 million daily trips (Ministry of Land, Transport, and Maritime Affairs: http://www.mltm.go.kr). Beginning with the operation of Line 1 in 1974, the SMA subway system has been extended significantly by the construction of new lines between 1995 and 2005. The total length of the subway system increased by 101% from 222 km in 1995 to 447 km in 2005 (Ministry of Land, Transport, and Maritime Affairs: http://www.mltm.go.kr). In addition, in 2004 the Seoul metropolitan government finished constructing a Bus Rapid Transit (BRT) system that also links suburban residential areas to urban employment centers. As of 2010, 81 km of the BRT network covers seven different corridors, with plans to extend the BRT to a length of 191.2 km over 16 routes (Seoul Metropolitan Government, 2009). Due to Seoul’s unique features in terms of development density, mixture of land uses and public transit provision, we sought to empirically examine how these land use features affect commuter mode choices using logistic regression models, focusing on the impacts of recent high-density suburban development on commuter mode choice. We include development density, land sizes of new developments and levels of mixed use in logistic regression analyses in order to measure the effects of these variables on commuter mode choice. Moreover, we include two interaction variables for investigating the net effects of suburban development and density on mode choice3: product terms of (1) newly developed residential areas and population densities of residential areas, and of (2) newly developed nonresidential areas and employment densities at workplaces. This paper consists of four sections. The first reviews the existing literature, focusing on theoretical and empirical studies pertaining to factors affecting commuter mode choice. The second introduces the characteristics of Seoul’s suburban development patterns and commuter mode choices. The third section describes binary logistic regression models and explanatory variables. The fourth section presents model results and interpretations, followed by conclusions and policy suggestions.

Previous studies The present study focuses on the mode choice effects of development density, mixed land use, proximity to public transportation or highway accessibility, suburbanization and socioeconomic characteristics. Many studies have presented inverse relationships between employment and population densities and travel distance and trip frequency, empirically confirming that high-density 3

Similar approaches can be found in Chatman (2008) and Buehler (2011).

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development reduces automobile dependency (Boarnet & Crane, 2001; Cervero, 2002; Giuliano & Dargay, 2006; Limtanakool, Dijst, & Schwanen, 2006; Souch, 2010; Travisi, Camagni, & Nijkamp, 2010). However, automobile dependency for nonwork-related travel has little relationship with density (Boarnet & Crane, 2001; Chatman, 2008). Among studies of East Asian cities, Loo and Chow (2008) examined the relationship between urban expansion and travel behavior in the case of Hong Kong. One of the most densely populated cities in the world, Hong Kong is experiencing rapid suburbanization due to high-density residential development in its suburban areas. Loo and Chow found that suburban commuters experience trips of longer duration and distance due to the lack of self-containment in the new growth suburban areas. However, most nonwork-related trips are more self-contained within new growth areas. Their primary concerns were spatial dependency between the traditional urban core and new-growth suburban areas, rather than the relationship between density and auto-dependency. There are numerous studies suggesting a negative relationship between land use mixing and automobile use (Buehler, 2011; Cervero, 2002; Cervero & Kockelman, 1997; Sung & Oh, 2011; Travisi et al., 2010). For example, Cervero and Kockelman (1997) found that the use of transport modes other than private automobiles was reduced as the level of mixed land use increased. Kenworthy (2002) also observed that high-density developments with a mix of land uses are associated with shorter trip distances and offer more opportunities for walking and bicycling. Boarnet and Crane (2001) concluded that mixed land use contributes to reductions of automobile dependence, even for nonbusiness travel. The proximity and quality of public transportation services are also important factors in determining mode choice. When quality public services are available near individuals who travel, the share of transit (automobile travel) increases (decreases) (Buehler, 2011; Vega & Reynolds-Feighan, 2009). On the other hand, improved highway accessibility stimulates the use of automobiles (Cervero, 2002; Cervero & Day, 2008; Kitamura et al., 1997; Limtanakool et al., 2006). The suburbanization of population and employment is a key factor determining commuter mode choice. Some researchers have concluded that suburbanization increases travel distances and travel times (Cervero & Landis, 1992), while others have argued the opposite (Gordon, Kumar, & Richardson, 1989; Gordon, Richardson, & Jun, 1991). However, concerning the effects of suburbanization on automobile dependence, it is generally accepted that dispersed low-density suburban development causes suburban residents to rely more heavily on private vehicles. Such travel behavior is observed in American, European and Chinese cities (Cervero & Day, 2008; Garcia-Palomares, 2010). Socioeconomic characteristics are the most important factors affecting mode choice. Many studies have shown that car ownership, income, education level, age, gender and number of children are key factors determining levels of automobile dependence. Studies by Algers (1993) in Sweden, by Boarnet and Crane (2001) and Cervero (2002) in the US, by Cervero and Day (2008) in China, by Loo and Chow (2008) in Hong Kong, by Bueheler (2011) in Germany, by Garcia-Palomares (2010) in Spain, by Limtanakool et al. (2006) in the Netherlands and by Sung and Oh (2011) in South Korea used these socioeconomic variables as control variables. Unlike previous studies that focused on either the effects of suburbanization on automobile dependence or on the relationships between densification and mixed land use and public transit use, we separate the impact of densification from that of suburbanization on mode choice by introducing interaction effects between densities and newly developed suburban land sizes into logistic regression models.

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Development patterns and transportation in Seoul Population suburbanization The SMA has experienced substantial suburbanization of both population and employment for the last 15 years while the area of the SMA has remained unchanged. Table 1 shows changes in population and employment shares by distance from the central business district (CBD) between 1990 and 2005 as indicators of population and employment suburbanization. The population significantly decreased within a 20 km radius from the CBD between 1990 and 2005. For example, the Seoul downtown population share declined by 38%, from 8.5% in 1990 to 5.2% in 2005. On the other hand, the population shares of suburban areas within a 20–40 km range increased significantly. Major large-scale suburban development in the SMA started with new town development projects in the late 1980s. The new town development plan for the SMA was announced in 1989, after the SMA experienced a serious housing shortage and high levels of housing market speculation. The housing rent index increased 3.2 times between 1980 and 1990, while the consumer price index increased around 1.9 times during the same period. As shown in Table 2 and Fig. 1, the Korean government began to construct five new towns at distances of 20–28 km from the CBD in 1989, and completed the construction in 1995. Even though new town development in the SMA helped stabilize housing prices by supplying a total of about 300,000 housing units in a short period of time, this new town development in the SMA was recently attacked and criticized for its development density (see Table 2). The average population density of the five new towns is greater than 17,000 persons per km2, which is around 5–10 times higher than new town developments in England (e.g., 2400 per km2 in Milton Keynes, www.mkweb.co.uk) (Jun & Hur, 2001). In addition to the new large-scale town development, a substantial amount of suburban land has been converted to urban uses during the last 15 years. Table 3 presents new residential and nonresidential development between 1990 and 2005 according to distance to the CBD of the SMA. About 60% of new residential development occurred between 20 km and 40 km away from the CBD, while only about 13% of new residential development occurred within the 15 km boundary, indicating that substantial population suburbanization occurred within the 40 km boundary of the SMA. The rise of medium-sized new residential developments near the five new towns, or so-called ‘‘parasitic’’ towns that depend on the urban service facilities of the new towns such as Yong-In, near Boondang, has also encouraged population suburbanization, attracting residents from central Seoul. Fig. 2 outlines the substantial amount of new residential land areas that were developed in suburban Seoul between 1990 and 2005. Table 4 and Fig. 3 show new residential developments implemented by central and local governments since 1990, demonstrating that these residential developments are concentrated along major freeways, especially the west- and south-bound freeways from Seoul.

Table 1 Changes in population and employment shares by distance from the CBD. Source: Korea Statistical Office. Distance from the CBD (km)

Population 1990 (%)

2005 (%)

% change

1990 (%)

2005 (%)

% change

0–5 5–10 10–15 15–20 20–25 25–30 30–35 35–40 40–45 40–45 50 Plus

8.5 25.7 23.4 11.2 7.9 8.1 6.5 1.9 1.2 1.1 4.6

5.2 18.3 21.0 10.5 13.9 11.3 9.8 3.2 1.1 1.4 4.3

38.1 28.5 10.2 6.9 75.5 39.6 52.1 70.8 6.8 20.8 6.5

21.6 26.4 15.3 8.0 6.4 9.1 5.7 2.2 1.0 1.1 3.2

12.0 22.1 16.8 8.0 10.4 9.0 10.0 3.3 1.9 1.7 4.7

44.3 16.3 9.4 0.4 63.0 0.6 77.3 54.1 79.7 61.1 46.3

Employment

and Youndungpo, during the same period. Suburban areas have also experienced substantial job gains during the past 15 years. Table 3 shows that about 90% of new nonresidential development took place beyond a 20 km radius from the CBD, while only 7% of new nonresidential development occurred within the city of Seoul. Fig. 4 also shows that a significant amount of suburban nonresidential development took place between 1990 and 2005. There are two major explanations for employment suburbanization. According to classical urban economic theory, populationserving jobs follow suburban residents, locating in or near newly developed residential areas. Another factor pertains to government regulations on industrial land use, which is strictly regulated in the Growth Control Zone (GCZ) that encompasses Seoul and its neighboring satellite cities. Because of this, the majority of new industrial firms are located in the Growth Management Zone, which includes southern and northern suburban areas of the SMA.4

Commuter mode choice in the Seoul metropolitan area Despite government efforts to reduce private automobile use and encourage public transit ridership, Seoul has experienced a rapid decline of transit shares. For example, Seoul’s transit share dropped from 56.4% in 1990 to 35.4% in 2005 for daily commuting trips. Table 5 shows dramatic changes in commuting mode shares for the last 15 years: a sharp growth in automobile shares from 17% to 40.6%, a rapid decline in bus shares from 45.7% to 20.3% and a modest increase in subway shares from 10.7% to 15.1%. Population and employment have increased only 22.4% and 33.2%, respectively, over the period. These data imply that a significant mode shift has occurred in Seoul from transit to automobile commuting during the last 15 years. This is related to increases in car ownership5 and other factors, such as the poor service quality of public transit. In addition, land use characteristics and suburbanization are important for determining both automobile dependence and transit ridership.

Employment suburbanization Changes in nonresidential development during the same time period also indicate substantial employment decentralization. The job share of downtown Seoul dropped by almost half, from 21.6% in 1990 to 12% in 2005 (Korean Statistical Information Service: http://kosis.kr). The job share of the area between 5 km and 10 km from the CBD also declined by 16%. However, the share of employment in the 10–15 km range from the CBD increased due to the emergence of employment subcenters, such as Kangnam

4 The Capital Region Management and Planning Law (CRMPL) classified the capital region into three zones and applied different control measures to each based on zonal characteristics: the Growth Control Zone (GCZ), the Growth Management Zone (GMZ), and the Environment Preservation Zone (EPZ). The GCZ encompasses the city of Seoul and its neighboring satellite cities, whereas the northern and southern parts of suburban Seoul were designated as the GMZ. The eastern part of Seoul, which has a superb natural environment with Han River Water Source Preservation Areas, was deemed the EPZ. 5 The number of registered automobiles in Korea has increased more than 10 times between 1990 and 2005, from 3.0 million to 14.6 million (Ministry of Land, Transport, and Maritime Affairs: http://www.mltm.go.kr).

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M.-J. Jun et al. / Cities 31 (2013) 230–238 Table 2 Construction period, area, and target population by town. Source: Korea Land Corporation, 1999

*

New town

Distance from CBD (direction)

Construction period

Project area (km2)

Target population

Population density* (persons/km2)

Bundang Ilsan Jungdong Pyungchon Sanbon

25 km 28 km 20 km 20 km 25 km

1989–1996 1989–1995 1990–1994 1989–1995 1989–1994

19.6 15.7 5.5 5.1 4.2

390,320 276,000 165,740 168,188 167,896

19,914 17,580 30,135 32,978 39,975

(SE) (NW) (W) (SW) (SW)

Population density in CBD (Jongro-gu) was16,873 persons per Km2 in 2010 (Source: Korea Census of Population and Housing 2010).

Fig. 1. Seoul Metropolitan Area and the locations of the five new towns.

Table 3 New residential and nonresidential developments by distance to the CBD, 1990–2005. Distance from the CBD (km)

Residential land (km2)

0–5 5–10 10–15 15–20 20–25 25–30 30–35 35–40 40–45 40–45 50 Plus Total

Share of total (%)

Nonresidential land (km2)

Share of total (%)

1.5 5.0 10.7 11.8 24.3 23.6 22.3 12.0 5.1 5.7 22.5

1.0 3.5 7.4 8.2 16.9 16.3 15.4 8.3 3.5 3.9 15.5

0.2 1.5 3.1 3.5 7.5 8.1 19.4 3.3 3.4 1.8 11.3

0 2 5 6 12 13 31 5 5 3 18

144.35

100.0

63.02

100.0

2006), the Population and Housing Census (Korea Statistical Office, 1995 and 2005) and the Business Census (Korea Statistical Office, 1996 and 2006). Household Travel Survey data include information about commuter mode choice, origin and destination locations, and commuter sociodemographic information, while population and employment densities were drawn from Population and Housing Census and Business Census data. We used satellite images of the SMA to obtain data regarding newly developed land, including land area and land use type. We extracted newly developed urbanized areas by overlapping satellite images taken in 1995 and 2005, and classified newly urbanized areas into residential, commercial and industrial use categories by overlaying satellite images with a land use map from the Land Management Information System. In addition, we obtained data including the locations of bus stops and subway stations in 2008 from the Gyunggi Research Institute (GRI).

Empirical analysis Models and variables Data The primary sources used in this study are Household Travel Surveys for the SMA (Seoul Metropolitan Government, 1996 and

To assess the effects of land use patterns on commuter mode choice, binary logistic regression models were developed in which the probability that a commuter will choose a given mode is a

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Fig. 2. Residential development in the SMA between 1990 and 2005.

Table 4 Population densities of new residential developmentsa by distance to the CBD. Distance from the CBD (km)

Land area (km2)

Target population

Population density per km2

10–15 15–20 20–25 25–30 30–35 35–40 40–45 45 plus

0.813 11.478 8.287 11.095 17.085 9.987 3.015 5.623

13,149 408,221 188,613 376,358 411,579 232,313 69,646 158,092

16,173 35,566 22,760 33,921 24,090 23,262 23,100 28,115

a Residential developments planned and implemented by local governments and Korea Land and Housing Corporation since 1990.

function of the commuter’s socioeconomic, land use and locationrelated variables. We built two logistic regression models for 1996 and 2006 to determine the probability that a commuter would choose private automobile transportation. For k explanatory variables and i = 1, 2, . . . , n individuals, the logit model is:

 ln

Pi 1  Pi



¼ a þ b1 xi1 þ b2 xi2 þ b3 xi3 þ    þ bk xik

ð1Þ

where pi is the probability that an individual i chooses mode 1 (automobile). The expression on the left-hand side is usually referred to as the logit or log-odds (Allison, 1999). We introduced 16 independent variables that may affect the decision to drive a private vehicle into our logistic regression models, including six demographic and economic variables and five location variables. We also included five land use-related variables representing population (employment) density, the land sizes of new residential and nonresidential development and the level of mixed use of newly developed land. Table 6 shows summary statistics of the explanatory variables used in binary logistic regressions.

Five socioeconomic variables (head of household, male gender, age, average household income and number of vehicles available per resident worker) are expected to have positive signs because heads of households, men, older employees, and members of families with higher incomes or those owning more vehicles are more likely to drive automobiles to work. The sign for household size is expected to be negative because members of larger families are more likely to take public transit. Among the five location-related variables, the sign for the distance to a subway station from home is expected to be positive, reflecting the fact that that commuters who live farther away from subway stations are more likely to drive private vehicles, while the sign for the distance to a freeway interchange from home is expected to be negative because commuters who live closer to freeway interchanges are more likely to drive private vehicles. The sign for the travel distance is also expected to be negative because longer trips are generally made via public transit. Among the five land use-related variables, the signs for both population density at home and employment density at workplaces are expected to be negative due to high transit availability in denser areas. The sign for the level of mixed land use is also expected to be negative because more diversified land use is associated with decreased automobile use. Finally, in this paper we introduce two interaction variables to capture interaction effects between the sizes of new developments and density: a product term between newly developed residential land areas and population densities in residential areas, and a product term between newly developed nonresidential land areas and employment densities in workplaces. As Jaccard and Turrisi (2003) argued, interaction effects are characterized based on the concept of moderation that occurs when the relationship between two variables depends on a third moderator variable. We specify density (population or employment density) as a moderator variable and the size of newly developed land (residential or nonresidential land) as a focal independent variable, since we hypothesize

M.-J. Jun et al. / Cities 31 (2013) 230–238

235

Fig. 3. Residential development sites implemented by central and local governments Since 1990 with land sizes of 0.5 km2 or larger.

Fig. 4. Nonresidential development in the SMA between 1990 and 2005.

that the effects of new suburban development on commuter mode choice vary depending on development density. The inclusion of interaction variables into logistic models generates a multicollinearity problem because the interaction terms are correlated with the main effects terms used to calculate them. Following Kreft and Leeuw (1998) who suggested two ways to

address this problem, centering and standardization, we employ the centering method in this study to reduce multicollinearity between main and interaction effects. In this method, the covariance and correlation of interaction terms become zero, with the mean of the main effect variables also equal to zero. Centering makes it possible to examine the main effects of one independent variable

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Table 5 Mode shares for commuting trips in the Seoul Metropolitan area. Source: Population and Housing Census, Korea Statistical Office, 1990 and 2005. Urbanized area was extracted from satellite image analysis.

Auto Bus Subway Bike/walk Other Population (person) Urbanized Area (km2)

1990

2005

17.0% 45.7% 10.7% 24.4% 2.3% 18,586,128 990.74

40.6% 20.3% 15.1% 21.8% 2.3% 22,754,579 3212.65

at the mean of another independent variable. The four independent variables used to calculate interaction effects in this study were mean-centered by subtracting the grand mean from each variable’s observed value (Jaccard, 2001). Estimation results Table 7 reports the results of a logistic regression model to predict mode choice. As expected, heads of households, men, older employees, and commuters in households with higher incomes or more vehicles were more likely to drive private automobiles, while commuters in large families were more likely to take public transit in both 1996 and 2006. Most of the five location-related variables have the expected signs with statistical significance for 1996 and 2006 (except for the sign for distance to freeway interchange from residence for 1996). Distances from residences to subway stations are positively related to the choice to drive private automobiles and are statistically significant at the 1% level for both 1996 and 2006, indicating that commuters who reside farther away from subway stations are more likely to drive private automobiles. On the other hand, distances from residences to the nearest freeway interchanges are negatively related to private vehicle use and are statistically significant at the 1% level for 2006, indicating that higher accessibility to freeways fosters higher automobile dependence. Travel distance is negatively related to the choice of automobile transit, indicating that commuters making longer trips are more likely to use public transit. Both distances from residences or workplaces

to the CBD are positively related to driving private automobiles, providing empirical evidence that population and employment suburbanization has contributed to increasing automobile dependence. Moreover, the predicted odds of driving a private automobile for varying distances between the workplace and the CBD are higher by about 60% for both years than those for distances from home to the CBD. This finding implies that employment decentralization has a greater influence on automobile dependence than population suburbanization in Seoul. We derive several interesting findings from our analyses of land use-related variables. First, the share of mixed land use of new developments was negatively correlated with automobile use for both 1996 and 2006, indicating that mixed land use contributes to reduction of automobile dependence. The probability of driving an automobile in mixed land use contexts increased from 0.777 in 1996 to 0.941 in 2006, implying that a one-unit increase in the level of mixed land use increased public transit ridership by 28.7% in 1996 and 6.2% in 2006. Second, as expected, population densities at residences and employment densities at workplaces are negatively correlated with the choice of automobile use for both 1996 and 2006. However, because population and employment densities are parts of the product terms, these are conditioned coefficients and therefore reflect the effects of population or employment densities when newly developed residential or nonresidential land sizes equal their sample means. When newly developed residential land areas are at their sample means, a one-unit increase in population density resulted in changes in the predicted odds of driving an automobile by a multiplicative factor of 0.955 in 1996 and 0.901 in 2006, meaning that a one-unit increase in population density increased public transit ridership by 5% in 1996 and 11% in 2006 at the condition of average land size of newly developed residential land. Third, new developments of both residential and nonresidential land areas are positively correlated with automobile use. Since these variables were also part of the product terms, they reflect the effects of newly developed land sizes on automobile dependence when population or employment densities equal their sample means. Our results show that a one-unit increase in new residential land area resulted in changes in the predicted odds of

Table 6 Summary statistics for the variables of the logistic regression model. 1996

Head of household (1 if head, 0 otherwise) Age Square of age (age  age) log (household income) Number of automobiles per household Male gender (1 if male, 0 otherwise) Number of family members log (travel distance: km) log (distance to subway station) at origin log (distance to freeway interchange) at origin log (distance to the CBD) at origin log (distance to the CBD) at destination log (population density) at originb log (employment density) at destinationb Newly developed residential land area at origin zone (km2)a,  Newly developed non-residential land area at destination zone (km2)a,b Share of mixed land use [new nonresidential land/(new residential and new nonresidential land)] Newly developed residential land⁄log (population density) Newly developed nonresidential land⁄ log (employment density) a b

2006

Mean

S.D.

Mean

S.D.

0.668 37.336 1498.330 5.077 0.698 0.769 3.946 1.775 0.742 4.762 2.622 2.315 0.000 0.000 0.000 0.000 0.342 0.062 0.088

0.471 10.217 825.769 0.561 0.553 0.422 1.095 1.036 1.193 0.115 0.691 1.072 1.105 1.691 0.172 0.366 0.368 0.304 0.554

0.605 40.914 1779.740 5.573 0.885 0.727 3.674 1.820 0.507 1.266 2.776 2.605 0.000 0.000 0.000 0.000 0.308 0.108 0.040

0.489 10.284 877.175 0.651 0.635 0.446 1.054 1.126 0.940 0.803 0.668 0.946 1.265 1.644 0.222 0.171 0.357 0.622 0.357

Change in land areas between 1990 and 1995 for the 1996 model, and between 1995 and 2005 for the 2006 model. Mean centered.

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M.-J. Jun et al. / Cities 31 (2013) 230–238 Table 7 Binary logistic regression estimates (reference group is public transit). 1996 Logistic coefficient Socio-Economic Variables

Location Variables

Land Use-related Variables

*

***

Exponent of coefficient

Logistic coefficient

Exponent of coefficient

Head of household (1 if head, 0 otherwise) Age Square of age (age  age) log (household income) Number of automobiles per household Male gender (1 if male, 0 otherwise) Number of family members log (travel distance: km) log (distance to subway station) at origin log (distance to freeway interchange) at origin log (distance to the CBD) at origin log (distance to the CBD) at destination

0.749** 0.292** 0.004** 0.131** 2.269** 1.242** 0.069** 0.022** 0.190** 0.787** 0.004 0.516**

2.114 1.340 0.996 1.140 9.670 3.461 0.933 0.979 1.210 2.198 1.004 1.675

0.391** 0.326** 0.004** 0.282** 1.012** 1.296** 0.033** 0.112** 0.180** 0.082** 0.172** 0.542**

1.479 1.385 0.996 1.326 2.751 3.654 0.968 0.894 1.197 0.921 1.187 1.719

log (population density) at origin log (employment density) at destination Newly developed residential land area at origin zone (km2)*** Newly developed non-residential land area at destination zone (km2)*** Newly developed residential land  log (population density) Newly developed nonresidential land  log (employment density) Share of mixed land use [new nonresidential land/(new residential and new nonresidential land)]

0.017* 0.128** 0.716** 0.227** 0.237** 0.123** 0.253**

0.983 0.880 2.047 1.255 1.268 1.131 0.777

0.108** 0.230** 0.479** 1.056** 0.159** 0.146** 0.061**

0.898 0.794 1.615 2.875 1.173 1.157 0.941

13.750 165256.36 107584.68

0.000

11.234 215814.05 152821.02

0.000

Intercept 2 Log L W/O covariates 2 Log L With covariates **

2006

p < .05. p < .01. Change in land areas between 1990 and 1995 for the 1996 model, and between 1995 and 2005 for the 2006 model.

driving an automobile by a multiplicative factor of 2.047 in 1996 and 1.615 in 2006 (1.255 in 1996 and 2.875 in 2006 for new nonresidential developments), meaning that a one-unit increase in new residential land area increased automobile use by 105% in 1996 and 61% in 2006 at the condition of mean population density (by 26% in 1996 and 187% in 2006 for nonresidential land area at the mean employment density). Last and most importantly, the exponent of the coefficient of the interaction term between newly developed residential (nonresidential) land area and population (employment) density indicates the factor by which multiplying factor of newly developed residential (nonresidential) land changes, given a one-unit increase in population (employment) density. We find that a one-unit increase in population (employment) density caused the impact of new residential (nonresidential) development on automobile usage to increase by a factor of 1.268 in 1996 and 1.173 in 2006 (1.131 in 1996 and 1.157 in 2006 for new nonresidential developments), confirming that high levels of automobile usage are caused by high-density suburban development in Seoul. Conclusions We empirically assessed how high-density suburban development in the SMA affected commuter mode choices by separating the impact of densification from that of suburbanization on mode choice through the analysis of interaction effects between employment and residential densities and newly developed suburban land sizes in logistic regression models. Our major findings can be summarized as follows. First, development density, the level of mixed land use, and accessibility to subways are negatively related to automobile usage, while distances from the CBD to the workplace or residence (proxies of population and employment suburbanization) and proximity to freeway entrances are positively related to automobile usage. Second, comparing the predicted odds of automobile commuting between population and employment suburbanization, we found

that employment decentralization has a greater effect on automobile usage than does population suburbanization. Third, the interactive logistic model empirically confirms that a one-unit increase in development density of the new suburban residential increased automobile usage by 27% in 1996 and 17% in 2006 (13% in 1996 and 16% in 2006 for new nonresidential developments), indicating that suburbanization and density are not contradictory, but rather are corroborative in encouraging automobile use in the SMA. One of the primary reasons for this unique finding in the SMA may be the strong preference of SMA residents for high-rise apartment buildings6. According to the Korea National Statistical Office, as of 2010, 64% of SMA residents lived in apartments. Furthermore, about 80% of newly constructed housing units in 2008 were apartments (Korea National Statistics Office, http://kosis.kr/nsp/index/index.jsp). Since apartment prices are elevated due to their popularity and other factors such as security and amenities, they are occupied mostly by middle- and high-income residents, who have high rates of car ownership. The actual mode shares of new suburban town residents also support this finding. For example, the automobile share of commuters living in new suburban towns was 64% in 2006, which is about 8% higher than that of non-new town commuters (SMA Household Travel Survey, 2006). The results of our empirical analyses provide mixed policy implications for proponents of Smart Growth. First, densification of population and employment and mixed land use may be effective Smart Growth tools for increasing transit ridership. However, our empirical evidence from the SMA suggests that suburban densification may not discourage automobile usage, which contradicts the arguments of Smart Growth advocates, who emphasize the importance of suburban densification to reduce automobile dependence.

6 An apartment is defined by the Korean Housing Law as multi-family housing of 5 or more stories. The majority of recently-constructed apartment buildings in the SMA are more than 15 stories tall.

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Second, there are two policy options to mitigate transportation problems due to high automobile usage in dense suburban areas in the SMA: (1) improving accessibility to public transit for suburban commuters, and (2) encouraging transit-oriented suburban nonresidential development. Although suburban commuters in the SMA have relatively good access to public transit compared to similar commuters in Western cities, the quality of transit service should be improved in order to encourage commuters to shift mode from automobiles to public transit. The Korea Transportation Institute (2012) reports that suburban commuters in the SMA suffer from bus and subway overcrowding during commutes (http://www.koti.re.kr). Another policy option for central and local governments is to emphasize the reduction of vehicle miles of travel (VMT) by improving the balance between jobs and housing in suburban areas. To this end, governments would benefit by providing incentives to factories and business firms to relocate to suburban areas. References Algers, S. (1993). Integrated structure of long-distance travel behaviors in Sweden. Transportation Research Record, 1413, 141–149. Allison, Paul D. (1999). Logistic regression using the SAS system: Theory and application. Cary, NC: SAS Institute Inc.. Boarnet, M., & Crane, R. (2001). The influence of land use on travel behavior: Specification and estimation strategies. Transportation Research Part A, 35, 823–845. Buehler, Ralph (2011). Determinants of transport mode choice: A comparison of Germany and the USA. Journal of Transport Geography, 19(4), 644–657. Cervero, R. (2002). Built environments and mode choice: Toward a normative framework. Transportation Research Part D, 7, 265–284. Cervero, R., & Day, J. (2008). Suburbanization and transit-oriented development in China. Transport Policy, 15, 315–323. Cervero, R., & Gorham, R. (1995). Commuting in transit versus automobile neighborhoods. Journal of American Planning Association, 61, 210–225. Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research D, 2(3), 199–219. Cervero, R., & Landis, J. (1992). Suburbanization of jobs and the journey to work: A submarket analysis of commuting in the San Francisco Bay Area. Journal of Advanced Transport, 25, 275–297. Chatman, D. (2008). Deconstructing development density: Quality, quantity and price effects on household non-work travel. Transportation Research Part A, 42, 1008–1030. Downs, Anthony (2005). Smart growth: Why we discuss it more than we do it. Journal of American Planning Association, 71(4), 367–378. Frank, Lawrence D., & Pivo, Gary (1994). Impacts of mixed use and density on utilization of three modes of travel: Single-occupant vehicle, transit, and walking. Transportation Research Record, 1466, 44–52. Friedman, B., Gordon, S. P., & Peers, J. B. (1994). Effect of neotraditional neighborhood design on travel characteristics. Transportation Research Record, 1466, 63–70. Garcia-Palomares, J. C. (2010). Urban sprawl and travel to work: The case of the metropolitan area of Madrid. Journal of Transport Geography, 18, 197–213.

Giuliano, G., & Dargay, J. (2006). Car ownership, travel and land use: A comparison of the US and Great Britain. Transportation Research Part A, 40, 106–124. Gordon, P., Kumar, A., & Richardson, H. W. (1989). Congestion, changing metropolitan structure, and city size in United States. International Regional Science Review, 12, 45–56. Gordon, P., Richardson, H. W., & Jun, M.-J. (1991). The commuting paradox: Evidence from the top twenty. Journal of American Planning Association, 57, 416–420. Handy, Susan (2005). Smart growth and the transportation-land use connection: What does the research tell us? International Regional Science Review, 28(2), 146–167. Jaccard, James (2001). Interaction effects in logistic regression. Newbury Par, CA: Sage Publications Inc.. Jaccard, James, & Turrisi, Robert (2003). Interaction effects in multiple regression. Newbury Park, CA: Sage Publications Inc.. Jun, M. J., & Hur, J. W. (2001). Commuting costs of ‘‘leap-frog’’ newtown development in Seoul. Cities, 18(3), 151–158. Kenworthy, J. (2002). A Global perspective on urban transport: Shaping the future of urban settlements with rail-based public transport systems. Bern, Switzerland: Swiss Federal Railways (SBB). Kitamura, R., Mokhtarian, P. L., & Laidet, L. (1997). A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation, 24, 125–158. Kreft, I., & Leeuw, J. D. (1998). Introducing multilevel modeling. London: Sage Publications Ltd.. Limtanakool, N., Dijst, M., & Schwanen, T. (2006). The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium- and longer-distance trips. Journal of Transport Geography, 14, 327–341. Loo, Becky P. Y., & Chow, Alice S. Y. (2008). Changing urban form in Hong Kong: What are the challenges on sustainable transportation? International Journal of Sustainable Transportation, 2, 177–193. Nelson and Nygaard Consulting Associates (1995). Land use and transit demand: The transit orientation index. Primary Transit Network Study Portland. OR: TriMet. Newman, Peter, & Kenworthy, Jeffrey (1989). Gasoline consumption and cities: A comparison of U. S. cities with a global survey. Journal of American Planning Association, 55, 24–37. Newman, Peter, & Kenworthy, Jeffrey (1999). Sustainability and cities: Overcoming automobile dependence. Washington, DC: Island Press. Pushkarev, Boris S., & Zupan, Jeffrey M. (1977). Public transportation and land use policy. Bloominton: Indiana University Press. Seoul Metropolitan Government (2009). Achievement and Present State of Public Transit Reform in Seoul, 2009. Seoul: Seoul Metropolitan Government. Seskin, S., & Cervero, R. (1996). Transit and urban form. Washington DC: Federal Transit Administration. Souch, S. (2010). Measuring the structural determinants of urban travel demand. Transport Policy, 17, 127–134. Sung, Hyungun, & Oh, Ju-Taek (2011). Transit-oriented development in a highdensity city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28, 70–82. Travisi, C. M., Camagni, R., & Nijkamp, P. (2010). Impacts of urban sprawl and commuting: A modeling study for Italy. Journal of Transport Geography, 18, 382–392. Vega, Amaya, & Reynolds-Feighan, A. (2009). A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns. Transport Research Part A, 43, 401–419.