The effects of polycentric evolution on commute times in a polycentric compact city: A case of the Seoul Metropolitan Area

Cities 98 (2020) 102587

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The effects of polycentric evolution on commute times in a polycentric compact city: A case of the Seoul Metropolitan Area

T

Myung-Jin Jun Department of Urban Planning and Real Estate, Chung-Ang University, Seoul, Republic of Korea

ARTICLE INFO

ABSTRACT

Keywords: Polycentricity Urban form Suburban subcenter Commute time Seoul

This study attempts to investigate the dynamics of polycentric evolution and the changing roles of existing and new employment centers in determining commuting patterns. For this purpose, we analyze temporal changes in urban form and individual commuting behavior between 2000 and 2015 for the Seoul Metropolitan Area (SMA)—an area that has experienced rapid employment growth and changes in urban form over the last halfcentury. Results reveal both positive and negative effects on commuting efficiency. On the positive side, innercity subcenter commuters have significantly reduced commute times over the last 15 years, possibly due to locational adjustment (i.e., the reduction of commute distance), improvement in public transit accessibility, and the emergence of new suburban subcenters that attract workers who previously traveled to inner-city subcenters. On the negative side, these new suburban subcenters have also attracted more workers from a wider job market, resulting in longer commute times for suburban subcenter commuters. The evidence suggests that mixed land use for development and improvements in public transit accessibility are important drivers of commuting efficiency, while improving public transport directly leads to shorter commute times in high-density urban areas.

1. Introduction The relationship between urban form and commuting patterns has long been the subject of extensive research due to the belief that urban spatial patterns fundamentally influence commuting patterns, which are highly associated with numerous urban problems, such as motorization, traffic congestion, energy consumption, air pollution, and psychological and health issues. Empirical studies on the relationship between urban spatial features and commuting have mainly focused on the impact of urban size, density and design, degrees of suburbanization and polycentricity, and the job/housing balance and mixed land use. Although it is generally agreed that the size of the urban area and a job/ housing imbalance lead to longer commutes (Lee, Gordon, & Richardson, 2009; Levinson, 1998; Levinson & Kumar, 1994; Modarres, 2011; Schwanen, Dieleman, & Dijst, 2004; Sultana, 2002), the effects of density and polycentricity on commuting remain unclear and inconclusive, with previous research finding conflicting empirical results. That is, while it is reasonable to assume a priori that higher density leads to a reduction in commuting distance and time, the congestion generated by higher densities may result in longer commutes (Antipova, Wang, & Wilmot, 2011; Levinson & Kumar, 1997; Loo & Chow, 2011; Melia, Parkhurst, & Barton, 2011; Schwanen et al., 2004; Yang, Shen, Shen, & He, 2012).

Similarly, two contradicting views regarding the effects of employment decentralization and polycentricity on commute length and duration exist. The first view, based on the co-location hypothesis, suggests that commute length and duration become shorter or more stable in polycentric urban areas due to the rational location adjustments of households and firms to maximize utility or profit (or to minimize costs). On the other hand, some studies have argued that employment decentralization contributes to increasing commute duration and have suggested the strategic manipulation of the urban spatial structure to mitigate commuting costs. Given these differing views, this study attempts to investigate the effects of polycentric evolution on commute times by analyzing both temporal changes in urban form and commuting behavior between 2000 and 2015 for the Seoul Metropolitan Area (SMA). In so doing, we first analyze the dynamics of the SMA's urban spatial structure by tracking the evolution of employment centers; we then examine how commute time has changed for those traveling to existing and newly emerging centers using individual-level commuting data from the 2002 and 2016 Seoul Household Travel Surveys. In our methodological approach, we use minimum density cutoff and polycentric density function estimation to identify the employment centers, which are techniques suggested by Giuliano and Small (1993) and Small and Song (1994), respectively; we also use multi-level random effects models to

E-mail address: [email protected]. https://doi.org/10.1016/j.cities.2019.102587 Received 27 November 2018; Received in revised form 13 December 2019; Accepted 26 December 2019 0264-2751/ © 2020 Elsevier Ltd. All rights reserved.

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capture both the macro-spatial and micro-individual influence on commute times (Hu, Sun, & Wang, 2018; Schwanen et al., 2004). This paper is divided into five sections. The first section contains a literature review for subcenter identification and the effects of urban form on commuting. The second section introduces the data and analysis methods used to identify employment centers and the effects of the evolution of urban form on commuting time. The third section presents the results of employment center identification and multilevel regression analysis to determine how polycentric evolution affects commute times in Seoul. The fourth section discusses the empirical findings using quantitative as well as qualitative arguments, followed by conclusions and policy implications arising from the present analysis.

2001; Small & Song, 1994). For example, McDonald and Prather (1994) estimated the monocentric density function and identified zones with significant residuals as subcenters in Chicago. Three types of polycentric density function have been suggested, depending on the type of subcenter influence: competitive, complementary (multiplicative negative exponential function), and intermediate (additive negative exponential function; Heikkila et al., 1989). The competitive form assumes that the benefits from all subcenters are perfectly substitutable, but this has rarely been applied to empirical studies due to the strict assumption of completely separated zones of influence (Anas et al., 1998). However, complementary specification is viewed as more realistic because different subcenters carry out different functions. Heikkila et al. (1989) used the multiplicative form to identify subcenters in Los Angeles, arguing that its polycentricity consists of complementary subcenters. The additive specification of the model is similar to the complementary specification, but the benefits of a center become insignificant as distance increases. Small and Song (1994) estimated the additive polycentric density function in Los Angeles and found that this function is statistically superior to monocentric density functions. Another relative density criteria approach is the utilization of nonparametric density functions such as the spline density function and geographically weighted regression (GWR). Craig and Ng (2001) used quantile smoothing spline functions to identify employment subcenters in Houston, Texas, while McMillen (2001) used GWR to estimate the smoothed employment density surface and identified center candidates that have significantly higher densities than the estimate. It has been argued that GWR is superior to traditional regression models in estimating density surface because it allows the model parameters to vary over space to reflect spatial heterogeneity and gives more weight to closer locations. Despite the merits of GWR, its estimates may produce a biased density surface at edges and corners due to the presence of fewer observations (Leong & Yue, 2017). The third type of practical approach in subcenter identification is to apply both absolute and relative density criteria by taking both the minimum employment threshold and urban employment density surface into account. For example, Lee (2007) set a minimum density cutoff at the 90th percentile for employment density and used GWR to identify employment centers in six U.S. metropolitan areas. Hu et al. (2018) also used a two-stage method to identify employment centers in Beijing. They used GWR to smoothen the natural logarithm of employment density in the first stage and then identified employment centers that met the minimum employment threshold (0.5% of metropolitan employment).

2. Literature review 2.1. Evolution of Urban Spatial Structure Contemporary urban growth patterns have exhibited significant levels of suburbanization and decentralization, and a number of cities have transformed from monocentric to polycentric forms, making urban spatial structure and its evolution a research topic of particular interest (Anas, Arnott, & Small, 1998; Richardson, 1988). For example, research in this area has addressed theories behind spatial transformation (see Anas et al., 1998) for theoretical arguments on polycentricity), the concept and definition of different urban forms, the causes and consequences of urban sprawl, employment center identification and its characteristics, and normative arguments on desirable urban forms (see Gordon & Richardson, 1997; Ewing, 1997; Ewing & Hamidi, 2015 for a debate on compactness versus sprawl). In a review of the literature, Anas et al. (1998) characterized the nature and role of subcenters within U.S. cities and reported the following key findings for polycentric cities in the U.S.: 1) there has been a prominent evolution of point and corridor subcenters in both new and old cities; 2) jobs are highly dispersed, with most jobs outside centers; 3) the main center, which has the highest job density and land-value peaks, has a metropolitan-wide influence despite the steady emergence of new subcenters; 4) sensitive subcenter identification depends on the selection criteria; and 5) monocentric and polycentric urban models have poor explanatory power for commuting patterns. Numerous studies have examined the location of subcenters and their boundaries by identifying employment centers (Cervero, 1989; Giuliano, Redfearn, Agarwal, Li, & Zhuang, 2007; Giuliano & Small, 1991; Gordon, Richardson, & Wong, 1986; Hu et al., 2018; Lee, 2007; McDonald, 1987; McDonald & McMillen, 1990; McMillen, 2001; McMillen & Lester, 2003). Although various practical approaches have been proposed for identifying employment centers, three types are most widely applied: absolute, relative, and both density criteria (Giuliano et al., 2007; Lee, 2007). The absolute density approach selects employment center candidates using minimum density cutoffs (Anderson & Bogart, 2001; Bogart & Ferry, 1999; Giuliano & Small, 1991; Gordon & Richardson, 1996; Pfister, Freestone, & Murphy, 2000). For example, Giuliano and Small (1991) used two cutoff criteria to identify centers in Los Angeles, USA: a density cutoff of D (10 jobs per acre) and a minimum total employment of E . They selected zones that equal to or exceed and then combined selected zones that were adjacent to each other. They identified a group of zones with total employment equal to or exceeding E (10,000 in inner regions and 7000 in outer regions) as an employment center. Although absolute density criteria are simple and easy to apply, they have been criticized both for being arbitrary and subjective in terms of determining minimum density cutoffs and for failing to take urban spatial context into account. On the other hand, the relative density approach introduces objectivity in defining employment centers by statistically estimating employment density surfaces using employment density functions, including monocentric, polycentric, and nonparametric functions (Craig & Ng, 2001; Gordon et al., 1986; McDonald & Prather, 1994; McMillen,

2.2. The influence of urban form on commuting Several research studies have investigated the relationship between urban land use patterns and mobility. These studies can be classified into various topics, such as trip purpose (e.g., commuting vs. shopping), trip mode (e.g., automobile vs. transit vs. walking), impedance (e.g., time vs. distance vs. cost vs. accessibility), land use and spatial patterns, and level of detail in the data (both spatial and temporal). Of these, land use and spatial patterns can be investigated in a number of ways, ranging from micro- and meso-levels of land use such as neighborhood design and layout, density, and diversity (e.g., mixed use of land vs. job/housing balance) to the macro-level, particularly urban spatial structure (e.g., suburbanization vs. monocentric vs. polycentric). Given the wide scope of literature on the influence of land use on travel, this review focuses on research that has investigated the effects of employment suburbanization and polycentricity on commuting time. As mentioned earlier, one of the most controversial debates on the relationship between urban form and commuting occurs between proponents of the co-location hypothesis and intervention planning. Those who subscribe to the co-location hypothesis believe that market forces can be employed to keep commute times within tolerable limits via the 2

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rational location choices of households and firms. In support of the colocation hypothesis, numerous empirical studies have found that commute time and distance are reduced or stabilized in a decentralized and polycentric urban spatial structure (Anas et al., 1998; Clark & KuijpersLinde, 1994; Crane & Chatman, 2003; Downs, 1992; Giuliano, 1991; Gordon, Richardson, & Jun, 1991; Levinson & Kumar, 1994; Mieszkowski & Mills, 1993; Sultana, 2000). However, the co-location proposition has been countered with contradictory empirical findings. Brotchie, Anderson, Gipps, and McNamara (1996) found substantial variations in mean trip length among U.S. metropolitan areas, arguing that Los Angeles and Boston have shorter trip distances, while Baltimore, San Antonio, and Rochester have longer trip lengths. Rosetti and Eversole (1993) found that average commute times increased between 1980 and 1990 in 35 of the 39 metropolitan areas from US Census data, while Cervero and Wu (1998) reported a rise in average commute time and distance for people working in subcenters in the San Francisco Bay Area, which has exhibited polycentricity since 1980. Similar results have been found in European metropolitan areas. For example, Aguilera (2005) found an increase in average commute distance in French cities as the number of people residing outside subcenters increases, while Schwanen et al. (2004) found longer commute distances and times for auto drivers in most polycentric regions when compared to monocentric urban areas in the Netherlands. Despite the large volume of literature on the relationship between urban form and commuting patterns, several reasons have been proposed for the inconclusive empirical findings. First, it is difficult to untangle the organizing rules regarding the association between urban form and commuting patterns because both components are highly intercorrelated and affected by numerous factors such as the city's historical, political, and socioeconomic situation, transportation system, and/or public intervention and regulation. Moreover, commuting behaviors are affected not only by spatial distribution of employment and population, but also by travel mode and commuters' sociodemographic attributes such as income, gender, and household characteristics (Ma & Banister, 2007). Therefore, substantial differences may exist in commuting patterns even among cities with similar size and distribution of employment and population. The co-location hypothesis is more strongly supported by empirical evidence from cities with higher levels of dispersion (both population and employment) and polycentrism, lower density, and a higher dependence on automobiles, such as in North American cities. This is due to the fact that employment decentralization and polycentricity allows firms to be geographically closer to people, leading to more housing and job options in the suburbs, away from congestion in the central city, ultimately resulting in a reduction in commute length and duration. However, a polycentric transformation may lead to longer commutes, possibly due to an increase in cross commuting because of heterogeneity in the housing and labor markets, limited options for commute modes, and/or public intervention and regulations interfering with the rational location adjustments of households and firms. Second, polycentric transformation comprises a continuing process of employment suburbanization and decentralization. Therefore, differences in the spatial location of suburban centers and in the polycentric phase can lead to conflicting empirical findings. For example, Sultana (2000) found that the distance of subcenters from the central business district (CBD) is negatively associated with mean commuting time in the Atlanta metropolitan area, USA, indicating that dispersed subcenters lead to a shorter commute than subcenters closer to the CBD. On the other hand, based on case studies of French and Chinese metropolitan areas, Aguilera and Mignot (2004) and Hu et al. (2018) reported that subcenters located far from the CBD have a lower job/ housing proximity, resulting in longer commutes. Third, the analytical methods that are used and level of data aggregation have an influence on the empirical findings. Numerous studies have investigated changes in average commute time and/or

distance and degrees of polycentricity over time at the metropolitan level without accounting for other factors that may influence commuting behavior, particularly the individual characteristics of workers. Few studies have examined the relationship between urban form and commuting by incorporating both commuters' personal/household characteristics and macro-level urban form features (Hu et al., 2018; Modarres, 2011; Schwanen et al., 2004). These studies have helped understand the effects of temporal changes in spatial evolution on commuting patterns by connecting the evolution of urban spatial structure with individual-level commuting data while accounting for commuters' sociodemographic factors. However, they have not considered temporal changes in individual-level commuting behavior that arise due to urban spatial evolution as only one-time individual-level commuting information was used for the analyses. The present study can be distinguished from earlier urban spatial feature research in several ways. First, to the best of author's knowledge, this study is the first to examine the dynamic relationship between subcenter emergence and change in commuting patterns. By examining this relationship, we can better understand the complex links between polycentric transformation and commuting behavior over time. Commuting behavior constantly changes in relation to the dynamic distribution of employment. Moreover, transportation system, commuters' sociodemographic attributes, commute time, or length for a subcenter fluctuates over time even within a metropolitan area, as subcenters have different locations and phases of spatial transformation. Second, as a methodological improvement, this study considers both changes in urban form and the sociodemographic factors of commuters, to investigate the effects of employment subcenters on commuting time. Multilevel random effects models are utilized for this purpose. A multilevel model is advantageous in that it can control unobserved zonal characteristics and capture both individual- and zonal-level influences on commute times. Third, the selection of Seoul as the case study area could be an important factor as it moves away from the existing literature. Seoul's development has been characterized as compact, mixed use, and transit-oriented, with extensive subways and bus rapid transit (BRT) as well as strict land use regulations, such as its greenbelt policy. It will, therefore, be interesting to investigate the association between polycentricity and commuting for a city with different urban characteristics than Western cities. This research is made possible due to the availability of 2000 and 2015 employment data for dong1 that helped identify employment centers in Seoul and the 2002 and 2016 Seoul Household Travel Survey (HTS) data. 3. Study area and data The SMA, the capital region of South Korea, is an ideal testbed for investigating the effects of employment suburbanization on commuting patterns as this area has experienced rapid population and employment growth, as well as suburbanization and decentralization over the last half-century. The SMA is one of the largest and densest cities in the world in terms of population, which increased five-fold over the past half-century, rising from 5.2 million in 1960 to 25.3 million in 2015 (Korean Statistical Information Service: http://kosis.kr). Seoul is the core city of the SMA, housing 39.2% of the SMA population and 47.4% of SMA employment, even though Seoul occupies only 5.1% of the SMA land area, leading to densities of 16,370 people and 7845 jobs per km2 (Korean Statistical Information Service: http://kosis.kr). 1

A dong is the smallest administrative boundary in Korea, equivalent to a Census Tract in the U.S. The SMA had 1133 dong in 2015. Each dong contains, on average, approximately 20,000 residents. The land size of dong varies by location. Dong size in the central city is approximately 1.4 km2, while suburban dongs are approximately 15 km2, on average. 3

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25000

20000

Density (Job/km2)

Year 2000

Year 2015

15000

10000

5000

0 0

5

10

15

20

25

30

35

40

45

Distance to the CBD (Km)

Fig. 1. Change in Employment Density by Distance to the CBD between 2000 and 2015.

The urban spatial structure of the SMA has been affected not only by market forces but also by public policy and intervention. Several government policies and regulations on urban development and land use can be highlighted in determining the current urban form. First, the Korean government has initiated large-scale suburban new town development projects over the last three decades to cope with a serious housing shortage and a great deal of housing-market speculation. The first phase of Seoul's new town project is to construct approximately 300,000 new housing units in five new towns (Bundang, Ilsan, Pyungchon, Sanbon, and Jungdong) in the early 1990s, located approximately 20–30 km from the CBD. After completion of the first phase of the new town project, the second phase of suburban new town development has begun to construct 10 new towns since 2000, located approximately 40–50 km from the CBD and accommodating approximately 590,000 new housing units. Second, the Seoul Metropolitan Government (SMG) introduced the “New Town Project” in 2002, which is a largescale urban regeneration project for rejuvenating old towns, mostly to the north of the Han River, designating 35 neighborhoods with a total area of 27.3 km2. Among these sites, 26 are primarily for residential redevelopment, while 9 renew the old downtown district for commercial use (Jun, Kim, Kim, Yeo, & Hyun, 2017). Third, the greenbelt regulation is regarded as one of the critical factors determining the urban spatial structure of the SMA. In 1971, the national government designated a 10- to 15-km-wide donut-shaped green space surrounding the City of Seoul, as a greenbelt beginning 15 to 20 km from the CBD. The greenbelt clearly divides the SMA into two parts – central city and suburban –, and has contributed to inner city densification and leapfrog development (Bae & Jun, 2003). Fig. 1 illustrates changes in employment density by distance to the CBD between 2000 and 2015. There was a large increase in employment density for areas 5–15 km from the CBD, which is within the central city, and a moderate increase for areas 20–35 km from the CBD. Fig. 2 shows changes in population density by distance to the CBD for the same period. The most significant increase in population density occurred within the 25–32 km range, whereas population density fell in downtown Seoul. These findings indicate that significant employment growth occurred at the edge of the central city, while population growth occurred in suburban areas. The two primary data sources used in this study are 2000 and 2015 employment data by dong, taken from the Korean Statistical

Information Service to identify employment centers and the 2002 and 2016 Seoul Household Travel Survey (HTS) from the Metropolitan Transportation Authority (MTA). These data are then used to analyze the effects of employment centers on commuting patterns. The employment data include number of firms and employment by dong for 2000 and 2015, as classified by the Korean Standard Industrial Classification (KSIC). The HTS data consist of individual trip information, which is made up of 1) household attributes such as housing type, tenure, household income, household size, and automobiles owned, 2) socioeconomic characteristics including gender, age, education level, and occupation, and 3) trip information such as purpose, mode, origin, destination, and duration. This study used dong boundaries to conduct density analysis and identify the origin and destination of commuting trips. 4. Empirical results 4.1. Identification of Employment Centers in Seoul. This study used a two-stage approach to identify employment centers in the SMA. In the first stage, we followed Giuliano and Small's (1991) approach for selecting employment center candidates using employment density cutoffs at a minimum total employment of 15,000 employees per km2 (10,000 for outer cities) and 30,000 per km2 (20,000 for outer cities) due to Seoul's urban compactness. We first identified 17 zones for 2000 and 36 zones for 2015 that met the total employment and density cutoffs. We then grouped adjacent multiple zones or single zones as employment center candidates based on spatial proximity and administrative district2 and identified six center candidates for 2000 and nine for 2015 (Fig. 3). In the second stage, we used Small and Song's (1994) polycentric density function to statistically estimate the employment density surfaces as follows: N

Dm =

An e n=1

bn rmn + vm ,

m = 1, 2, 3. …M

(2)

where N = number of employment centers, rmn=distance between zone m and center n, and vm = error term for density associated with 2

4

The SMA had 66 administrative autonomous districts (‘si-gun-gu’) in 2015.

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25000

Density (person/km2)

20000 Year 2000

Year 2015

15000

10000

5000

0 0

5

10

15

20

25

30

35

40

45

Distance to the CBD (Km)

Fig. 2. Change in Population Density by Distance to the CBD between 2000 and 2015.

2000

2015

Fig. 3. Employment Center Candidates identified by the Employment Density Cutoffs.

zone m. We tested several polycentric density functions with the center candidates identified in the first stage using nonlinear least squares estimation. Our findings demonstrated that all estimates for four centers in 2000 and for seven centers in 2015 were statistically significant at the 0.05 level, as shown in Table 1 and Fig. 4. The overall performances of the nonlinear least square models are moderate with an R2 of 0.56 for the 2000 model and 0.53 for the 2015 model. The model results indicate that three new subcenters emerged in the south of Seoul over the 2000–2015 period. It is interesting to find that the CBD3 had the highest intercept and gradient values for both years and the highest statistical significance, implying that the CBD maintains the highest job concentration and asserts metropolitan-wide economic dominance. An interesting finding was that Gangnam played an important role in the polycentric spatial transformation as the second most prominent employment center. Gangnam had the second largest intercept and gradient values, apart from two outlying subcenters (Anyang and Suwon) whose

Table 1 Job density gradients generated by the polycentric density function.

3 In Seoul, the CBD is the historic city center and holds the modern financial and economic district and the premier shopping area, located in Jung-gu and Jongno-gu.

*Estimate is statistically significant at the 0.05 level.

Center ID

1. CBD 2. Gangnam 3. Yeongdeungpo 4. Bucheon 5. Anyang 6. Bundang 7. Suwon N R2

5

Parameter

Intercept Gradient Intercept Gradient Intercept Gradient Intercept Gradient Intercept Gradient Intercept Gradient Intercept Gradient

2000

2015

Coef. value

t-Value

Coef. value

t-Value

83,151.4 0.851 45,869.8 0.633 11,267.6 0.509 5402.8 0.024 – – – – – – 1133 0.560

21.66* 23.72* 12.64* 13.16* 3.07* 3.70* 12.31* 6.60* – – – – – –

102,452.0 0.946 56,422.4 0.629 16,210.4 0.422 4889.4 0.049 14,696.2 0.827 5926.5 0.041 13,152.8 0.861 1133 0.534

20.07* 21.64* 12.09* 12.23* 4.64* 4.92* 5.59* 2.11* 2.85* 2.64* 5.62* 2.85* 2.46* 2.22*

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2000

2015

Fig. 4. Employment Centers identified by the Polycentric Density Function.

increased by approximately 2-fold, while automobile share declined from 62% to 53%, and public transit (bus and subway) share increased from 37% to 47%. Jobs/resident ratio within residential zones increased from 0.37 to 0.46, whereas the ratio within workplaces declined from 2.59 to 1.87. On the other hand, population density within residential zones decreased by 17.4%, while employment density within workplaces increased by 16%. Lastly, the share of commuting trips to the CBD declined slightly from 6% to 5%, while the share to subcenters increased from 7% in 2002 to 11% in 2016. Table 3 shows the multilevel regression results for 2002, 2016, and a pooled model to determine the effects of the explanatory variables on commuting time. The intraclass correlation coefficient (ICC) indicates how much of the total variation in commute time is accounted for by commuter's residence. The model results demonstrate that 7.0% and 10.7% of the variability in commute time were accounted for by the commuter's residential zone in 2002 and 2016, respectively, indicating an increase in the zonal effects on commute times. These results are consistent with Schwanen et al. (2004), who concluded that the structure of a metropolis, despite significantly influencing commuting patterns, explains only a small component of the variation in individuals' commuting behavior. The results demonstrate that household heads, female workers, and commuters with infants were likely to have shorter commute times, while commuters from higher-income households and larger household sizes and those residing in apartments tended to have a longer commute. Public transit commuters were likely to have longer commute times, apart from bus passengers in 2002, whose commute times were shorter than those of automobile drivers. As we expected, commuters living in a zone with more jobs per person were likely to have shorter commutes, while those working in job-rich zones tended to have longer commutes. Likewise, commuters working in a zone with high employment density were likely to have longer commute times. It was interesting to find that, in 2002, commuters working in the CBD and inner-city subcenters had commute times that were 10.6% and 25.4% longer, respectively, compared to the reference group (noncenter commuters), while those working in suburban subcenters tended to have times that were shorter by 25.7%. However, commuters working in suburban centers were likely to have longer commutes (by 1.9%) in 2016. This finding indicates that the geographic influence of suburban centers became wider over the 15-year period, attracting more distant workers. The results from the pooled model statistically support this argument. No significant changes were found in the commute times of CBD commuters, with the interaction variable between CBD commuters and year being statistically insignificant. However, the interaction variable between inner-city subcenter commuters and year was significantly

gradients were steeper. It was also found that the three newly emerged subcenters outside of Seoul had a local influence on the employment density surface with lower intercept values and/or steeper gradients. 4.2. The effects of employment center evolution on commuting To estimate the influence of employment center evolution on commuting patterns, we first combined individual commuting data from the HTS with employment center data. Because origin and destination zones were recorded in the data, it was possible to determine whether a commuter's workplace was located in an employment center. Second, we used multilevel models to investigate how the emergence of new subcenters affected commuting patterns. We built two-level models because the data set contained information about individual commuters nested within the zones. The natural logarithm of commute time was used as the dependent variable in the multilevel models. We included explanatory variables such as the socioeconomic features of commuters, commute modes, characteristics of residential and work places, and center-related dummy variables, because theoretical and empirical studies have suggested that these variables are key determinants of commuting time. Socioeconomic variables include age, gender, occupation, household size and income, number of infants, car ownership, and housing type, while commute modes such as bus and subway are included as dummy variables. Job/housing balance at residence and workplace and population density within residential zones and employment density within workplaces were included as zonal characteristics. Finally, we classified the identified employment centers into three types (the CBD, inner-city subcenter within the central city, and suburban subcenter), and included three employment center dummy variables in the multilevel models to test whether center type affects commute patterns. We first built two multilevel models for 2002 and 2016. We then built a pooled multi-level model by combining observations for both years with year dummy interaction variables by center type to capture the interaction between center type and year. As Jaccard and Turrisi (2003) have argued, it is possible to statistically test whether changes in commute time vary by center type when the type of year interaction variables are used. Table 2 shows summary statistics of the dependent and explanatory variables used in the multilevel regression models for 2002 and 2016. The average commute time increased by 10.5%, from 40.6 min in 2002 to 44.8 min in 2016. The average age of the sampled commuters was 41 in 2002 and 44 in 2016, while the share of female commuters substantially increased from 13% to 33% over the same period. The share of apartment residents also increased from 52% to 60%, while household size decreased from 3.79 to 3.11. Average household income 6

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Table 2 Descriptive Statistics for the Variables Used in the Multilevel Models. Variable

2002

Commute time (minutes) Commuter age Gender (1 if female, 0 otherwise) Housing type (1 if apartment, 0 otherwise) Household head (1 if head, 0 otherwise) Household size Number of infants under the age of 5 Car ownership (1 if own, 0 otherwise) Household income (million won/month) Commute by car Commute by bus Commute by subway Job/resident ratio within the residential zone Job/resident ratio within the workplace Population density within the residential zone (persons/km2) Employment density within the workplace (job/km2) Commute to the CBD Commute to a subcenter within Seoul Commute to a suburban subcenter N

2016

Mean

S.D

Mean

S.D

40.58 40.95 0.13 0.52 0.81 3.79 0.35 0.84 225.86 0.62 0.22 0.15 0.37 2.59 18,941.35 10,262.60 0.06 0.06 0.01 77,878

23.73 9.63 0.34 0.50 0.39 1.02 0.63 0.37 101.97 0.48 0.42 0.36 1.50 7.95 13,625.38 14,653.09 0.25 0.23 0.08

44.83 43.85 0.33 0.60 0.62 3.11 0.20 0.84 460.18 0.53 0.28 0.19 0.46 1.87 15,650.32 11,906.67 0.05 0.09 0.02 98,368

25.20 11.48 0.47 0.49 0.49 1.07 0.50 0.37 223.33 0.50 0.45 0.39 1.21 5.32 12,263.15 17,474.73 0.21 0.29 0.15

Table 3 Multilevel model results. 2002

Intercept Commuter age Commuter age2 Gender (1 if female) Household head (1 if head, 0 otherwise) Housing type (1 if apartment, 0 otherwise) Household income (million Won/month) Household size Car ownership (1 if own, 0 otherwise) Number of infants under the age of 5 Commute by bus (ref.: car) Commute by subway (ref.: car) Job-resident ratio within the residential zone Job-resident ratio within the workplace Log (Population density) within the residential zone Log (Employment density) within the workplace Commute to the CBD Commute to an inner-city subcenter Commute to a suburban subcenter Year dummy Commute to the CBD*Year Commute to an inner-city subcenter*Year Commute to a suburban subcenter*Year N Intraclass correlation coefficient (ICC) −2 Log likelihood AIC

2016

Pooled model

β

t-Value

β

t-Value

β

t-Value

3.148 −0.007 1.E-04 −0.097 −0.018 0.016 0.007 0.008 −4.E-04 −0.016 −0.032 0.275 −0.008 0.003 0.036 0.012 0.106 0.254 −0.257 – – – – 77,878 0.07 140,467.1 140,485.1

61.4 −4.0⁎⁎⁎ 3.6⁎⁎⁎ −11.9⁎⁎⁎ −2.1⁎⁎ 2.6⁎⁎ 1.4 3.4⁎⁎⁎ −0.1 −4.0⁎⁎⁎ −4.0⁎⁎⁎ 30.5⁎⁎⁎ −2.6⁎⁎⁎ 7.0⁎⁎⁎ 11.5⁎⁎⁎ 7.4⁎⁎⁎ 8.0⁎⁎⁎ 25.6⁎⁎⁎ −9.4⁎⁎⁎ – – – –

3.402 0.001 −3.E-05 −0.112 −0.015 0.019 0.009 0.004 −0.006 −0.006 0.472 0.736 −0.012 0.003 −0.010 0.005 0.069 0.146 0.019 – – – – 98,368 0.107 125,358.1 125,376.1

82.0 0.8 −2.9⁎⁎⁎ −23.7⁎⁎⁎ −3.6⁎⁎⁎ 5.1⁎⁎⁎ 2.7⁎⁎⁎ 2.4⁎⁎ −1.3 −1.7⁎ 71.5⁎⁎⁎ 87.8⁎⁎⁎ −3.8⁎⁎⁎ 6.9⁎⁎⁎ −2.8⁎⁎⁎ 3.8⁎⁎⁎ 6.4⁎⁎⁎ 24.3⁎⁎⁎ 1.8⁎ – – – –

3.196 −0.001 −1.E-05 −0.102 −0.012 0.020 0.008 0.006 −0.004 −0.012 0.237 0.537 −0.009 0.003 0.016 0.008 0.087 0.252 −0.255 0.031 −0.011 −0.104 0.274 176,246 0.102 274,919.3 274,939.3

108.1⁎⁎⁎ −1.1 −1.1 −23.5⁎⁎⁎ −2.8⁎⁎⁎ 6.2⁎⁎⁎ 2.7⁎⁎⁎ 4.3⁎⁎⁎ −0.8 −4.6⁎⁎⁎ 36.8⁎⁎⁎ 80.7⁎⁎⁎ −4.5⁎⁎⁎ 10.1⁎⁎⁎ 7.6⁎⁎⁎ 7.4⁎⁎⁎ 8.2⁎⁎⁎ 29.3⁎⁎⁎ −10.5⁎⁎⁎ 4.1⁎⁎⁎ −0.9 −10.1⁎⁎⁎ 10.1⁎⁎⁎

⁎⁎⁎

⁎⁎⁎

***p < 0.01, **p < 0.05, *p < 0.1.

negative, while the interaction between suburban subcenter commuters and year was significant positive. It was found that the commute times of inner-city subcenter commuters fell by 10.4%, while those of suburban subcenter commuters increased by 27.4% compared to the reference group during the 2002–2016 period. These findings are consistent with Aguilera and Mignot (2004) and Hu et al. (2018), who argued that subcenters further away from the CBD lead to a longer commute than subcenters closer to the CBD.

5. Conclusion Although a large volume of research has been conducted over the last three decades to empirically investigate the effect of urban form on commuting time, little attention has been paid to understanding the dynamic relationship between changes in urban spatial structure and commuting patterns. This study sought to contribute to this literature by investigating the dynamic association between the evolution of urban spatial structure and commuting patterns in the SMA, while explicitly considering temporal changes in both urban spatial structure and commuters' sociodemographic attributes. This approach helps 7

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differentiate the impact on commuting due to polycentric evolution and other similar factors. As such, a number of notable empirical findings were uncovered. First, the SMA experienced job decentralization and polycentricity over the 15-year period of the analysis, but the number of employment centers was substantially lower than that reported for Western cities: 32 centers in Los Angeles (Giuliano & Small, 1991), 15 centers in Chicago (McMillen & McDonald, 1998), and 22 centers in the San Francisco Bay Area (Cervero & Wu, 1998). Second, as in many Western cities (Anas et al., 1998), the CBD maintained metropolitan-wide dominance and the highest job density, despite the emergence of new suburban subcenters. Also, similar to Western cities, those commuting to employment centers were likely to have longer commutes than those commuting to non-centers. The most noticeable finding of this study is that the empirical results have mixed implications for commuting efficiency. On the positive side, inner-city subcenter commuters have significantly reduced their commute times over the last 15 years, while on the negative side, newly emerged suburban subcenter commuters have longer commute times. Several arguments can be made to explain these distinctive results. First, the descriptive statistics demonstrate that commute time reduction of inner-city subcenter commuters is related to a reduction in their commuting distance reduction by 5.1%, from 12.7 km in 2002 to 12.1 km in 2016. A second explanation for the improvements in commute times could be that access to public transit (i.e., bus and subway) within inner-city subcenters increased. According to HTS data, the use of cars by inner-city subcenter workers was found to decrease from 56.8% in 2002 to 39.0% in 2016, while the use of bus and subway increased from 17.5% to 19.9% and 25.8% to 41.2% over the same period, respectively. A third reason for the reduction in commuting times could be the emergence of new suburban subcenters. As shown in Fig. 4, new suburban subcenters are likely to draw workers who had previously traveled to inner-city subcenters, reducing the number of commuters traveling from distant areas to inner-city subcenters. However, despite this promising improvement in commuting times for inner-city subcenter commuters, the emergence of new suburban subcenters increased the commute times for other commuters. Commute times for suburban subcenter commuters increased by 56% (30.4 min in 2002 to 47.4 min in 2016) and commuting distance increased by 84% (5.0 km in 2002 to 9.2 km in 2016) between 2002 and 2016, indicating that the new suburban subcenters attracted workers from wider job markets. The longer commutes to new suburban subcenters can be explained by a job/housing mismatch and higher dependence on cars in suburban areas. Suburban areas (Gyeonggi Province and Incheon City) were found to account for 53.7% of the SMA population in 2000, increasing to 60.8% in 2015, while they only accounted for 46.7% of jobs in 2000, increasing to 53.2% in 2015. In addition, the high dependence on cars and the resulting congestion also contributes to longer commutes for new suburban subcenter residents. According to HTS data from 2016, car and bus use (54.1% and 33.5%, respectively) among these commuters was significantly higher than among inner-city subcenter commuters (39% and 20%, respectively). Mixed policy implications can be drawn from the empirical findings. The evidence suggests that higher-density and mixed land use for development and improvements in public transit accessibility are important drivers of commuting efficiency because these factors contributed to a reduction in the commute distances and times of those working in inner-city subcenters. Unlike strict zoning regulations, a higher level of mixed land use can expedite locational adjustment by offering jobs that are closer to residential areas or vice-versa. Higherdensity development makes public transit economically feasible by attracting higher ridership. Improving public transport directly leads to shorter commute times in a high-density urban area like Seoul due to higher line-haul speed and more mode and route choices. On the other hand, public intervention in urban development and

land use in Seoul contributes to the longer commute of suburban subcenter commuters, as large-scale suburban new town development and greenbelt regulation surrounding the central city distort the real estate market and aggravate the job–housing balance. However, longer commutes in new suburban subcenters could also be transitional, as it takes time for households and firms to align their locations with newly emerged subcenters, and for the government to link transit services with these subcenters. Future research should thus determine how households and firms align locations and the consequence of this alignment on the commuting patterns of suburban subcenter workers when new suburban subcenters mature. More empirical studies need to be conducted to compare the relationship between polycentricity and commuting patterns while considering dynamic polycentric processes, urban land use and transport systems, and commuters' sociodemographic attributes. CRediT authorship contribution statement Myung-Jin Jun:Conceptualization, Methodology, Software, Data curation, Writing - review & editing. References Aguilera, A. (2005). Growth in commuting distances in French polycentric metropolitan areas: Paris, Lyon and Marseille. Urban Studies, 42(9), 1537–1547. Aguilera, A., & Mignot, D. (2004). Urban sprawl, polycentrism and commuting. A comparison of seven French urban areas. Urban Public Economics Review, 1(1), 93–114. Anas, A., Arnott, R., & Small, K. A. (1998). Urban spatial structure. Journal of Economic Literature, 36, 1426–1464. Anderson, N. B., & Bogart, W. T. (2001). The structure of sprawl: Identifying and characterizing employment centers in polycentric metropolitan areas. American Journal of Economics and Sociology, 60, 147–169. Antipova, A., Wang, F., & Wilmot, C. (2011). Urban land uses, sociodemographic attributes and commuting: A multilevel modeling approach. Applied Geography, 31(3), 1010–1018. Bae, C. H. C., & Jun, M. J. (2003). Counterfactual planning: What if there had been no greenbelt in Seoul? Journal of Planning Education and Research, 22, 374–383. Bogart, W. T., & Ferry, W. C. (1999). Employment centres in Greater Cleveland: Evidence of evolution in a formerly monocentric city. Urban Studies, 36, 2099–2110. Brotchie, J. F., Anderson, M., Gipps, P. G., & McNamara, C. (1996). Urban productivity and sustainability: Impacts of technological change. In Y. Hayashi, & J. Roy (Eds.). Transport, land-use and the environment (pp. 81–99). Boston, MA: Kluwer Academic Publishers. Cervero, R. (1989). America’s suburban centers: The land use-transportation link. Routledge. Cervero, R., & Wu, K.-L. (1998). Sub-centring and commuting: Evidence from the San Francisco Bay Area, 1980–90. Urban Studies, 35, 1059–1076. Clark, W. A., & Kuijpers-Linde, M. (1994). Commuting in restructuring urban regions. Urban Studies, 31, 465–483. Craig, S. G., & Ng, P. T. (2001). Using quantile smoothing splines to identify employment subcenters in a multicentric urban area. Journal of Urban Economics, 49, 100–120. Crane, R., & Chatman, D. (2003). Traffic and sprawl: Evidence from US commuting, 1985 to 1997. Planning and Markets, 6, 1–21. Downs, A. (1992). Stuck in traffic: Coping with peak-hour traffic congestion. Washington, DC: The Brookings Institution. Ewing, R. (1997). Is Los Angeles–style sprawl desirable? Journal of the American Planning Association, 63, 107–126. Ewing, R., & Hamidi, S. (2015). Compactness versus sprawl: A review of recent evidence from the United States. Journal of Planning Literature, 30(4), 413–432. Giuliano, G. (1991). Is the journey to work explained by urban structure? Transportation Research Record, 1305, 305–312. Giuliano, G., Redfearn, C., Agarwal, A., Li, C., & Zhuang, D. (2007). Employment concentrations in Los Angeles, 1980–2000. Environment & Planning A, 39(12), 2935–2957. Giuliano, G., & Small, K. A. (1991). Subcenters in the Los Angeles region. Regional Science and Urban Economics, 21(2), 163–182. Giuliano, G., & Small, K. A. (1993). Is the journey to work explained by urban structure? Urban Studies, 30(9), 1485–1500. Gordon, P., & Richardson, H. W. (1996). Beyond polycentricity: The dispersed metropolis, Los Angeles, 1970–1990. Journal of the American Planning Association, 62(3), 289–295. Gordon, P., & Richardson, H. W. (1997). Are compact cities a desirable planning goal? Journal of the American Planning Association, 63, 95–106. Gordon, P., Richardson, H. W., & Jun, M. J. (1991). The commuting paradox: Evidence from the top twenty. Journal of the American Planning Association, 57(4), 416–420. Gordon, P., Richardson, H. W., & Wong, H. L. (1986). The distribution of population and employment in a polycentric city: The case of Los Angeles. Environment and Planning A, 18, 161–173. Heikkila, E., Gordon, P., Kim, J. I., Peiser, R. B., Richardson, H. W., & Dale-Johnson, D. (1989). What happened to the CBD-distance gradient? Land values in a polycentric

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