Measuring The Performance of Metro-Based Transit Oriented Development (TOD): A Comparative Study between and

by

Yiling Xie

B.E. in Urban Planning B.A in Economics Peking University, Beijing, (2015)

Submitted to the Department of Urban Studies and Planning In partial fulfillment of the requirements for the degree of

Master in City Planning At the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2017 @2017 Massachusetts Institute of Technology. All rights reserved.

Signature of Author: Signature redacted Department of Ur an Sudies 'd Planning redacted August 10, 2017 Certified by: Signature Associate Profes'&r P.hristopher Zegras Departme t of 9rban tudies and Planning redacted Thesis Supervisor Accepted by: Signature Associate Professor . Christopher Zegras MASSACHUSETTS INSTITUTE C r, MCP Committee OF TECHNOLOGY Department of Urban Studies and Planning SEP 267017 LIBRARIES ARCHIVES 2 Measuring The Performance of Metro-Based Transit Oriented Development (TOD): A Comparative Study between Beijing and Singapore By Yiling Xie Submitted to the Department of Urban Studies and Planning on August 10, 2017, in partial fulfillment of the requirements for the degree of Master in City Planning Abstract

Transit oriented Developments (TOD) is a package that consists of urban form and development strategies that aims to foster efficient land use patterns to create sustainable neighborhoods. By using public transit as a focal point to create attractive environments, TOD is a promising approach to tackle urban issues such as traffic congestion, ineffective land use, and air pollution in metropolises like Beijing. Beijing, and even the entire country of China, is at its 'Golden Age' of metro system expansion. As metro systems have the capacity to reshape urban mobility and urban form, they are considered one of the key components for TOD and thus pose transformational opportunities for cities to embrace better TOD. Yet to provide suggestions to future TOD implementation, we need to first evaluate the performance of existing TOD, including outputs and outcomes of TOD. This thesis examines TOD in the Chinese context, specifically, Beijing, and compares it to Singapore, a well-recognized TOD city in Asia with comparable density to Beijing.

In this thesis, I see the outputs of TOD as a spectrum of built environment indicators that characterize TOD, and the outcome of TOD as increased metro ridership. I constructed a TOD evaluation framework that contains indicators of density, diversity, design and connectivity and measures the TOD-ness of metro station area. I find that Singapore has an overall higher TOD score across its MRT station areas than Beijing, with both cities displaying better TOD-ness in the inner city areas. Yet the spatial distribution of TOD-ness is more monocentric in Singapore in the design, diversity and connectivity dimensions. I then used the TOD indicators and station-level ridership to estimate direct ridership models for both cities. I find evidence that built environment indicators, such as population density, ground-floor retail density and number of metro entrances, are positively associated with ridership. The Singapore models, overall, have a better fit than the Beijing models. I conclude by summarizing the work, identifying some challenges to improving TOD performance in Beijing, and identifying areas for future work.

Keywords: Transit oriented Developments, Beijing, Singapore, built environment, indicator framework, metro station, ridership

Thesis Supervisor: P. Christopher Zegras, Associate Professor of Urban Studies and Planning

Thesis Reader: Joseph Ferreira, Professor of Urban Studies and Planning

3 Acknowledgement

First and foremost, this thesis would not have been possible without the support of my advisor Professor Chris Zegras. Throughout my two years at MIT, Chris has been a fount of wisdom, knowledge and insights. Thank you Chris for constantly helping me shape my vague ideas and move toward the right direction. I am also very grateful to my thesis reader, Professor Joseph Ferreira, who has offered invaluable help on research methodology and insightful comments to my thesis. Thank you Joe for patiently guiding me through the statistical knowledge, data processing and model construction.

Additionally, I would like'to thank Samuel Tak Lee lab for providing STL Grant to our research project- Implementing TOD in China:from The Supply Perspective. This thesis is part of the project outcome. I really appreciate that STL lab not only offered financial support but also gave great comments and data support to this thesis.

I also recognize all the support I have received from my colleagues at China Sustainable Transportation Center (CSTC), who kindly directed me to the proper interviewees and offered great help in data collection. I had great time during my last summer internship at CSTC.

I would also like to thank my colleagues at the TOD office for generously providing valuable comments on my research and thesis. In particular, thank you Erin Kenney, an amazing research partner and great friend.

I want to thank my DUSP classmates. It has been great honor spending the past two year with a group of passionate, motivated and friendly young minds. I want to also thank all friends that I have met at MIT.

Finally, I would love to thank my parents for the unconditional love and help, especially during my most difficult time at MIT. Without your encouragement and support, none of this would be possible.

4 Table of Contents

Acknow ledgem ent...... 4

Table of Contents...... 5

List of Figures ...... 8

List of Tables ...... 10

1. Introduction ...... 11

1.1 Introduction and M otivation ...... 11

1.2 Research Questions ...... 13

1.3 Research M ethod ...... 14

1.3.1 TOD indicator fram ework ...... 14

1.3.2 M ultivariate regression ...... 15

1.3.3 Com parative statistical analysis ...... 15

1.4 Thesis Structure ...... 16

2 Literature Review ...... 17

2.1 The Definition of TOD ...... 17

2.2 Scale of TOD...... 18

2.3 M easurem ent of TOD ...... 20

2.4 Benefits of TOD...... 21

2.5 TOD and Travel Behavior ...... 23

3 Context of Beijing and Singapore...... 25

3.1 The city of Beijing ...... 26

3.1.1 Beijing Overview ...... 26

3.1.2 Beijing Subw ay...... 27

3.2 The city of Singapore ...... 29

3.2.1 Singapore Overview ...... 29 5 3.2.2 Singapore M RT...... 31

3.2.3 Singapore TOD...... 33

4 TOD Indicator Fram ew ork...... 37

4.1 TOD Area...... 37

4.2 TOD Indicators ...... 38

4.2.1 Density Indicators...... 43

4.2.2 Diversity Indicators...... 43

4.2.3 Design Indicators...... 43

4.2.4 Connectivity...... 44

4.3 Com posite TOD Index...... 45

4.4 TOD Score Result ...... 48

4.5 Sum m ary...... 57

5 Station-level Ridership ...... 58

5.1 Ridership Data ...... 58

5.1.1 Spatial Distribution of Ridership...... 59

5.1.2 Transform ation of Ridership...... 61

5.2 Ridership and TOD Outputs...... 62

5.2.1 OLS M odel...... 63

5.2.2 Spatial M odels...... 70

5.2.3 Cross Validation and M odel Com parison ...... 80

5.3 Sum m ary...... 90

6 Conclusion and Discussion...... 92

6.1 TOD Perform ance...... 92

6.2 Challenges to TOD in Beijing...... 94

6.3 Lim itations and Future W ork...... 95

6 R e fe re n ce ...... 9 7

A p p e n d ix ...... 1 0 2

A. Stations Analyzed in Beijing ...... 102

B. Stations Analyzed in Singapore...... 112

C. Spatial Autocorrelation Report for Beijing: log (Ridership) ...... 116

D. Spatial Autocorrelation Report for Singapore log (Ridership) ...... 117

E. Spatial Autocorrelation Report for Beijing: OLS M odel2 Residual ...... 118

F. Spatial Autocorrelation Report for Singapore: OLS M odel2 Residual...... 119

G. TOD Challenges in Practice...... 120

H. Interview Guidebook ...... 124

1. Interview List ...... 125

7 List of Figures

FIGURE 1-1 TRAFFIC CONGESTION IN BEIJING (XINHUA, 2014) 12

FIGURE 3-1 METRO SYSTEM IN BEIJING(LEFT) AND SINGAPORE(RIGHT) 26

FIGURE 3-2 BEIJING ADMINISTRATIVE DIVISIONS 27 FIGURE 3-3 THE RING AND RADIUS STRUCTURE IN BEIJING (GOOGLE MAP, 2017) 27

FIGURE 3-4 MAP (BEIJING SUBWAY, 2017) 29

FIGURE 3-5 THE PLANNING AREA OF SINGAPORE (SINGAPORE URBAN REDEVELOPMENT AUTHORITY) 30

FIGURE 3-6 THE DISTRIBUTION OF PLANNED COMMERCIAL CLUSTERS AND INDUSTRIAL PARKS

BY 2030 (URA 2014 PLAN) 31 FIGURE 3-7 SINGAPORE RAIL SYSTEM MAP (SINGAPORE LAND TRANSPORT AUTHORITY, 2017) 32

FIGURE 3-8 SINGAPORE CONSTELLATION PLAN 1991 (URBAN REDEVELOPMENT AUTHORITY) 34 FIGURE 3-9 DISTRIBUTION OF NEW TOWNS AND MRT INFRASTRUCTURE (YANG AND LEW,

2009 ,COMPILED FROM HDB ANNUAL REPORTS 1960-2005) 35 FIGURE 4-1 EXAMPLE OF 800M EUCLIDIAN TOD AREA IN BEIJING 38 FIGURE 4-2 TOD SCORE OF EACH METRO STATION IN BEIJING 51 FIGURE 4-3 DENSITY, DIVERSITY, DESIGN AND CONNECTIVITY SCORE IN BEIJING 52 FIGURE 4-4 TOD SCORE OF EACH METRO STATION IN SINGAPORE 54 FIGURE 4-5 DENSITY, DIVERSITY, DESIGN AND CONNECTION SCORE IN SINGAPORE 55 FIGURE 4-6 DISTRIBUTION OF HDB TOWNS (HDB 2011ANNUAL REPORT) 56

FIGURE 4-7 SINGAPORE MASTER PLAN 2008 (SINGAPORE URBAN REDEVELOPMENT

AUTHORITY) 56 FIGURE 5-1 BEIJING AVERAGE WEEKDAY METRO STATION BOARDING (DEC, 2015) 60 FIGURE 5-2 SINGAPORE AVERAGE WEEKDAY METRO STATION BOARDING (APRIL, 2012) 61 FIGURE 5-3 ORIGINAL DISTRIBUTION AND LOGARITHMIC TRANSFORMATION FOR WEEKDAY-

BOARDING RIDERSHIP IN SINGAPORE 62

8 FIGURE 5-4 ORIGINAL DISTRIBUTION AND LOGARITHMIC TRANSFORMATION FOR WEEKDAY-

BOARDING RIDERSHIP IN BEIJING 62 FIGURE 5-5 HISTOGRAM OF THE NUMBER OF NEIGHBORS (BEIJING) 70 FIGURE 5-6 HISTOGRAM OF THE NUMBER OF NEIGHBORS (SINGAPORE) 71 FIGURE 5-7 THE OVERLAPPED BUFFER IN THE INNER CITY OF BEIJING 77 FIGURE 5-8 BEIJING RIDERSHIP PREDICTION USING BEIJING OLS MODEL 83 FIGURE 5-9 BEIJING RIDERSHIP PREDICTION USING BEIJING SLM MODEL 84 FIGURE 5-10 BEIJING RIDERSHIP PREDICTION USING SINGAPORE OLS MODEL 85 FIGURE 5-11 SINGAPORE RIDERSHIP PREDICTION USING SINGAPORE OLS MODEL 87 FIGURE 5-12 SINGAPORE RIDERSHIP PREDICTION USING SINGAPORE SEM MODEL 88

FIGURE 5-13 SINGAPORE RIDERSHIP PREDICTION USING BEIJING OLS MODEL 89

FIGURE 0-1 MORAN I TEST RESULT FOR BEIJING LOG (RIDERSHIP) 116 FIGURE 0-2 MORAN I TEST RESULT FOR SINGAPORE LOG (RIDERSHIP) 117 FIGURE 0-3 MORAN'S TEST RESULT FOR BEIJING SIMPLE MODEL 3 RESIDUAL 118 FIGURE 0-4 MORAN'S TEST RESULT FOR SINGAPORE SIMPLE MODEL 3 RESIDUAL 119 FIGURE 0-5 THE SPATIAL AND PROFESSION DISTRIBUTION OF INTERVIEWEES 120

9 List of Tables

TABLE 2-1 CLASSES AND RECIPIENTS OF TOD BENEFITS (CERVERO ET AL. 2004), ...... 22

TABLE 2-2 RELATIVE FREQUENCY OF TRANSIT-AGENCY TARGETS FOR TOD PROJECTS (CHEN, 2010, CITING CERVERO ET AL. 2004)...... 23

TABLE 3-1 SUMMARY OF KEY CHARACTERISTICS IN BEIJING AND SINGAPORE ...... 25

TABLE 4-1 INDICATOR SYSTEM FOR EVALUATING TOD (JIANG ET AL, 2016)...... 40

TABLE 4-2 TOD INDICATORS AND DATA SOURCE ...... 41 TABLE 4-3 SUMMARY OF STATISTICS OF TOD INDICATORS ...... 44

TABLE 4-4 TOD INDICATORS AND CORRESPONDING WEIGHT ...... 47 TABLE 4-5 SUM MARY OF TOD SCORE STATISTICS...... 48

TABLE 5-1 STATISTICS OF WEEKDAY METRO STATION BOARDING ...... 59

TABLE 5-2 VIF FO R O LS M O DELS(1) ...... 65 TABLE 5-3 OLS MODEL RESULTS FOR BEIJING: DEPENDENT VARIABLE: LOG (RIDERSHIP)..... 67 TABLE 5-4 OLS MODEL RESULTS FOR SINGAPORE: DEPENDENT VARIABLE: LOG (RIDERSHIP).... 69

TABLE 5-5 SPATIAL MODEL RESULTS FOR BEIJING DEPENDENT VARIABLE: LOG (RIDERSHIP) .... 74

TABLE 5-6 SPATIAL MODEL RESULTS FOR SINGAPORE: DEPENDENT VARIABLE: LOG (RIDERSHIP)

...... 7 6 TABLE 5-7 SUMMARY OF RELATIONSHIPS BETWEEN TOD INDICATORS AND METRO RIDERSHIP ...... 7 9 TABLE 5-8 MEAN SQUARE ERROR OF DIFFERENT MODEL PREDICTIONS...... 81 TABLE 5-9 ABSOLUTE VALUE OF PREDICTION RESIDUALS OF DIFFERENT MODEL PREDICTIONS 81 TABLE 0-1 TOD SCORE AND DAILY STATION BOARDING OF BEIJING ...... 102 TABLE 0-2 TOD SCORE AND DAILY STATION BOARDING OF SINGAPORE ...... 112 TABLE 0-3 GLOBAL MORAN'S I SUMMARY FOR BEIJING LOG (RIDERSHIP)...... 116

TABLE 0-4 GLOBAL MORAN'S I SUMMARY FOR SINGAPORE LOG(RIDERSHIP) ...... 117 TABLE 0-5 GLOBAL MORAN'S I SUMMARY FOR BEIJING OLS MODEL RESIDUAL ...... 118

TABLE 0-6 GLOBAL MORAN'S I SUMMARY FOR SINGAPORE OLS MODEL RESIDUAL ...... 119 TA BLE 0-7 LIST O F IN TERV IEW S ...... 125

10 1. Introduction

1.1 Introduction and Motivation

China is now in the midst of rapid urban expansion. From 1978 to 2014, China's urban population increased from 172.5 million to 731.1 million (China Yearbook, 2015)1, with the rate of urbanization 2 increasing from 17.92% to 53.73%. The increase in urban scale, and related factors such as land reforms, have several consequences - such as the segregation of working places and residences and the marketization of urban land and large-scale suburbanization - which have resulted in many residents living far away from their productive activities. Dependency on motor vehicles is therefore growing at an unprecedented rate. Among motor vehicles, private car ownership has rocketed dramatically from 284,900 in 1985 to 105,016,800 in 2014 (China Yearbook, 2015). China's unprecedented rate of urbanization, motorization, economic growth, and changing consumer expectations pose transformational opportunities and challenges at different scales. Specifically, many Chinese cities face unaffordable housing prices, environmental pollution, traffic congestion, and lack of walkable spaces. First-tier cities, including Beijing, are representative of such problems. According to Foton Chinese Index for Mobility released by Horizon Research Consultancy Group (2009), traffic congestion produced a monthly economic cost of 335.6 RMB for an average Beijing resident, topping those of six other Chinese cities including Shanghai, Guangzhou, Wuhan, Chengdu, Xi'an and .

Multiple approaches have been tried to address the problems brought by China's rapid urbanization and motorization. One idea, gaining some traction is transit-oriented development

1 Online version available at: http://www.stats.gov.cn/tisi/ndsi/2015/indexeh.htm 2 The rate of urbanization in this thesis is measured by population and refers to the percentage of the total population living in urban areas. 11 (TOD). TOD aims to maximize land use-transit synergies, using transit investments and services to guide land development and thereby create sustainable, lively, pedestrian and cycling friendly built environments (CTOD, 2011). Using urban form and design, TOD encourages people to take transit instead of using private cars and, in turn, boosting transit ridership (Singh et al, 2014). TOD has the potential to enhance the financial sustainability of municipalities and public transportation services, while also providing social benefits and private developer returns. Therefore, TOD has been considered as a promising approach to tackle inefficient land use, air pollution and urban congestion.

Figure 1-1 Traffic Congestion in Beijing (Xinhua, 2014) By the end of 2015, 24 cities in mainland China had urban rail systems and a growing number of cities are constructing urban rail infrastructure. By the end of 2016, the National Development and Reform Commission (NDRC) had approved urban rail construction plans in 43 Chinese cities and the total planned mileage reached 8,600 kilometers3 . Although bus (BRT) or even regular buses might also contribute to creating TOD environments, metro

3 This is reported by China Stock (http://news.cnstock.com/industryrdii-201609-3898123.htm ) 12 systems 4 are believed to be more likely to reshape urban mobility and change people's travel behavior due to its large capacity and punctuality5. Therefore, this thesis only considers metro systems and metro-based TOD refers to development surrounding metro stations. In this 'Golden Age' of China's metro system expansion, will TOD be a remedy to China's urban problems such as congestion, environmental degradation and inefficient land use? Originated and implemented in North America, how can the theory of TOD be adjusted to fit in the Chinese context? As many Chinese cities are constructing urban rail systems, can TOD be measured in Chinese cities? Does TOD in China influence urban mobility performance? How does China's TOD compare, internationally? Does the opportunity exist for China to refine its urban structure and to embrace

TOD? What are the challenges to implementing better TOD in China?

1.2 Research Questions

While the questions above are interesting to ask, they are too broad to answer in the scope of a single thesis. To shed light on possible answers, I turn to the Chinese capital, Beijing, home to the nation's oldest metro system. On the one hand, Beijing has a large metro system and is constructing new metro lines at rapid speed. By the end of February 2016, Beijing had 554 kilometers of metro, the second largest system in China (closely following Shanghai). In the process of rapid metro construction, numerous problems related to TOD arise and need attention. On the other hand, while Beijing is a unique city in China given its political status and international influence, the lessons of Beijing are influential nationally and other Chinese cities can learn from Beijing. Therefore, this thesis uses Beijing as an example to study Chinese cities. More specifically, the thesis aims to answer the following questions:

1. How does Beijing's station-level TOD "measure up"? How does Beijing's TOD compare internationally, specifically to Singapore?

4 In this thesis I use metro as a synonym for urban rail systems, which might include heavy rail and light rail. s This point is raised by two planners I interviewed in Beijing. 13 2. Does a relationship exist between TOD and metro ridership in Beijing? Does a similar relationship exist in Singapore?

3. Based on the comparison with Singapore, how might TOD performance in Beijing be improved?

1.3 Research Method

At the highest level, this thesis is a comparative study between two Asian cities and their metro systems' conditions and ridership. To specifically answer the research questions, I first apply a TOD indicator framework to develop measures of metro-based TOD at the station level. These are measures of TOD outputs. I apply the approach to Beijing and a well-known Asian transportation "success" story, Singapore, to see how the two metropolises compare. Then, to examine the relationship between TOD outputs and outcomes, I focus on one outcome of interest, metro ridership, and explore whether similar relationships exist in both places. That analysis can provide some evidence for potential TOD improvements in Beijing. Finally, based on interviews with stakeholders in Beijing, I assess challenges to making TOD happen in the city.

1.3.1 TOD indicator framework

TOD can be represented at a regional (Cascetta and Pagliara, 2009; Reardon and Dutta, 2012), urban (Yang and Lew, 2009; Salat, 2016) or neighborhood level (Li, 2015; Blynn et al, 2016). In this thesis I focus at the neighborhood level, applying a station-level TOD framework that contains multiple spatial indicators and measures existing TOD at each station. Basically, a TOD area has been described as an area that a person feels comfortable walking to get to a transit node (Singh et al, 2014) and is typically an area within about 0.5 mile of a transit node (Calthorpe, 1993; Calthorpe, 2012). Hence at the station level, I define a TOD area as an 800-meter Euclidian buffer from a metro station. I compiled multiple spatial indicators to calculate station-area values for each metro station in Beijing and Singapore. Then I used the information entropy weight (IEW) method to assign weights to individual indicators and aggregate multiple indicators into a single TOD score. A single city-level TOD score could be calculated by aggregating individual station- 14 level scores and adding extra city-level indicators (Jiang et al, 2016). Comparing the TOD scores for the metro stations in Beijing and Singapore, reveals possible gaps between the two cities in TOD implementation. By mapping the TOD score using ESRI ArcGIS, I present the spatial pattern of TOD in the two cities.

1.3.2 Multivariate regression

As I use station-level ridership as a measurable outcome of TOD, I explore the association between this outcome (ridership) and TOD outputs, i.e. station-level built environment indicators. Many studies have examined if transit ridership can be predicted by built environment indicators using direct demand models (Juan, 2009; Sung, 2011; Chen and Zegras, 2015; Reyes, 2016). Direct demand models are multivariate cross-sectional regressions between station characteristics and station-level ridership (Reyes, 2016). To compile reasonably comparable outcome measures, I acquired datasets that record average weekday individual metro trips in Beijing and Singapore and I synthesized both datasets into daily station-level ridership 6. I then apply multivariate linear regression to predict daily station-level ridership with the explanatory variables being the TOD indicators.

1.3.3 Comparative statistical analysis

To answer the third research question, I compare the TOD indicators and TOD scores between Beijing and Singapore and also compare the results from the linear regression models estimated for the two cities. This comparison suggests not only how TOD outputs differ in the two cities, but also if the relationship between TOD indicators and ridership differs and whether possible improvements for Beijing can be identified.

6 Station-level boarding recorded by Beijing's transit card system and Singapore's EZ-Link system. 15 1.4 Thesis Structure

Following this introduction, Chapter 2 presents the literature reviewed on TOD definitions, scales, measurement, outcomes and the relationship between built environment and travel behavior. Chapter 3 gives an overview of Beijing and Singapore and their metro system development. Chapter 4 describes the TOD indicator framework and presents the results of its adaptation at the station level in Beijing and Singapore. Chapter 5 presents the multivariate linear regression models on station-level ridership using TOD indicators. The results of two models are compared between Beijing and Singapore. Chapter 6 concludes, summarizing the findings and limitations.

16 2 Literature Review

2.1 The Definition of TOD

The concept of TOD was originally proposed by Peter Calthrope (1993). TOD can be considered part of the new urbanism "movement" that emerged as an attempt to solve urban problems such as sprawl. Since World War II, two mutually reinforcing processes have characterized U.S. cities: decentralization and an increasing reliance on the automobile. As Belzer

and Autler (2012) state: "Heavy investment in roads and other implicit subsidies of automobile use, combined with comparatively low levels of transit funding, have facilitated decentralized urban development patterns and inefficient use of land." One oft-advocated alternative is TOD.

TOD is a package of urban form from the physical perspective, as well as development

strategy from the policy perspective (Rayle, 2014). As an urban form, it features mixed land use and growth, using public transit as a focal point to create attractive pedestrian environment (The World Bank, 2013; Cervero et al., 2002). By focusing on development around transit nodes and

mixing residential and commercial area, TOD aims to "transform an urban place into a less car- oriented - and more human-oriented - one" (Mu & de Jong, 2012,). Effective TOD also helps foster more efficient land use patterns and "create a more balanced set of transportation choices in which automobiles coexist alongside other options" (Belzer and Autler, 2002).1

This definition of TOD is broad and conceptual. Despite the fact that TOD has multiple potential social, economic and environmental benefits and such dimensions should be included

in its definition, physical characteristics of TOD make the concept tangible and measureable by quantifiable indicators. Several examples of TOD definitions include physical requirements about the built environment:

* Peter Calthrope (1993) defines TOD as "medium and high-density residential buildings, together with jobs, retails and public services concentrated within a walking distance to

7 This paragraph is partially based on an ongoing framework draft that will later be submitted to MIT STL lab.

17 stations along the urban ".

* Cervero and Ferrell (2002) conclude that TOD features "mixed-use development", "development that is close to and well-served by transit", "compactness", "pedestrian - and cycle-friendly environs", "public and civic spaces near stations" and "stations as community hubs".

" Dittmar and Poticha (2004) describe TOD as "a mix of residential, commercial and other public services, at medium to high densities, within a half-mile radius around transit stations."

" The Institute for Transportation & Development Policy (ITDP, 2014) proposed 8 key words - walk, cycle, connect, transit, mix, densify, compact, shift - that define TOD in its latest TOD standard report.

While studies of TOD focus on various scopes and contexts, the following elements are shared in most definitions:

* Developments that are proximate to and interactive with a transit node

" Medium to high density

* Mix land use

* Pedestrian- and cycle-friendly conditions

* Connections to other area

These characteristics are used as the foundation for the framework of TOD built environment outputs applied in Chapter 4.

2.2 Scale of TOD

The literature discusses TOD on multiple scales and from multiple perspectives. Cervero et al. (2002) discuss Peter Calthorpe's (1993) TOD scales as "urban TODs" or "neighborhood TODs," distinguished, in part, by the feeder or trunk status of the transit serving them. Salat (2016) 18 defines TOD at city, network and local level. At the city level, TOD coordinates urban economic, land use, and transport plans. At the network level, TOD is integrated into transit line plans. At the local level, TOD includes site specific considerations'. Li (2015) categorizes TOD practices in Shenzhen, China into urban TODs, neighborhood TODs, and Special TODs. Urban TODs include regional and district center plans. Neighborhood TODs include TODs in both urban and suburban areas. Special TODs refer to areas surrounding transport hubs (Li, 2015, citing Shao et al., 2011).

In this thesis I distinguish two scales of TOD planning and implementation. Macro-level TOD refers to citywide or regional plans which aim to structure an overall metropolitan area's growth around its public transport corridors and station. Micro-level TOD, on the other hand, refers to the neighborhood or station-area, whereby sites and environs are designed and programmed with a station orientation.

Examples of macro TOD plans include:

* Copenhagen's famed Finger Plan (Zheng, 2015, citing Cervero, 2006; Suzuki et al., 2013), which is a well-recognized model of the integration of land use and rail transit corridors and displays the benefits of the system-level mixed use and job-housing balance brought by TOD.

* Kunming's TOD city development strategy, which includes "compact" neighborhoods near trains (Mehndiratta and Salzberg, 2012)

* 's eco-city plan (Zheng, 2015), which has applied the TOD concept in both its master plan (2008) and its regulatory plan (2009) and aims to construct a green and sustainable urban environment.

Examples of micro TOD plans includes:

* The Rosslyn-Ballston corridor smart growth plan, which aims to "concentrates high- density, mixed-use development along a major transit corridor, while preserving and

'This paragraph is partially based on an ongoing framework draft that will later be submitted to MIT STL lab.

19 enhancing existing residential neighborhood" 9.

Garfield Park, an older urban community in Chicago, , which makes an El station the anchor for its revitalization efforts and has created a comprehensive mixed-use development. (FDOT, 2009).

This thesis focuses on micro-level TOD in Beijing and Singapore. As many neighborhood TODs are associated with a transit node and a TOD area should be within the distance that a person feels comfortable to walk to get to a transit node, I define TOD area as an 800-meter Euclidian radius buffer surrounding the centroid of a metro station.

2.3 Measurement of TOD

Efforts to measure transit-oriented development date back to the origins of the idea itself. Since Cervero and Kockelman (1997) introduced the now well-known '3D's of measuring the built environment - density, diversity and design - these design principles have often been used to measure TOD. In subsequent research, additional dimensions of destination accessibility and distance to transit were added, contributing to a '5D' framework (Cervero et al, 2009). Ewing and Cervero (2010) later add demand management and demographics to the measurement and expand it to '7D'.

Singh et al. (2012) conclude via case studies that previous methods to evaluate TOD are hardly operationalized. They recommend direct and quantifiable indicators to develop a composite TOD index and proposed a two-set TOD measurement framework: the potential TOD index which adopts an area or regional wide scope and the actual TOD as an 800-meter buffer surrounding a transit node. The actual TOD buffer make it easier to quantify indicators and collect relevant data.

Over the past few years, literature has emerged on selecting quantifiable indicators and developing composite measures to assess TOD. For example, Fart (2013) follows Singh et al's

9 Cited from Arlington, VA website, available at https://projects.arlingtonva.us/planning/smart-growth/rosslyn-ballston-corridor/ 20 (2012) potential TOD index framework and uses residential density, commercial density, level of land use mix and business density to measure the TOD-ness in the Netherlands based on regular tessellation of space. Singh et al (2014) select 13 indicators, which can be grouped into four criteria, and adopted the Spatial Multiple Criteria Assessment (SMCA) platform to generate a location-based composite TOD index for Arnhem Nijmegen City Region in the Netherlands. ITDP (2014) proposed 21 indicators for assessing a TOD project, which can be grouped into walk, cycle, transit, connect, mix, densify, compact and shift.

Literature that focuses on the measurement of TOD in the Chinese context is relatively scarce. Zhang (2007) presents an operational TOD model which is characterized as five D's squared. The framework includes five attributes: differentiated density, dockized district, delicate design, diverse destination, and distributed dividends. Jiang et al (2016) point out that previous TOD indicators mainly focus on neighborhood level measurement. In an effort to characterize TOD across urban China they develop four system-level measures of quantity and 13 station-level indicators of TOD quality, synthesizing a city-wide TOD Index to compare 24 Chinese cities in the two dimensions.

Singh (2014, citing Evan & Pratt, 2007) argues that "TOD indicators that define or characterize TOD must be quantifiable" and fit for "forward-looking assessment rather than back- ward looking evaluations". Jiang et al (2016)'s framework is particularly relevant since it uses quantifiable indicators to measure station-area characteristics and is constructed based on the Chinese context. I follow Jiang et al (2016), adapting their station-level TOD quality indicator framework. This is discussed in detail in Chapter 4.

2.4 Benefits of TOD

TOD aims to provide various positive outcomes and "has gained popularity as a means of redressing a number of urban problems, including traffic congestion, affordable housing shortages, air pollution, and incessant sprawl "(Cervero and Ferrell, 2002, pp. 2). TOD benefits can be direct and indirect. Cervero et al. (2004) divide TOD benefits into primary and secondary

21 categories (see Table 2-1) and find that increased ridership is the TOD target most agreed upon among 100 transit agencies they surveyed (see Table 2-2).

Table 2-1 Classes and Recipients of TOD Benefits (Cervero et al. 2004), Class of Benefits Primary Recipient of Benefits Public Sector Private Sector 1. Increase ridership and fare box 5. Increase land values, rent, and revenues real-estate performance Primary 2. Provide joint development 6. Increase affordable housing opportunities opportunities 3. Revitalize neighborhoods 4. Economic development A. Less traffic congestion and VMT- G. Increase retail sales (1, 2) related costs, like pollution and fuel consumption (1) B. Increase property- and sales tax H. Increase access to labor pools revenues (5) (A, 6) Secondary/Collateral C. Reduce sprawl/conserve open space I. Reduced parking costs (C, 2) (1, 3, 6) D. Reduce road expenditures and other J. Increased physical activity (C, infrastructure outlays (1) E, F) E. Reduce crime (3,4) F. Increased social capital Note: Values in parentheses represent primary benefits and/or secondary benefits that are the source(s) of the secondary/collateral benefit listed.

22 Table 2-2 Relative Frequency of Transit-Agency Targets for TOD Projects (Chen, 2010, citing Cervero et al. 2004) TOD Targets % of Transit Agencies Agreeing to This Target Increase ridership 20.0% Promote economic development 15.6% Raise revenue 13.3% Enhance livability 11.1% Widen housing choice 8.9% Improve safety 4.4% Share construction cost 4.4% Reduce parking 4.4% One-stop center/fare outlet 4.4% Improve intermodal integration 2.2% Enhance pedestrian access 2.2% Improve air quality 2.2% Put property on tax rolls 2.2%

Research on the relationship between TOD outputs and outcomes requires that TOD outcomes be easy to measure objectively. As a direct outcome of TOD, metro ridership is relatively easy to observe and quantify. Previous studies use direct ridership models to predict transit ridership and have identified associations between ridership and the built environment, land use, socioeconomic characteristics, transit service, etc. (Kuby et al, 2004; Cervero and

Murakami, 2010; Juan and Oh, 2011; Chen and Zegras, 2015; Reyes, 2016). This thesis thus chooses metro station ridership as a proxy of TOD outcomes and assesses if similar relationships exist in Beijing and Singapore in Chapter 5.

2.5 TOD and Travel Behavior

Literature has found that built environment can influence travel behavior and thus metro ridership. Jiang et al (2012) point out that people's transit use decisions are greatly influenced by travel time, including in-vehicle time, wait times, transfer times, and access/egress times. Travel time of a trip consists of actual and perceived times, with the latter influenced by comfort levels. 23 The station area built environment could impact both actual and perceived times and therefore impact people's travel mode decisions. For example, the directness of streets and number of crossings could affect the actual travel time while the density and diversity of street environment could affect the walking experience. Good TOD design could therefore reduce both actual and perceived travel times and encourage walking and transit use.

Zegras et al (2015) point out that land use and mobility are two key subsystems that relate to people's location decisions and defines urban accessibility. Since TOD aims to maximize mix use and encourages transit investment, good TOD practice will boost ridership by attracting "pedestrian access to quality transit service, compact, mixed land uses, and safe environmental conditions" (Zegras et al, 2015, citing Suzuki et al 2013).

Relative location, transit service quality, and urban form and design could also affect travel behaviors. Chen and Zegras (2015) find that locations with higher population and employment density, more connected stations and more walkable stations attract more riders.

In conclusion, station area built environment is relevant in determining transit use behavior and thus impacts station-level ridership. The built environment elements will be captured as TOD outputs in Chapter 4 and will be used to predict ridership in Chapter 5.

24 3 Context of Beijing and Singapore

This thesis compares TOD in Beijing and Singapore and its relationship with ridership in both cities. This chapter includes an overview of the two cities from the perspective of the integration of their urban rail systems and land use strategies. A summary of key characteristics in Beijing and Singapore is shown in Table 3-1. Figure 3-1 displays the metro system in Beijing and Singapore at the same scale.

Table 3-1 Summary of Key Characteristics in Beijing and Singapore'0 Beijing Singapore Population 21.7 million (2015) 5.6 million (2016) Population Density 1323 ppl/km 2 (2015) 7797 ppl/km 2 (2016) Area 16400 km 2 719.2km 2 Divisions 16 Districts 5 region and 55 Planning Area Urban Structure Ring and radical roads Fan-like structure Metro line length 19 lines/515km (2016) 5lines/170.7km (2016) Metro Stations 288 (2016) 102 (2016) All Transit Mode Share 48% (2014) 44% (2012) Rail Mode Share 22.4%(2014) 19%(2012) 2.5 million/day (2012) Metro Daily Ridership 9.27 million/day (2014) 3.1 million/day (2016)

10 Data in this table are from different sources and are detailed in the following sections.

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Figure 3-1 Metro System in Beijing(left) and Singapore(right)

3.1 The city of Beijing

3.1.1 Beijing Overview

Beijing, located in Northern China, is the capital city as well as the political, educational and cultural center of China. It is one of the most populous cities in the world with a total population of over 21.7 million; the overall population density reached 1,323 people/ km 2 by the end of 2015 (Beijing Yearbook, 2016)". Beijing spans an area of over 16,400 km 2 and includes 16 districts (see Figure 3-2). Beijing has a ring and radial highway system to support the urban spatial pattern (see

Figure 3-3). Among the 16 districts in Beijing, Xicheng and Dongcheng Districts are inside the 2 "d

Online version available at: http://www.bistats.gov.cn/ni/main/2016-tini/zk/indexch.htm 26 Ring Road and form the old inner city area. Shijingshan, Haidian, Chaoyang and Fengtai are located between the 2nd and 5 th Ring Roads and form the expanded urban area. Changping,

Mentougou, Fangshan, Daxing, Tongzhou and Shunyi are linked by the and form the inner suburban area. Yanqing, Huairou, Miyun and Pinggu in the North form the outer suburbs. For the scope of this thesis, the outer suburban area is not considered.

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Figure 3-2 Beijing Administrative Divisions Figure 3-3 The Ring and Radius Structure in Beijing (Google Map, 2017)

3.1.2 Beijing Subway

The Beijing Subway is a rapid rail transit network that serves the urban and suburban districts of Beijing. By the end of 2016, the network had 19 lines, 288 unique stations and 575 km of track in operation (see Figure 3-4)12. There are two subway operators in Beijing. Beijing MRT Corporation operates , 14 and 16 and Beijing Subway Corporation operates the other 16 lines. Following Shanghai, Beijing has the second longest subway network in the world (UITP, 2015) with an average daily ridership of 9.27 million and annual ridership of 3.38 billion in 2016 ( Beijing Infrastructure Investment Co. LTD, 2015)".

12 Reported by China News, available at: http://www.chinanews.com/ci/2016/12-31/8110767.shtml 1 Revealed by Beiiing Infrastructure Investment Co. LTD, available at: http://www.bii.com.cn/705-2063-5114.aspx 27 As for the timeline of construction, is the oldest subway line in Beijing and was constructed in 1969. Rapid expansion of the subway started in 2002 (only Line 1 and 2 operated before then). Lines 4, 5, 8, 10, 13 and the , Airport Line, Changping Line, , Fangshan Line and Yizhuang Line were constructed from 2002 to 2010. Lines 6, 7, 9, 14 and 16 are relatively new, entering into service after 2010.

The Beijing subway network is still being expanded and according to the Construction Plan for the Second Phase of Rail Transit in Beijing'4, the total length will reach 999 km. The Beijing Transportation Report 201415 reports that the public transit mode share is 22.4% for rail and 25.6% for bus in Beijing. Officially, "by the time all these ongoing projects and their approved extensions are finished in the next few years, public transit mode share of motorized trips will reach 60%, with subway accounting for 62% of all public transit rides",16

14 Available at http://www.ndrc.zov.cn/zcfb/zcfbghwb/201509/WO20150929497631283093.pdf 15 Available at http://www.bitrc.org.cn/infoCenter/NewsAttach/2014%E5%B9%B4%E4%BAA4%E9%80%9AE8%BF%90%E8%A1%8C%E6%8 A%A5%E5%91%8A 20160303145519856.pdf 16 According to the official reply to Beijing Planning Commission by the National Development and Reform Commission, available at: http://www.ndrc.gov.cn/zcfb/zcfbhwb/201509/t215O929 753187.html 28 Beijfing Subway

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3.2 The city of Singapore

3.2.1 Singapore Overview

Singapore is a city as well as a country at the southern tip of peninsular Malaysia, with Indonesia's Riau Islands to the south. It is a global commerce, finance and transport hub with a population of 5.6 million; the population density reached 7797 people/km 2 (statistic Singapore, 2017)18. Singapore has a total land area of 719.2 km 2, divided by the Urban Redevelopment

17 Available at: http://www.bosubway.com/ 18 Statistic Singapore available at: http://www.singstat.gov.sg/statistics/latest-data#16 29 Authority into 55 Planning Areas, grouped into five regions: West Region, North Region, North- East Region, East Region and Central Region (see Figure 3-5 ). The area is the Central Business District. Although the government Housing and Development Board (HDB) continues to build more housing estates on the outskirts to decentralize job centers, commercial and industrial facilities still concentrate in the Central Area (see Figure 3-6). Singapore maintains strong monocentric characteristics.

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3.2.2 Singapore MRT

The Mass Rapid Transit (MRT) is a rapid transit rail network that forms the major component of the Singapore public transport system. By the end of 2016, Singapore's MRT system had 5 lines in service and 102 stations in operation, with 1 line under construction and 2 under planning (Land Transport Authority, 2017). The rail system is operated by two public transport operators SMRT Trains (SMRT Corporation) and SBS Transit. In 2016, the total length of the MRT system reached 170.7 km and had an average daily ridership of 3.1 million (not including LRT). 20

19 Singapore Urban Redevelopment Authority 2014 Plan, available at htts:/www.ura.aov.sa/uol/master-plan/view-master-plan/master-Ian- 2014/Growth-Area.asox 20 Data source: Singapore Public Data, available at https://data.gov.sg/dataset/public-transport-utilisation-average-public- transport-ridership. 31 A

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The North South Line and East West Line opened in late 1987, the North East Line in 2003, the Circle Line in 2009 and the Downtown Line in 2013. As of 2017, three lines are either under construction or under planning, including the Thomson-East Coast Line, the Region Line and the Cross Island Line; they will start opening in 2019 and will be in full operation by 2030. The peak hour public transport mode share was 66% in 2014; the Land Transport Master Plan

21 Available at: httos://www. Ita .gov.sg/content/ltaweb/en/Du blic-tra nsport/mrt-a nd-Irt-trains/tra in-system-map, html 32 2013 set the goal that by 2030 the peak hour public transport mode share would increase to 75%

(Ministry of Transport, 2014)22.

3.2.3 Singapore TOD.

Singapore has widely-recognized good transportation and land use planning. Mees (2014) argues that TOD application needs a multi-modal understanding of concept and "significantly, the need for multi-modal planning can be seen in Singapore, the city generally regarded as the epitome of successful TOD" (pp 466). At the macro level, Singapore integrates its urban structure and land use strategies closely with its MRT plans. The current urban pattern of Singapore is greatly shaped by the 1970 Concept Plan that aimed to decentralize the urban center along the transit corridor (Yang and Lew, 2009). The 1991 Concept Plan proposed a fan-like urban structure served by the MRT network (see Figure 3-8Figure 1-1). The Western, Northwestern, Northeastern and Eastern sub centers serve as dense regional hubs connected by the rail system. Figure 3-9 illustrates the evolution of new town development from 1960 to 2005. It is evident that new towns are supplemented by MRT expansion in Singapore.

22 Available at https://www.mot.gov.sg/About-MOT/Land-Transport/Public-Transport/

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34 1960 1977 First generation new towns North, east, west decentralisation near to city centre

1996 Woodlands line completed Plan for North East line started

Legend Distribution of New Towns

-- MRT n11and stations

North east line completed

Figure 3-9 Distribution of New Towns and MRT Infrastructure (Yang and Lew, 2009 ,compiled from HDB Annual

Reports 1960-2005) At the micro level, Singapore, on the one hand, highlights pedestrian friendly environment and mix land use to facilitate transit use and, on the other hand, has a series of policies such as road pricing and vehicle quota system to control private vehicle use. From the perspective of urban design, Singapore "transformed the new town from a car-preference pattern to a transit-oriented pattern" (Li, 2015, pp 42).

35 However, Singapore is also criticized for not being pedestrian friendly in its urban form. Barter (2013) views the HDB house as "towers in the park" with ample space for large roads and little priority for pedestrians. Smith (2014) criticizes that the concrete towers of public housing "tend to hew to the Corbusian form, with parking podiums, bays and lots on the bottom, and relatively useless open space between buildings" He also argues that the ubiquitous public towers have "none of the architectural diversity or spontaneity that characterizes more market- oriented residential neighborhoods". One the other hand, Tay (2012) points out that HDB estates have considerable amounts of permeable open space, enabling walkable "short cuts" among the blocks with shade, safety and reduced exposure to vehicle traffic.

Some design elements of TOD are hard to capture or quantify. The following chapters will select quantifiable indicators to measure and compare the TOD-ness of Beijing and Singapore and explore whether Singapore has better TOD outputs and outcome than Beijing.

36 4 TOD Indicator Framework

Measuring TOD-ness using a single TOD score requires combining multiple indicators that influence the transit-orientation of a development (Singh et al, 2017) at various scales. In this thesis, I focus only on station-level TOD in Beijing and Singapore.

I first defined the TOD area surrounding a metro station, then selected 12 indicators that characterize TOD at the station level and constructed a composite TOD score based on the indicators. The selected indicators mostly measure the physical side, representing outputs of TOD. I collected two datasets that contain station-area characteristics for 280 unique 23 Beijing metro stations24 and 90 unique Singapore MRT station 25 and pooled the two datasets together to assign a TOD score to each station using ArcGIS. This chapter describes the data used, maps the spatial distribution of TOD scores in Beijing and Singapore and compares the TOD-ness of the two cities.

4.1 TOD Area

A TOD area is the geographical area that a person feels comfortable walking to get to a transit node (Singh et al, 2014). While we do not have a universal agreement on the length of a comfortable walking distance, a 5-10 min walk is considered a typically acceptable walking time to a metro station (Reyes, 2016). Calthorpe (1993, 2013) proposes 0.5 mile as an acceptable walking distance. Guerra and Cervero (2011) find evidence from 1449 high-capacity American transit stations that using quarter-mile catchment area works best for job density and half-mile

23 Transfer stations with the same name are combined and calculated only once. For example, there are two stations called Dawanglu Station in Beijing, one on Line 1 and another on , with a distance of 258 meters between the centroids of two stations. I use the midpoint as the centroid of the two stations to create a 800-meter radius buffer and calculate corresponding station area characteristics only once. I did so to match the data format of station-level ridership used in chapter 5.

24 Most data were originally collected by China sustainable Transportation Center (CSTC). More information about CSTC is available at: http://www.chinastc.org/ . The dataset mainly contains data from the year 2014 for Beijing.

25 Most data were originally collected by research group of The Future Urban Mobility IRG (FM) at Singapore-MIT Alliance for Research and Technology (SMART). More information about FM is available at: http://fm.smart.mit.edu/. More information about SMART is available at: https://smart.mit.edu/.The dataset mainly contains data from the year 2012 for Singapore.

37 works best for population density for the purpose of predicting ridership. However, the marginal gains from a quarter-mile to a half-mile catchment circle are quite small. They also find support that diamond shape or network path-based station area buffers do not change the predictive power of direct demand models dramatically. In this thesis, therefore, I consider a TOD area as an 800-meter (~0.5 miles) Euclidian radius buffer surrounding the centroid of a metro station in Beijing (Figure 4-1) or Singapore. This TOD area bounds the data for the TOD indicators calculated in the following sections.

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indicators that measure station-level quality and city-level quantity of TOD. The city-level TOD indicators mainly reflect the overall supply level of urban rail. The station-level TOD indicators mainly reflect the level of integration of the urban rail system with land use and other transport

3b networks around rail stations. The 13 station-level indicators are grouped into four TOD dimensions: density, diversity, design, and connectivity. This thesis does not consider the city- level TOD potential. I modified some of the indicators proposed by Jiang et al (2016) due to data availability and use only station-level indicators in my calculations.

39 Table 4-2 summarizes the TOD indicators used in this thesis and data sources.

Table 4-1 Indicator System for Evaluating TOD (Jiang et al, 2016) Dimensions Indicators Relationship Code with TODb

Rail Line density (km/km 2) + X1

Rail Station Density (unit/km2 ) + X2

Urban Land Coverage Ratio (%) + X3

Urban Population Coverage Ratio (%) + X4 2 Population Density (10k ppl/km ) a + X5 2 Density Employment Density(unit/km ) a + X6

Density gradient a + X7

Land Use Mix a + X8

Diversity Job-housing Imbalance a -X9

Street Network Density (km/km 2) a + X10

2 X1 Design Expressway Density (knm/km )a - TOD Quality Ground-floor Retail Density (unit/k) + X12

Number of Parking Facilities (unit) a -X13

Distance to Passenger Transport X Terminal (km)

Number of Bus Lines (unit) a + X15

Connectivity Number of Bus Stops (unit) a + X16 Distance to Municipal Public Service - x17 Facilities (kin)

40 Table 4-2 TOD Indicators and Data Source

Dimension Indicator Operational Definition Relationship Data Source with TOD Population Residential population density. + Worldpop Density database (10 ppl/km 2 ) Workplace Count The number of work POI, + Beijing: Baidu (Unit) including public service, online map; Density commercial, company POIs. Singapore: FM database Density Gradient The ratio of FAR of the 400m- + Beijing:CSTC radius core to that of the rest database; station area. Density Gradient Singapore: FM = FAR of the inner 400-m database circle area/FAR of the outer 400- to 800-m ring area Land Use Mix POI Mix + Beijing: Baidu online map; Diversity Singapore: FM database Ground-floor The number of retail POIs + Beijing: Baidu Retail Density within a 35-m-wide buffer online map; (unit/km) along both sides of road center Singapore: FM lines divided by the length of database road Road Density The length of urban streets + Beijing:CSTC (km/km 2) divided by the size of the TOD database; area Singapore: Land Use Authority Design database Highway Density The length of highway divided - Beijing: CSTC (km/km2 ) by the size of the TOD area database; Singapore: SLA database Number of The number of parking - Beijing: Baidu Parking Facilities facilities in the TOD area online map; (unit) Singapore: MyTransportSG Building The footprint of all buildings - Beijing: Baidu Coverage Ratio divided by the size of the TOD online map; (%) area Singapore: FM database

41 Number of Bus The number of bus lines + Beijing: Baidu Lines (unit) online map; Connectivity Singapore: FM database Number of Bus The number of bus stops + Beijing: Baidu Stops (unit) online map; Singapore: FM database Number of Metro The number of metro station + Beijing: Beijing Station Entrance entrance Metro hardcopy (unit) map; Singapore: MyTransportSG

42 4.2.1 Density Indicators

Density describes population, employment and development density surrounding a metro station. Higher population and employment density means "more" TOD. The density gradient here is a proxy for a true gradient, calculated as the ratio of the FAR of the 400m-radius core to that of the rest station area. A higher gradient means "more" TOD.

4.2.2 Diversity Indicators

In measuring diversity, I use points of interest (Pol) 26 as proxies of land use types. I first wrote Python script to scrape POls data for Beijing from Baidu online map27, and I obtained Singapore POls data from the FM database. I grouped the POls into 6 categories: residential, public service (e.g., government, education, medical, etc.), commercial (e.g., entertainment, retail, etc.), company, park and other (e.g., transportation facility, warehouses, etc.). I estimate mix as (Manaugh, 2013, citing Shannon, 1949):

16 mix =- 1 p, Inp, 4-1 In 6 i where Mix is the mixing degree, pi is the percent of the ith type of POls in all POls. Additionally, ground-floor retail density is also measured for the diversity dimension.

4.2.3 Design Indicators

Design indicators measure the design of urban fabric in terms of encouraging pedestrian activities and improving the attractiveness of the built environment. I selected road density, highway density, building coverage ratio (BCR) and number of parking facilities to represent design.

26 A P01 is a development of a certain type. I used keywords including 'residential', 'apartment', 'government', 'school', 'medical', 'mall', 'hawker', 'hotel', 'retail', 'entertainment', 'company', 'park', 'transportation', etc. to scrape P01 location data f rom online map.

27 Baidu map: http://map.baiducom, accessed July 1, 2016

43 4.2.4 Connectivity

Connectivity represents the convenience of transferring from the metro system to other travel modes. A well-designed metro station should have multiple exits and connect to other transit nodes such as bus stops and bike hubs. I thus selected the number of metro entrance/exits, the number of bus lines and the number of bus stops to measure the connectivity of a TOD.

Table 4-3 Summary of Statistics of TOD Indicators

Indicator Beijing (N=281) Singapore (N=90)

Max Min Mean Std Max Min Mean Std

Population Density (10k/kM 2) 3.31 0.06 1.47 0.91 7.25 0.00 2.51 1.65 Work POI (unit) 960.00 0.00 109.74 141.59 820.00 4.00 202.67 184.06 Density Gradient 2.27 0.00 0.69 0.53 2.54 0.54 1.05 0.29 Land Use Mix 0.98 0.00 0.81 0.14 0.85 0.00 0.53 0.22 Ground-floor Retail Density 20.62 0.00 4.85 3.62 33.50 0.00 5.45 7.84 (unit/kin)______

Road Density (km/kM2) 24.64 1.62 13.74 5.25 36.42 0.00 10.68 7.46

Highway Density (km/kM 2) 3.88 0.00 1.03 1.18 8.40 0.00 1.15 1.35

Number of Parking Facilities 10.00 0.00 1.49 1.74 14.00 1.00 6.03 4.34 (unit)

Building Coverage Ratio(%) 0.45 0.00 0.15 0.11 0.45 0.07 0.23 0.08

Number of Bus Lines (unit) 56.00 0.00 10.21 8.90 35.00 0.00 13.90 7.78

Number of Bus Stops (unit) 115.00 0.00 27.50 16.54 62.00 3.00 31.37 11.49

Numbereof Metro Station 12.00 1.00 4.21 1.71 10.00 1.00 3.38 1.84 Entrance(unit)

Table 4-3 summarizes the descriptive statistics for the indicators in both cities. Singapore has higher values for all three density indicators, higher ground-floor retail density, lower road density, higher highway density, more parking facilities, higher building coverage ratio, more bus lines and bust stops and less metro station entrance than Beijing. Additionally, Singapore has 44 higher variance in most of its indicators, which suggests that geographically MRT stations in Singapore have more varied built environment surroundings than stations in Beijing.

4.3 Composite TOD Index

To measure the overall TOD-ness of metro stations and compare the distribution of TOD in the two cities, I follow Jiang et al (2016), constructing a composite index by assigning weights to individual indicators and aggregating them into a single index. I used the information entropy weight method (IEW) to assign weights. It is developed by Shannon and Weaver (1948) and uses weights according to the variation degrees among indicators. In other words, if an indicator contains a higher amount of information, it will be assigned a higher weight using IEW and vice versa. It utilizes the data information and is a comparatively objective weighting method. Jiang et al (2016) detail the calculation in the following steps:

1) Decision matrix construction

The data matrix X= (X;) mxn,, is constructed. The matrix comprises m metro stations and n indicators, where xij is the value of the fh indicator of the jth metro station. 2) Normalization of the decision matrix To eliminate the effect of various units and to get the quantitative index value of the same degree X';1, each indicator is normalized by using Eq. 4-2 (if positively associated with TOD-ness) or Eq. 4-3 (if negatively associated with TOD-ness).

x..-xi. X. = u jmin 4-2 jmax jmin

jmax 1i 43 jmax jmin 3) Calculation of the entropy value of each indicator

Entropy value H. of the fh indicator,

45 H-- pi, Inpj 4-4 In mnpn

where

X' Pj = IM iix - k=1 4-5 i, , --, ;j = , ,--,n

If p1 = 0, then In pj is deemed as 0.

H is the information entropy of an indicator. In general, a smaller value of Hi indicates that the indicator has higher deviation degree and higher amount of information, and will therefore result in higher weight in the composite TOD score calculation.

4) Calculation of the imbalance coefficient of each indicator

Deviation in the coefficient G of the Jhindicator,

G =1-H 4-6

5) Calculation of the weight of each indicator

Weight W of thefh indicator,

W. = 4-7 J n,-G

6) Calculation of the TOD score of each metro station

The TOD score V. of the jth station is,

V = W X x100 4-8

1first used IEW to calculate the weights of individual indicators for Beijing and Singapore separately. Then I pooled the two datasets of station-area indicators together and calculated corresponding weights. Table 4-4 summarizes weights of the individual indicators as well as the

46 four dimensions of TOD-ness. Beijing has higher deviation degree in its work POI, density gradient, highway density, number of bus lines and number of bus stops indicators and lower deviation degree in other indicators than Singapore. While the pooled weights are slightly different from the separate weights for both cities, I used the pooled weights to calculate the composite index for both cities so that they could be more comparable across cities. In measuring the TOD score, the density dimension weights the highest, followed by connectivity and diversity dimension. The design dimension weights the lowest, suggesting that the design elements around stations are similar across Beijing and Singapore. The individual weights indicate that number of work POls, ground-floor retail density, density gradient, number of bus lines and population density vary a lot across area around different stations in Beijing and Singapore.

Table 4-4 TOD Indicators and Corresponding Weight

Weight Dimension Basic Indicators Expect Weight Weight Effect (Pooled) (Beijing) (Singapore) Density Population Density (10k/km2 ) + 0.1080 0.0952 0.0983 (w=0.4763) Number of Work POIs (unit) + 0.2417 0.2463 0.1582 Density Gradient + 0.1266 0.1578 0.0613 Diversity Land Use Mix + 0.0216 0.0074 0.0516

(w=0.1943) Ground-floor Retail Density + 0.1727 0.1085 0.3165 (unit/km) Design Road Density (km/kM 2) + 0.0487 0.0418 0.0945 2 (w=0.0993) Highway Density (km/kM ) - 0.0076 0.0422 0.0111 Building Coverage Ratio (%) - 0.0368 0.0317 0.0350

Number of Parking Facilities (unit) - 0.0061 0.0097 0.0156 Connectivity Number of Metro Entrances(unit) + 0.0387 0.0518 0.0561 (w=0.2301) Number of Bus Lines (unit) + 0.1258 0.1355 0.0648 Number of Bus Stops (unit) + 0.0657 0.0722 0.0370

47 4.4 TOD Score Result

Table 4-5 shows the summary of TOD scores in Beijing and Singapore. Appendix A and B include the full results of the calculated TOD scores (Table 0-1) (Table 0-2). For the four detailed dimensions, Beijing has overall slightly higher diversity score and design score whereas Singapore has higher density and connectivity scores. The TOD score results show that Singapore stations, as expected, have overall higher TOD scores than Beijing stations with less variances. However, some stations in Beijing have very high TOD score. For example, Guomao Station (58.65) in Beijing ranks at the top in the two cities. Among the top 20 TOD stations, 10 are in Beijing and 10 are in Singapore.

Table 4-5 Summary of TOD Score Statistics

Density Diversity Design Connectivity TOD Score Score Score Score Score Beijing Mean 8.420 4.280 5.510 5.220 23.430 N=281 Std. Dev 6.177 1.906 1.125 2.918 8.847 Minimum 0.129 0.004 2.757 0.759 7.774 Maximum 35.150 12.152 8.083 19.083 58.652 Singapore Mean 14.078 3.978 4.332 6.002 28.390 N=90 Std. Dev 5.184 4.172 1.231 2.612 8.073 Minimum 5.153 0.000 1.942 0.171 12.074 Maximum 31.773 18.595 7.360 12.901 48.457

Figure 4-2 shows the spatial distribution of Beijing's TOD score by station. For Beijing, most stations with a TOD score higher than 40 are located within the 4th urban ring road, which is roughly the route of , with clusters around area, Guomao area and Sanyuanqiao area. Most high-score stations are on subway Lines 1, 2, 4, 5, 10 in the expanded urban area. Stations with a TOD score lower than 10 are mostly outside the 5 th urban ring and lie on Fangshan Line, Changping Line, Daxing Line, Airport Line and the end of Lines 14, 15 and 16.

48 Density provides the foundation to secure sufficient passenger demand for the operation of metro system. In the aspect of density, the population density of the areas surrounding metro stations in Beijing is generally high. However, greater differences among stations exist in all three indicators of density. For example, some stations have a population density over 33,000 people/km2 , but some other stations have less than 600 people/km2 . A decreased building density gradient is found at 85 stations in Beijing. For the other 195 stations, the density gradient

is below 1. Geographically, stations within the 4th urban ring area generally have higher density scores (see Figure 4-3). Highest-density-score stations cluster in the northwestern and eastern area of Beijing, where Zhongguancun and Guomao are located. Zhongguancun is the technology hub and Guomao area is the central business district (CBD) of Beijing. It shows that high-tech companies and CBD are important attractors of density in Beijing.

In terms of diversity, Figure 4-3 displays the spatial pattern of diversity distribution in Beijing. Generally, the inner urban area has higher diversity scores, although the diversity score is not drastically different across the entire city. The inner urban area has a longer history of development and has formed more diversified land use. While Beijing still has monocentric characteristics, it is in transition to a polycentric pattern (Huang et al, 2015). Multiple employment sub centers have formed in the expanded urban area (, Chaoyang District and ) since the late 1990s, which helps explain the comparatively uniform pattern of diversity score in Beijing.

Design scores display a similar spatial pattern as density. Stations within the 4th ring road rank higher overall than other stations and all four indicators have great differences among different stations (see Figure 4-3). For example, some stations have a road density of 24.64 km/kiM 2, an average retail density of 7.08 unit/km, and no expressways in its 800-meter radius service zone. In comparison, some other stations have a road density of only 7.1 km/ kM2 , an average retail density of 2.73 unit/km, and an expressway density over 3.8 km/ km 2. This pattern could be explained by the fact that the inner urban area (Dongchen, Xichen and Chaoyang Districts) have significantly higher ratios of old urban areas, constructed with smaller urban

49 fabrics and narrower urban roads. Also, the Protective Plan for Beijing Traditional Urban Area28 writes that "Large scale parking is strictly controlled ... in traditional urban districts". Therefore, the old urban areas are traditionally more active in terms of street environment.

As for connectivity, stations on Lines 1 and 4 have significant higher scores since major transport hubs lie on these lines. Additionally, transfer metro stations generally have better connection than other stations. For example, Liuliqiao East metro station is only 200 meters away from the Liuliqiao Bus Terminal and therefore has over 70 bus stops and 55 bus lines in its service area. In comparison, Yihezhuang Station, only has 1 bus stop and 1 bus line in its service area.

18 Available at http://162.105.138.200/uhtbin/cgisirsi/x/0/0/5?searchdata1=^C3003094 50 Zon n

006

N

0 2.5 5 10 km Si i i 1111

TOD Beijing Metro Line - - Batong Line

* 5 - 10 - --- Under Construction - Line 1 - Daxing Line

* 11 -20 - Line 10 - Fangshan Line

o 21 -30 Line 4 - Changping Line

* 31 -40 Line 14 - Yizhuang Line Airport Line * 41-60

Figure 4-2 TOD Score of each Metro Station in Beijing 51 * 00

DWOerIty

* < 4.0 * <2.0 * 4.1 - 8.0 *2.1-4.0 o 8.1 -12.0 o 4.1-6.0 * 12.1 -18.0 * 6.1 -8.0 >16.0 * 8.0

0

DesIgn nnection * <4.0 40<3.0 * 4.1-6.0 * 3.1-6.0 o 6.1-8.0 o 6.1 -9.0 * 9.1-12.0 * 8.1 - 10.0 * >12.0 * >10.0 N A 0 5 10 20 km 1 11-1

Figure 4-3 Density, Diversity, Design and Connectivity Score in Beijing

52 Figure 4-4 shows the spatial distribution of Singapore's TOD scores. For Singapore, most stations with a TOD score higher than 40 cluster within the Central Planning Area. Most high- score stations are on the North South Line, North West Line and East West Line.

Station (NS4/BP1) has the highest TOD score of 48.46, followed by Bugis Station (EW12), Esplanade (CC3) and (NE8), all higher than 45. Most high score stations are located around the Downtown Core. Singapore does not have stations with a TOD score lower than 10. The low TOD score group includes Botanic Garden Station (CC19), Woodleigh Station(NE11), Labrador Park Station (CC27), Station (EW10), Stadium Station (CC6), Haw Par Villa Station (CC25), Station (NS7), Bukit Brown Station (CC18), all assigned a score below 20 and located in the southern and eastern part of Singapore.

Figure 4-5 displays the spatial distribution of the density score. Singapore has a higher average density score across the entire country with less variance than Beijing, reflecting the fact that Singapore is generally a compact country. In the pooled dataset, among the top 15 high- density-score stations, 9 are located in Singapore. Geographically, stations with high density not only cluster in the Downtown Core, but also scatter in the South West, North West, North East and South East. This could result from the efforts of the Singapore government to decentralize its urban center. The Housing and Development Board has increased public housing supply in regional centers and sub centers to relieve the congestion in urban centers over the past three decades (see Figure 4-6). This may explain the less centralized pattern of station-area density across the city.

Figure 4-5 displays the spatial pattern of diversity in Singapore.. Stations within the

Downtown Core have higher diversity scores whereas other stations have very small variance. This could be explained by the fact that central Singapore has higher job density and more balanced types of POls to serve its population. I compared the results with the land use of Singapore in its 2008 Master Plan (see Figure 4-7) and discovered that the POls data is in line with land use maps. Figure 4-7 also shows that the Downtown Core has more mixed land use.

As for design and connectivity in Singapore, they both show the same pattern as the overall TOD score: stations in the Downtown Core have significantly better design scores than 53 other stations (see Figure 4-5). This could be explained by the fact that Singapore still has a strong monocentric urban structure. Despite the decentralized distribution of housing, commercial services and jobs are still strongly clustered around Downtown Core. Additionally, Singapore has better connectivity around its MRT station areas than Beijing, reflecting the feeder bus network and possibly a product of the road pricing scheme and high car ownership costs pressuring the city to ensure sufficient transit service.

N - town Cor

0 2.5 5 10 kmn Esri, DeLorme, GEBCO, NOAA NGDC, and other contributors, Esri, Inc., Sources: Esri, GEBCO, NOAA, National Geographic, DeLorme, HERE, Geonames.org, and other contributors

TOD Score - East Wst Line (EW) - Circle Une (CC) - LRT (SK) * 5-10 - Branch Une (CG) - Circle Une Extension (CE) - LRT (PG) * 11 -20 - East Wst Line (EW) (Under Construction) - Downtown Line (DT) - LRT (BP) o 21-30 - North South Line (NS) - Downtown Une (DT) (Under Construction) - Express (S) * 31-40 - North South Une(NS) (Under Construction) -- Downtown Una (DT) (Planned) * 41-60 - North East Line (NE) - Thomson Une (TS) (Planned)

Figure 4-4 TOD Score of each Metro Station in Singapore

54 Density Diversity * c4.0 * < 2.0 * 4.1-8.0 * 2.1-4.0 0 8.1-12.0 0 4.1-6.0 * 12.1-16.0 * 6.1-8.0 * >16.0 * > 8.0 Esri, Inc. Esrn, Inc.

Design Connection * < 4.0 41< 3.0 S4.1-6.0 * 3.1-6.0 0 6.1 -8.0 o 6.1-9.0 * 8.1-10.0 * 9.1-12.0 > 10.0 * > 12.0 Eui, Inc. Esri. Inc

N A0 5 10 20km

Figure 4-5 Density, Diversity, Design and Connection Score in Singapore

55 CjqA KM umaoo

* Completed * Under Construction -e HDB Development Boundaryv

Figure 4-6 Distribution of HDB Towns (HDB 2011Annual Report)

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Figure 4-7 Singapore Master Plan 2008 (Singapore Urban Redevelopment Authority 29 )

29 Available at: https://www.ura.gov.sg/uol/master-plan.aspx?p1=view-master-plan&p2=master-plan-2014

56 4.5 Summary

The TOD indicator calculations in this chapter allow a few generalizations.

First, Singapore has an overall higher TOD score than Beijing, which is largely because station areas tend to have higher population density, higher FAR density gradient, higher job density, higher ground-floor retail density and better transit connectivity. This suggests Beijing could increase density and connectivity at transit stations to enhance TOD. Second, for both Beijing and Singapore, the density dimension varies the most across different metro stations whereas the diversity dimension varies the least. Peripheral areas tend to have lower density, suggesting also an area for improving TOD-ness of the entire city. Third, stations with high density are mainly located within the 4th urban ring area in Beijing and are scattered in the entire Singapore due to the efforts of the government's decentralization of HDB housing supply. Finally, Singapore still has strong monocentric characteristic as suggested by its diversity, design and connectivity score, whereas Beijing is in an apparent transition to a more polycentric pattern.

A few limitations bear mentioning. The IEW method used in this thesis captures the deviation degrees among indicators and the results the TOD-ness measured in this thesis is most strongly fueled by density and less influenced by design. This conclusion is in line with the assumption that density provides the foundation for effective use of transit capacity. This may not be true in other TOD measurement methods. For example, the ITDP standard suggests equal weights of density, connectivity and land use mix. Future work could include a comparison of different weight assigning methods. Also, the data used for Beijing and Singapore are from different sources, which makes the results less comparable. Despite efforts to minimize the effects of the differences between the two datasets, future studies could still improve the data for intercity comparison.

57 5 Station-level Ridership

Chapter 4 examines TOD as a physical output as measured in Beijing and Singapore. This Chapter presents one outcome of TOD, station-level ridership, and its association with outputs. As illustrated in Chapter 2, while TOD has multiple outcomes associated with transit usage, environmental improvement, economic development and neighborhood revitalization, I only used station-level ridership as a proxy a TOD outcome. The models test the relationship between ridership and the TOD Index as well as the individual indicators comprising the TOD index; this provides some indication of the validity of the Index as a measure of TOD and also some measure of the amount of information "loss" when using the aggregate Index versus the individual indicators when predicting ridership effects.

In this Chapter, I first synthesized and visualized individual metro boarding data at the station level in Beijing and Singapore. Then I used a series of models, including basic Ordinary Least Square models (OLS), Spatial Lag models (SLM) and Spatial Error Models (SEM), to test the effect of TOD outputs on ridership and compare variation across different models. Finally I test the predictive power of different models in both cities.

5.1 Ridership Data

I obtained individual metro card records during December 28-30, 2015 for Beijing from Tshinghua Tongheng Planning Institute and a similar dataset for Singapore during April 11-13, 2011 from the FM project database. I aggregated the individual records to station-level daily ridership; daily boarding data were available for 266 Beijing metro stations and 78 Singapore MRT stations. I took the three-weekday average boardings as station-level ridership30 (Appendix A and Appendix B display the full results of station-level boardings). Table 5-1 presents the summary statistics of the average weekday boardings in Beijing and Singapore. Beijing has a slightly higher

30 For transfer stations on multiple lines, both datasets do not tell which boarding corresponds to each of them, so transfer stations with the same name are aggregated in only one observation. 58 mean value of 19,396 boardings, but this number varies more widely across stations than Singapore. The highest ridership appeared at Beijing West Station and the lowest appeared at Ciqu South Station. Singapore has a mean of 18,774, with the highest of 73,897 at Station and the lowest of 2,655 at Stadium Station.

Table 5-1 Statistics of Weekday Metro Station Boarding Weekday Beijing Singapore All Boardings N=266 N=78 N=344 Mean 19396 18774 19383 Std. Dev 15557 12746 14975 Minimum 348 2655 348 Maximum 98081 73897 98081

5.1.1 Spatial Distribution of Ridership

Figure 5-1 and Figure 5-2 map the spatial distribution of ridership in Beijing and Singapore.

For Beijing, we see that high ridership appears at rail terminals and transport hubs such as Beijing West Station, Beijing South Station, , , Sanyuanqiao and Guomao. Transfer stations are also hotspots for high ridership. Most stations with an average daily ridership higher than 40,000 boardings/day are on Line 1, 2, 5, 10, 13 and 14. Most stations with a daily ridershio lower than 2,000 boardings/day are located outside the 5 th urban ring of Beijing. For Singapore, most stations with an average daily ridership higher than 30,000 boardings/day are clusterd in the Downtown Core; Harbour Front Station and Station also have high ridership. Most of the high-ridership stations serve multiple lines and are primarily on the North South Line, North East Line and East West Line. Singapore does not have stations with a daily boardings less than 2000 and most stations with less than 5000 daily boardings are on the Circle Line.

59 -I

Oa xz

Beijing West 0

N

0 2.5 5 10 km

Ridership Beijing Metro Line - Line 15 - Line 6 - Batong Line

* 0-9191 - --- Under Construcion - Line 1 - Line 7 - Daxing Line

* 9192 - 18052 - Line 10 Line 2 - Line 8 - Fangshan Line 18053 - 28417 Line 13 Line 4 - Line 9 - Changping Line

28418 - 49895 - Line 14 Line 5 - Yizhuang Line - Airport Line 49896 - 98081

Figure 5-1 Beijing Average Weekday Metro Station Boarding (Dec, 2015)

60 % %g

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2.5 5 10 km Esri DeLonme, GEBCO. NOAA NGDC, and other contributors. Esri, Inc., 0 Souros: Esn. GEBCO. NOAA, National Geographic, DeLorme, HERE, Geonames.org, and other contributors

7OD Score - East West Line (EW) Circle Una (CC) - Sengkang LRT (SK) 0 2655 -8974 - Changi Airport Branch Lins (CG) Circle Uns Extension (CE) Punggol LRT (PG) o 8975 - 15503 -- East West Line (EW) (Under Construction) - Downtown bne (DT) - Bukit Panjang LRT (BP) 0 15504 -23069 North South Un (NS) - Downtown Line (DT) (Under Construction) - Sentosa Express (S) Q 23070- 36147 North South Une (NS) (Under Construction) Downtown Line (DT) (Planned) North East Une (NE) Thomson Line (TS) (Planned) 36148- 73897

Figure 5-2 Singapore Average Weekday Metro Station Boarding (April, 2012)

5.1.2 Transformation of Ridership

In the following sections I used linear regression models to test the effect of TOD indicators on ridership. The dependent variable, i.e. ridership, has positive numeric characteristics with a fat-left non-linear distribution. Thus, transforming this variable can help fulfill linear regression assumptions and also avoid negative model predictions. I logarithmically transformed the ridership data for modeling. Figure 5-3 and Figure 5-4 show the distribution

61 patterns of on-board weekday transit ridership for both the original data and the transformed data, showing that the logarithmic transformations are closer to a normal distribution.

25 14 Ridership - logRidership 12 20 1 10 15 8 LL 6 L 10

5

0 0 1 2 3 4 5 6 7 8 7 8 9 10 11 12 Singapore Weekday Metro Ridership 104 Singapore Weekday Metro Ridership

Figure 5-3 Original distribution and logarithmic transformation for weekday-boarding ridership in Singapore

100 80 logRidership 80 60 -

C 60

40 -

L- 40

20- 20

0 0 1 0 2 4 6 8 10 5 6 7 8 9 10 11 12 Beijing Weekday Metro Ridership x1o Beijing Weekday Metro Ridership

Figure 5-4 Original distribution and logarithmic transformation for weekday-boarding ridership in Beijing

5.2 Ridership and TOD Outputs

In this section, I estimate OLS, SLM and SEM models to test the relationship between TOD outputs and station-level ridership. In addition to TOD outputs, some other variables are also relevant to ridership and are included in the explanatory variables as controls, including:

0 Line-specific information: dummy variable per line.

62 This variable is used to capture line-specific information. For example, some subway lines are inherently designed to have larger capacity and faster speed and therefore more easily attract higher ridership. Also the construction year of a metro station is relevant to ridership, and could be, in part, captured by the line-specific dummies. I use the Line 1 as base category for Beijing and the Circle Line as base category for Singapore.

* Distance to CBD

This variable is used to approximate the relative location of a metro station in the region and is a proxy to measure centrality. Both cities are assumed to have single CBD. Beijing CBD is located in Chaoyang District near Guomao station and Singapore CBD is near Downtown Core.

* Transfer station: dummy variable.

Transfer stations are expected to have higher ridership.

* Distance to the nearest metro station.

It is typical in both Beijing and Singapore that metro stations cluster towards the urban centers and disperse towards the peripheries. Neighboring stations could share similar surrounding conditions and compete for ridership due to proximity. This variable is used to capture such effect.

5.2.1 OLS Model

The OLS model is the most common models adopted to test the effect of built environment elements on ridership (Kuby et al, 2004; Cervero and Murakami, 2010; Juan and Oh, 2011; Chen and Zegras, 2015; Reyes, 2016), so I first used simple OLS models assuming the following form:

y = XJ + E 5-1 I used Matlab to program the OLS models. I first tested the models on TOD score and the control variables (model (1) ). Then I ran the model on all 12 indicators and the control variables

63 (model (2)). I also calculated the Variation Inflator Factors (VIF) for all explanatory variables to assess multicollinearity. The VIF formula is expressed as:

VIFk = 1/(1 - R') 5-2 where R2 is the coefficient of determination of the regression with Xk on the left hand side, and all other X variables on the right hand side. "The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction"31 . I used ArcGIS to calculate the VIF and the test results are shown in Table 5-2, which suggest multicollinearity is not a serious issue in the models. The initial OLS model results are shown in Table 5-3 for Beijing and Table 5-4 for Singapore.

31 Cited from Penn State STAT501, Regression Methods, available at: https://onlinecourses.science.psu.edu/stat50l/node/347

64 Table 5-2 VIF for OLS Models(1)

Dependent variable: log (Ridership) Beijing OLS Model Singapore OLS Model Variable Coefficient VIF Coefficient VIF Constant 7.8512 ------7.3969 ------Building coverage ratio -1.8049 4.0237 0.5096 2.7826 Population Density 0.1413 3.1439 0.1558 2.0744 Density Gradient 0.1989 3.1241 0.6062 1.4677 Land Use Mix 0.7872 1.4519 0.0798 1.2637 Number of Work POI 0.0006 2.2710 -0.0006 1.3859 Road Density 0.0172 1.1701 0.0039 1.1877 Expressway Density 0.0283 1.1355 0.0131 1.3690 Ground retail Density 0.0505 2.2717 0.0166 4.1023 Parking -0.0036 1.8291 -0.0118 2.4999 Number of Entrance 0.0168 1.7732 0.0795 2.0282 Bus Line 0.0064 2.9307 -0.0023 4.0739 Bus Station 0.0077 2.6366 -0.0002 2.9412 Distance to CBD -0.0133 3.4548 -0.0133 2.4523 Distance to Nearest 0.3271 2.1277 0.1354 2.7175 Station Transfer Station 0.4211 1.6657 -0.0748 1.9644 Line 2 0.1528 1.7591 Line 4 0.0590 2.5622 Line 5 0.1734 2.1100 Line 6 -0.0651 2.3370 Line 7 -0.7322 2.0613 Line 8 -0.3518 1.8708 Line 9 -0.1966 1.4194 Line 10 -0.1236 2.6998 Line 13 0.2600 2.1693 Line 14 -0.6973 2.2036 Line 15 -0.6852 2.0426 Yizhuang Line -0.4263 2.1164 Batong Line -0.2508 1.8426 Changping Line 0.2457 1.5075 Fangshan Line -0.4635 1.9862 Airport Line -0.8280 1.3342 North South Line (NS) 1.0641 3.0417 North East Line (NE) 0.4846 2.3950 East West Line (EW) 0.8158 2.7669 Changi Airport Line (CG) 0.3217 2.0398

65 For Beijing, the TOD Index model (1) has an adjusted R square value of 0.5253 and model (2) has that of 0.5549. Most TOD indicators with high weights are statistically significant as shown by model (2) and that explains why using model (1) to predict ridership does not result in much worse results. For the density dimension, population density and density gradient are statistically significant and have a positive impact on ridership. Unexpectedly, the number of work POls is not significantly positively associated with ridership. It could be that the number of work POls is not the best proxy for job density in Beijing (and/or the POI data are inaccurate). As for the diversity dimension, both land use mix and ground-floor retail density are significant and are positively associated with ridership, as expected. Turning to the design dimension, expressway density and number of parking facilities are not significant; a possible reason for the latter could be that I only collected data on the number of parking facilities but the size of parking facilities instead may be more relevant. Road density is significant and positively associated with ridership, possibly indicating high permeable street network, while building coverage ratio is negatively associated with ridership, perhaps indicating superblock-type buildings. Turning to the connectivity dimension, only the number of bus stations is significant. As for control variables, distance to CBD, distance to nearest metro station and transfer stations are all significant with expected signs. Stations on Lines 7, 14, 15, Yizhuang Line, Fangshan Line and Airport Line have significantly less ridership than stations on Line 1.

66 Table 5-3 OLS Model Results for Beijing: Dependent variable: log (Ridership)

Explanatory variables model(1) model(2) Beta P-Value Beta P-Value Constant 8.2023 0.0000*** 7.8512 0.0000*** TOD score 0.0488 0.0000*** Density Population Density 0.1413 0.03329**

Number of Work POI 0.0006 0.1164

Density Gradient 0.1989 0.07942* Diversity Land Use Mix 0.7872 0.0072*** Ground retail Density 0.0505 0.00082*** Design Road Density 0.0172 0.0136** Expressway Density 0.0283 0.3341 Parking -0.0036 0.8908 Building coverage -1.8049 0.00272*** ratio Connectivity Number of Entrance 0.0168 0.5174 Bus Line 0.0064 0.3176 Bus Station 0.0077 0.02224** Distance to CBD -0.0016 0.8252 -0.0133 0.09535* Distance to Nearest 0.2869 0.00182*** 0.3271 0.00034*** Station Transfer Station 0.3942 0.00011*** 0.4211 0.00012*** Line- Line 2 0.1637 0.4164 0.1528 0.4494 Specific Line 4 0.0371 0.8280 0.0590 0.7314 Information Line 5 0.1652 0.4009 0.1734 0.3908 Line 6 -0.0239 0.8964 -0.0651 0.7300 Line 7 -0.6788 0.00087*** -0.7322 0.00043*** Line 8 -0.2580 0.2302 -0.3518 0.1038 Line 9 -0.2373 0.3558 -0.1966 0.4375 Line 10 -0.1020 0.5391 -0.1236 0.4715 Line 13 0.3676 0.0605 0.2600 0.2176 Line 14 -0.6450 0.00042*** -0.6973 0.00018*** Line 15 -0.6123 0.00193*** -0.6852 0.00066*** Yizhuang Line -0.6377 0.00362*** -0.4263 0.06406* Batong Line -0.0199 0.9293 -0.2508 0.2798 Changping Line 0.0342 0.9116 0.2457 0.4244 Fangshan Line -0.6147 0.0160 -0.4635 0.06674* Airport Line -0.7983 0.083* -0.8280 0.06955* obs 269 269 R-square 0.5607 0.6064 Adjusted R-square 0.5253 0.5549 F value 15.8272 11.7764 Prob > F 0.0000 0.0000 67 For Singapore, only four TOD indicators are significant as shown by model (2). As the composite TOD index is a combination of all TOD indicators, it contains 'irrelevant' information for ridership prediction in Singapore and thus model (1) has a much lower value of adjusted R square. In the density dimension, Population density and density gradient are significant and have positive impacts on ridership. However, the number of work POls is negatively associated with ridership. It could be that work POIs data is not the best proxy for job density in Singapore. Turning to the diversity and design dimensions, surprisingly all indicators in these dimensions are not significant for Singapore. Possible reasons could include that, (a) the POls are not the best proxies for land use types and (b) the work POI dataset may be incomplete. Turning to the connectivity dimension, only the number of metro entrances is significant for Singapore. Number of metro entrances can be viewed as a proxy of the scale of metro station and thus the results reflect that larger stations in Singapore have higher ridership; and/or, more station entrances make the stations more accessible from throughout the area of influence. As for the control variables, only line-specific dummies are significant. Stations on the North South Line, the North East Line and the East West Line have significantly higher ridership than stations on the Circle Line. Possible reasons include (a) the Circle Line was opened in 2009 and is very new compared to other lines and (b) the Circle Line is designed to be a medium-capacity line for its current stage. As a medium capacity line, each Circle Line train has only three cars instead of the six-car configuration as seen on other MRT lines and therefore it is designed to have lower ridership.

While the best Singapore OLS model includes less TOD indicators than the Beijing model, it has a higher adjusted R 2 value.

68 Table 5-4 OLS Model Results for Singapore: Dependent variable: log (Ridership)

Explanatory variables model(e) model(2) ______Beta P-Value Beta P-Value Constant 8.0687 0.0000*** 7.3969 0.0000*** TOD score 0.0191 0.0188** Density Population Density 0.1558 0.00016*** Number of Work -0.0006 0.02063** POI Density Gradient 0.6062 0.00133*** Diversity Land Use Mix 0.0798 0.7158 Ground retail 0.0166 0.1263 Density Design Road Density 0.0039 0.5325 Expressway 0.0131 0.7091 Density Parking -0.0118 0.4368 Building coverage 0.5096 0.5629 ratio Connectivity Number of 0.0795 0.02232** Entrance Bus Line -0.0023 0.8399 Bus Station -0.0002 0.9730 Distance to CBD -0.0034 0.8080 -0.0133 0.2721 Distance to 0.0266 0.8712 0.1354 0.3230 Nearest Station Transfer Station -0.1570 0.4047 -0.0748 0.6697 Line- North South Line 1.2483 0.0000*** 1.0641 0.0000*** Specific (NS) Information North East Line 0.8918 0.0000*** 0.4846 0.0061** (NE) East West Line 1.1011 0.0000*** 0.8158 0.0000*** (EW) Changi Airport 0.5358 0.2085 0.3217 0.4146 Line (CG) obs 78 78 R-square 0.5835 0.7858 Adjusted R-square 0.5352 0.7156 F value 12.084 11.1193 Prob>F 0 0

69 5.2.2 Spatial Models

The dependent variable I used has a spatial component (ridership at one station could be correlated with ridership at neighboring stations) and space also plays a role in variable measurement. These could result in spatial autocorrelation, which has been found in previous similar studies (Cervero and Murakami, 2010; Chen and Zegras, 2015; Reyes, 2016). Spatial autocorrelation causes problems for simple OLS methods that make assumptions about the independence of residuals. In order to consider the spatial effect, I first ran Moran's I test on both ridership and the residuals of the OLS model and then adopted spatial models to test my results. In the following section, I used Geoda software and adopted the Queen Contiguity Method (order =1) to assign spatial weights and run spatial models. Figure 5-5 and Figure 5-6 shows the histogram of number of neighboring stations using the Queen Contiguity weight matrixes for Beijing and Singapore accordingly.

2 4 6 8 10 12 14 Number of Neighbors

Figure 5-5 Histogram of the Number of Neighbors (Beijing)

70 Co

O -

2 3 4 5 6 7 8 9 10 11 12 Number of Neighbors

Figure 5-6 Histogram of the Number of Neighbors (Singapore)

5.2.2.1 Spatial Autocorrelation Analysis

"Spatial autocorrelation analysis tests whether the observed value of a variable at one location is independent of the values of the variable at its neighboring locations"32. The Moran I test is a commonly used method to test the existence of spatial autocorrelation. The Moran I index is expressed as:

N Zi jwij(Xi-X)(Xj-X) 5-3 Li zj wij zitXi - 9), Where N is the number of spatial units indexed by i and j (i # j), X is the varable to be tested and wi- is an element of a matrix of spatial weights.

I first adopted the Moran I test on ridership for Beijing (see Appendix C) and Singapore (see Appendix D). The results suggest a spatial autocorrelation for Beijing log(Ridership) at 1% cautionary level and for Singapore at 5% cautionary level. I then test on the residuals of the

32 Cited from GIScience in Research, available at: http://ibis.geog.ubc.ca/courses/geob479/notes/spatial analysis/spatial autocorrelation.htm

71 previous best OLS models. The results show that the residuals of the Beijing OLS model is spatially clustered at 5% cautionary level (see Appendix E) whereas the residual of Singapore OLS model is spatially dispersed at 5% cautionary level (see Appendix F). The tests tells that spatial autocorrelation do exist in ridership data. Parts of effect of the spatial autocorrelation, however, are already explained by the OLS model. This could result from the fact that all explanatory variables also have spatial components and are also spatially correlated in the same pattern as ridership. Since the residuals of both models are still spatially auto correlated, I adopted spatial models, including the spatial lag model and the spatial error model in the following sections.

5.2.2.2 Spatial Lag Model and Spatial Error Model

There are two primary types of spatial dependence. The first one is that the dependent variable y at location i is affected by the independent variables at both location i and j and the second one is that the error terms across different spatial units are correlated. Spatial lag models (SLM) account for the first type of dependence and spatial error models (SEM) for the second.

Spatial lag models produce an additional repressor in the form of a spatially lagged variable on the right hand side of a regression equation (Baltagi, 2001). A spatial lag model can be formally expressed as the following:

y=pWy+Xfl+ e 5-4 Where p is a spatial autoregressive coefficient, Wy is the spatially lagged dependent variable for weights matrix W, X is the matrix of observations on the explanatory variables, fl is a vector of coefficients, and E is a vector of error terms. The SLM says that "levels of the dependent variable y depend on the levels of y in neighboring regions. It is thus a formulation of the idea of a spatial spillover" (Viton, 2009, pp.10).

A spatial error model (SEM) considers the estimation of maximum likelihood of a spatial regression model that includes a spatial autoregressive error term on the right hand side of the regression equation (Hartsell, 2012). A spatial error model can be formally expressed as below:

y = X# + E 5-5

72 = XWE + y 5-6 so that the model is

y = X9 + (I - XW)-p 5-7 where W is the spatial weights matrix, E is a vector of spatially auto correlated error terms, [ is a vector of independent identically (i.i.d.) distributed errors, and X is a parameter.

I used GeoDa to run spatial lag and spatial error models for both Beijing and Singapore. The results are summarized in Table 5-5 and Table 5-6 Table 5-6.

For Beijing (see Table 5-5), the number of work POls and Line 8 dummy become significant in both spatial models and the sign of the coefficients does not change. Also the value of each coefficient is quite similar among the OLS, SLM and SEM models. Neither the spatial coefficient Rho for SML or lambda for SEM is significant, which possibly suggests that (a) the spatial dependence of Beijing ridership is very weak or (b) the spatial dependence of Beijing ridership has already been explained by explanatory variables in the OLS model, i.e., explanatory variables are also spatially correlated in the same way as ridership. This could be reasonable since in the inner urban area of Beijing (see Figure 5-7), metro stations are located very close to each other and the 800-meter radius service area of one station overlaps at a very high ratio with other of other stations. Therefore, the independent variables are also spatially correlated. Since the spatial models are estimated with the maximum likelihood method, I choose the model with the highest log-likelihood, or with the lowest Akaike info criterion (AIC) or the lowest Schwarz (or Bayesian Information or BIC) criterion. In this case, the SLM is a better fit for Beijing. However, the three models do not change the result drastically.

For Singapore (see Table 5-6), we see that both spatial models do not change the sign of the coefficients and ground-floor retail density become significant in spatial models. Spatial models do not change the value of coefficients drastically compared to the OLS model. The spatial coefficient Rho for SML is significant at 5% cautionary level and lambda for SEM is significant at 1% cautionary level, which validates that spatial dependence exist for Singapore ridership. For Singapore, SEM is the best model for Singapore, as suggested by the log-likelihood.

73 Table 5-5 Spatial Model Results for Beijing Dependent variable: log (Ridership) OLS SLM SEM Explanatory variables Beta P-Value Beta P-Value Beta P-Value Constant 7.8512 0*** 8.1924 0*** 7.8534 0*** Population Density 0.1413 0.0333** 0.1470 0.0201** 0.1414 0.0220**

Number of Work POI 0.0006 0.1164 0.0006 0.0862* 0.0006 0.0936*

Density Gradient 0.1989 0.0794* 0.1980 0.0616* 0.1987 0.0604* Land Use Mix 0.7872 0.0072*** 0.7932 0.0037*** 0.7862 0.0039*** Ground retail Density 0.0505 0.0008*** 0.0509 0.0003*** 0.0504 0.0003*** Road Density 0.0172 0.0136** 0.0175 0.0073*** 0.0173 0.0078*** Expressway Density 0.0283 0.3341 0.0286 0.2973 0.0287 0.2959 Parking -0.0036 0.8908 -0.0032 0.8974 -0.0035 0.8874 Building coverage -1.8049 0.0027*** -1.7909 0.0014*** -1.8003 0.0012*** ratio Number of Entrance 0.0168 0.5174 0.0167 0.4924 0.0167 0.4915 Bus Line 0.0064 0.3176 0.0067 0.2735 0.0064 0.2897 Bus Station 0.0077 0.0222** 0.0076 0.0153** 0.0077 0.0139** Distance to CBD -0.0133 0.0954* -0.0137 0.0671* -0.0133 0.0710 * Distance to Nearest 0.3271 0.0003*** 0.3275 0.0001*** 0.3277 0.0001*** Station Transfer Station 0.4211 0.0001*** 0.4194 0.0000*** 0.4197 0.0000*** Line 2 0.1528 0.4494 0.1481 0.4338 0.1494 0.4297 Line 4 0.0590 0.7314 0.0611 0.7049 0.0577 0.7193 Line 5 0.1734 0.3908 0.1706 0.3671 0.1712 0.3649 Line 6 -0.0651 0.7300 -0.0670 0.7046 -0.0673 0.7031 Line 7 -0.7322 0.0004*** -0.7474 0.0001*** -0.7361 0.0001*** Line 8 -0.3518 0.1038 -0.3476 0.0860* -0.3524 0.0805* Line 9 -0.1966 0.4375 -0.2069 0.3836 -0.2002 0.3981 Line 10 -0.1236 0.4715 -0.1273 0.4288 -0.1263 0.4314 Line 13 0.2600 0.2176 0.2649 0.1810 0.2584 0.1894 Line 14 -0.6973 0.0002*** -0.7030 0.0000*** -0.6992 0.0001*** Line 15 -0.6852 0.0007*** -0.6938 0.0002*** -0.6874 0.0002*** Yizhuang Line -0.4263 0.0641* -0.4415 0.0420** -0.4286 0.0453** Batong Line -0.2508 0.2798 -0.2625 0.2272 -0.2562 0.2369 Changping Line 0.2457 0.4244 0.2622 0.3654 0.2482 0.3863 Fangshan Line -0.4635 0.0667* -0.4800 0.0440** -0.4648 0.0477** Airport Line -0.8280 0.0696* -0.8393 0.0489** -0.8350 0.0499** Rho -0.0369 Wjlog(Ridership) -0.0369 0.6586 Lambda -0.0129 0.9001 obs 269 269 269 R-square 0.6064 0.6067 0.6064 Adjusted R-square 0.5549 0.5515 0.5530

74 Log-likelihood -205.796 -205.713 -205.790 Akaike info criterion 475.592 477.426 475.58 Schwarz criterion 590.623 596.051 590.611

75 Table 5-6 Spatial Model Results for Singapore: Dependent variable: log (Ridership)

SEM Explanatory variables OLS SLM Beta P-Value Beta P-Value Beta P-Value Constant 7.3969 0*** 9.4799 0*** 7.4051 0*** Population Density 0.1558 0.0002*** 0.1522 0*** 0.1668 0*** Number of Work POI -0.0006 0.0206** -0.0007 0.0019*** -0.0006 0.0037*** Density Gradient 0.6062 0.0013*** 0.6151 0.0001*** 0.5070 0.0008*** Land Use Mix 0.0798 0.7158 0.1114 0.5443 0.0540 0.7630 Ground retail Density 0.0166 0.1263 0.0157 0.0795* 0.0149 0.0910* Road Density 0.0039 0.5325 0.0031 0.5499 0.0010 0.8496 Expressway Density 0.0131 0.7091 0.0221 0.4542 0.0235 0.4068 Parking -0.0118 0.4368 -0.0166 0.1975 -0.0079 0.4664 Building coverage 0.5096 0.5629 0.3332 0.6508 0.5202 0.4121 ratio Number of Entrance 0.0795 0.0223** 0.0916 0.0015*** 0.0925 0.0009*** Bus Line -0.0023 0.8399 0.0022 0.8230 -0.0036 0.6851 Bus Station -0.0002 0.9730 -0.0006 0.9131 -0.0014 0.7638 Distance to CBD -0.0133 0.2721 -0.0137 0.1724 -0.0094 0.3778 Distance to Nearest 0.1354 0.3230 0.1421 0.2116 0.1949 0.1089

Transfer Station -0.0748 0.6697 -0.1480 0.3198 -0.1765 0.1905 North South Line (NS) 1.0641 0*** 1.0949 0*** 1.0368 0*** North East Line (NE) 0.4846 0.0061*** 0.5317 0.0002*** 0.5461 0.0000***

East West Line (EW) 0.8158 0*** 0.8263 0*** 0.8265 0***

Changi Airport Line 0.3217 0.4146 0.3270 0.3184 0.3005 0.2813 (CG) Rho -0.2211 W_og(Ridership) -0.2211 0.0476** Lambda -0.7300 0.0001*** obs 78 78 78 R-square 0.7858 0.7981 0.8223 Adjusted R-square 0.7156 0.7224 0.7557 Log-likelihood -24.1919 -22.2302 -20.5851 Akaike info criterion 88.3837 86.4604 81.1701 Schwarz criterion 135.518 135.951 128.304

76 4 / K / 'I' '2 j ~1~z-;~ I I (~V lt I I 4 IC, 4 Fit I0 .. 44w4 ~ ~ ...... oo", - N IL "a, * Beijing Metro Station I N 800 meter buffer 0 0.5 1 2 Miles I i I 1 1 1 1 Water area

Figure 5-7 The Overlapped Buffer in the Inner City of Beijing Table 5-7 summarizes the effect of TOD indicators on ridership as supported by the model results in Beijing and Singapore. Beijing ridership is positively associated with population density, number of work POls, density gradient, land use mix, ground-floor retail density, road density, and number of bus stations and negatively associated with building coverage ratio. For Singapore, population density, density gradient, ground-floor retail density and the number of MRT station entrance have a positive impact on ridership while the number of work POls and number of parking facilities have a negative impact on ridership. For the four shared explanatory variables, population density and number of metro station exits have higher impact on ridership in

77 Singapore, ground-floor retail density has higher impact in Beijing while number of work POls has opposite effects in the two cities.

For the four control variables, Beijing station ridership is positively affected by distance to nearest metro station and transfer station and negatively affected by distance to CBD. Except line-specific dummies, all control variables are not significant in Singapore. Overall, Singapore models have better fit than Beijing models, as measured by the value of the R square and log- likelihood.

78 Table 5-7 Summary of Relationships between TOD Indicators and Metro Ridership

Expected Identified Relationship Explanatory variables Effect Beijing Singapore Density Population Density + + + Number of Work POI + + Density Gradient + + + Diversity Land Use Mix + + Ground retail Density + + + Design Road Density + + Expressway Density Parking Building coverage ratio Connectivity Number of Entrance + + Bus Line + Bus Station + + Distance to CBD Distance to Nearest Station + Transfer Station + + Line-Specific Line 2 Information Line 4 Line 5 Line 6 Line 7 Line 8 Line 9 Line 10 Line 13 Line 14 - Line 15 - Yizhuang Line - Batong Line Changping Line Fangshan Line - Airport Line North South Line (NS) + North East Line (NE) + East West Line (EW) +

Changi Airport Line (CG)

79 5.2.3 Cross Validation and Model Comparison

I used different models to test the effect of TOD outputs on ridership. This section compares the predictive power of different models across the two cities. For OLS models, I adopted the K-fold cross validation method and used the Beijing OLS model to predict Beijing ridership and used Singapore OLS model to predict Singapore ridership. Then I used Singapore OLS model to predict Beijing ridership and vice versa. By comparing the results of different OLS predictions, I could see the level of higher mean square error introduced by using the model of a different city to make prediction and decide whether a uniform model is possible across different cities. I used Matlab to complete the above process.

I used Geoda to estimate spatial models and the software automatically reports prediction error of cross validation. I also compare the mean square error of OLS, SLM and SEM to validate the conclusions in the previous section that SEMs are the best models for both Beijing and Singapore. I did not use the Singapore spatial model to predict Beijing ridership or vice versa as the Geoda software does not allow out-sample cross-validation. This could be included in future work.

I map section the distribution of ridership over-prediction and under-prediction using different models to identify the spatial distribution

5.2.3.1 K-fold Cross Validation

Cross validation is a model evaluation method that tells how good the model is in terms of make prediction using new data. The K-fold cross validation method first divides the data into k subsets. Each time, one of the k subsets is used as the test dataset and the remaining k- 1 subsets are pooled as a training dataset; then the average error across all k trials can be computed.

Table 5-8 summarizes the mean square error of different model predictions. It validates that SLM is the best model for Beijing and SEM is the best model for Singapore as the cross validation results suggest that SEM has the lowest mean square error and thus the best predictive 80 power. Overall Singapore models have better predictive power than Beijing models. Applying the Singapore OLS model to Beijing generates a mean square error of 0.3034, 0.0333 higher than the Beijing OLS model and a 0.0310 higher mean square error is introduced vice versa. The increased mean square error is relatively small and therefore a uniform model that links TOD outputs to ridership is not impossible across different cities. Table 5-9 summarizes the absolute value of prediction residuals of different model predictions and shows a similar pattern.

Table 5-8 Mean Square Error of Different Model Predictions

Mean Square Error Beijing Singapore Beijing OLS 0.2704 0.1399 Beijing SLM 0.2701 Beijing SEM 0.2704 Singapore OLS 0.3034 0.1089 Singapore SLM 0.1026 Singapore SEM 0.0902

Table 5-9 Absolute Value of Prediction Residuals of Different Model Predictions

Singapore Beijing

absolute value of prediction residual absolute value of prediction residual SG-OLS SLM SEM BJ-OLS BJ-OLS SLM SEM SG-OLS mean 4878 4916 4813 5062 6909 6895 6906 7145 std 4786 4836 5543 5118 9148 9171 9151 10009 max 22229 22225 29516 27213 80628 80995 80739 84715 min 121 115 59 118 7 8 13 11

5.2.3.2 Prediction Error

To assess the overall magnitude of prediction errors, I converted the prediction error of the OLS and SEM from the logarithmic form back to the original form and mapped the residuals in the form of original ridership.

81 For Beijing, Figure 5-8, Figure 5-9 and Figure 5-10 map the spatial distribution of prediction residuals using Beijing OLS, Beijing SLM and Singapore OLS models respectively. Beijing OLS underpredicts ridership for 134 stations and overpredict 135 stations with an average absolute residual of 6909 boardings/day. The model underpredicts ridership for most stations on Line 10 and the Western end of Line 1 and overpredicts ridership for most stations on the middle part of

Line 5. Additionally, a lot stations with high prediction error were on Line 2. Line 2 and Line 10 are circle lines, which may be a relevant factor but is not included in the model. Also, the ridership of some transit hubs, such as Beijing West Station and Beijing South Station, is underpredicted. The Beijing SLM model displays almost the same pattern as the Beijing OLS model. The overall absolute prediction residual is slightly smaller for this model, which is in line with the previous conclusion that spatial dependence is already explained in the OLS model. The Singapore OLS model shows a similar result but overall underpredicts more ridership in Beijing. It underpredicts ridership for 172 stations and overpredicts ridership for 97 stations. This could result from the fact that the Singapore model omits the great effect of land use mix, road density and bus station on ridership in Beijing.

82 Aft I V Li

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0 2.5 5 10 km I I I I I I I

Underpredict by > 30k 40 Overpredict by -5k - - -- Under Construction -- Une 4 - Batong Une S Uderpredict by 20k-30k Overpreidct by 5k-10k - Line 10 - Une 5 - Dexing Line S Uderpredict by 10k-20k Overpredict by 1Ok-20k Line 13 - Une 6 -- Fangshan Une S Uderpredict by 5k-10k Oveifpredict by >20k - Une 14 - Une 7 -- Changping Une 0 Uderpredict by <5k -- Une 15 - Une 8 -- Airport Line

- Une 1 -Line 9

-Line 2 - Yizhuang Une

Figure 5-8 Beijing Ridership Prediction Using Beijing OLS model

83 K

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0 2.5 5 10 km I 1 1 I

0 Underprecict by ) 30k 0 Overpredict by <5k ---- Under Conatuiction - Une 4 - Batong Une Uderpredict by 20k-30k 0 Overpreidct by 5k-10k - Une 10 - ne 5 - Daxng Line by 1Ok-20k Overpredict by 10k-20k r--ne 13 -- Une 6 -- Fangehan Line 0 Uderpredict 0 Uderpredict by 5k-10k Over[predict by >20k - Une 14 - Une 7 - Changping Une Uderpredict by -5k - Une 15 - Une 8 --- Airport Une - Une - Une 9 - Une 2 - YizhuangUne

Figure 5-9 Beijing Ridership Prediction Using Beijing SLM model

84 @0

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0 2.5 5 10 km L 1 i1 LJi wJ

0 Underpredict by > 30k 0 Overpredict by <5k ---- Under Construction - Unr 4 - Batong Una S Uderpredict by 20k-30k 0 Overpreidt by 5k-10k - Un 10 - ne 5 --- Daxing Uins 0 Uderpredict by 10k-20k 0 Overpredlct by 10k-20k Uns 13 - ne 6 --- Fangshan Un 0 Uderpredict by 5k-10k 0 Overdpredict by >20k -UUn 14 - ne 7 -- Changping Uina S Uderpredict by <5k - ne 15 -ine 8 ---- Airport Uina - ne 1 - Un 9

- Uisn 2 - Yizhuang Une

Figure 5-10 Beijing Ridership Prediction Using Singapore OLS model

85 For Singapore, Figure 5-11, Figure 5-12 and Figure 5-13 map the spatial distribution of prediction residuals using Singapore OLS, Singapore SEM and Beijing OLS models respectively. The Singapore OLS underpredicts ridership for 45 stations and overpredict 33 stations with an average absolute residual of 4878 boardings/day. The model tends to underpredict stations on the NS and EW Lines near the Downtown Core. It also underpredicts ridership at stations on the Circle Line in the Northern part but overpredicts stations on the Circle Line near Downtown Core. During my fieldtrip to Singapore, I found that the headways during peak hour for stations near Downtown Core is very short, which enables higher ridership. If a better dataset is available, future studies could include headway as a variable to overcome the shortcomings. Similar spatial patterns, with milder mean square errors and absolute values of residuals, appear for the Singapore SEM. One difference for the SEM is that this model tends to underpredict most stations in the Downtown Core. The Beijing OLS model displays similar distribution with higher variance and prediction error, and overall Beijing OLS model underpreidcts more station in Singapore. This could be explained by the fact that the Beijing OLS introduced extra variables that prove insignificant for the Singapore model.

86 di I

qb ,4

N A 0 2.5 5 10 km Esn, DeLorme, GEBCO. NOAA NGDC, and other contnbutors. Esri, Inc., Sources: Esri, GEBCO. NOAA, National Geographic. DeLorme, HERE, I a a1 M a a 1 Geonames.org, and other contributors

Underpredict by > 30k - East West Line (EW) Downtown Line (DT) Uderpredict by 20k-30k - Changi Airport Branch Line (CG) Downtown Line (DT) (Under Construction) Uderpredict by 10k-20k East West Line (EW) (Under Construction) Downtown Line (DT) (Planned) Uderpredict by Sk-10k - North South Line (NS) Thomson Line (TS) (Planned) 0 Sengkang 0 Uderpredict by <5k North South Line (NS) (Under Construction) LRT (SK) Overpredict by <5k - North East Line (NE) Punggol LRT (PG) Overpreidct by 5k-10k Circle Line (CC) Bukit Panjang LRT (BP) Overpredict by 10k-20k Circle Line Extension (CE) Sentosa Express (S) Over[predict by >20k

Figure 5-11 Singapore Ridership Prediction Using Singapore OLS model

87 40 I

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0 2.5 5 10 km Esd, DeLorme, GEBCO, NOAA NGDC, and other contributors, Esri, Inc, Sources: Esri, GEBCO, NOAA, National Geographic, DeLorme, HERE, Geonarnes.org, and other contributors

0 Underpredict by > 30k - East West Line (EW) - Downtown Line (DT) 0 Uderpredict by 20k-30k - Changi Airport Branch Line (CG) - Downtown Line (DT) (Under Construction) S Uderpredict by 10k-20k East West Line (EW) (Under Construction) Downtown Line (DT) (Planned) 0 Uderpredict by 5k-10k - North South Line (NS) -- Thomson Line (TS) (Planned) Uderpredict by <5k -- North South Line (NS) (Under Construction) Sengkang LRT (SK) S Overpredict by <5k - North East Line (NE) - Punggol LRT (PG) 0 Overpreidct by 5k-10k Circle Line (CC) - Bukit Panjang LRT (BP) S Overpredict by 10k-20k Circle Line Extension (CE) - Sentosa Express (S) 0 Over[predict by >20k

Figure 5-12 Si ngapore Ridership Prediction Using Singapore SEM model

88 440

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0 2.5 5 10 kon Esn, DeLorme, GEBCO, NOAA NGDC, and other contributors, Esri, Inc., Sources: Esri, GEBCO. NOAA, National Geographic, DeLorme, HERE, Geonames.org, and other contributors

S Underpredict by > 30k - East West Line (EW) - Downtown Line (DT) 0 Uderpredict by 20k-30k - Changi Airport Branch Line (CG) - Downtown Line (DT) (Under Construction) 0 Uderpredict by 10k-20k East West Line (EW) (Under Construction) Downtown Line (DT) (Planned) S Uderpredict by 5k-10k - North South Line (NS) Thomson Line (TS) (Planned) S Uderpredict by <5k North South Line (NS) (Under Construction) Sengkang LRT (SK) Overpredict by <5k - North East Line (NE) Punggol LRT (PG) 0 Overpreidct by 5k-10k Circle Line (CC) - Bukit Panjang LRT (BP) Overpredict by 10k-20k Circle Line Extension (CE) - Sentosa Express (S) 0 Overqpredict by >20k

Figure 5-13 Singapore Ridership Prediction Using Beijing OLS model

89 5.3 Summary

The results show, first, that Beijing continues to display relatively polycentric characteristics in that metro ridership in Beijing tends to peak around transport hubs and transit terminals in multiple locations. Singapore has stronger monocentric features with high-ridership stations clustering in the Downtown Core.

Second, all four TOD dimensions reveal relationships with ridership in Beijing. In Beijing, ridership is positively driven by population density, number of work POls, density gradient, land use mix, ground-floor retail density, road density, and number of bus stations; it is negatively affected by building coverage ratio. For Singapore, population density, the density gradient, ground-floor retail density and the number of MRT station entrances have a positive impact on ridership while the number of work POls has a negative impact on ridership.

Third, ridership data in both Beijing and Singapore have spatial dependence; spatial error models prove to have the best fit for both cities. Overall Singapore models have better fit and better predictive power.

Fourth, the cross validation analysis and model prediction error comparison, suggests that a uniform model that links TOD outputs to ridership is possible across different cities.

Some limitations need to be mentioned. First, as this thesis is focused on the outputs and outcomes of TOD, some indicators, less relevant to TOD-ness, are not included in the models but may also affect ridership. For example, the headway of subways could also impact ridership. Also, this thesis does not consider the temporal dimension. The construction time of metro line could also impact ridership.

Second, the causality between TOD outputs and outcome is ambiguous. The causality can go both ways: metro stations might be added in dense neighborhoods to facilitate travel and neighborhoods might densify in various ways to take advantage of transit availability. The models used in this thesis indicate correlation but cannot confirm the causality. Future work could

90 include a temporal lag variable to measure the change in density since the station opened to better capture the causality between transit lines and urban density.

Finally, the datasets of ridership and TOD outputs are from different sources and different times, which could negatively affect the results.

91 6 Conclusion and Discussion

6.1 TOD Performance

This thesis examines TOD performance in two different Asian cities, by relating TOD outputs to outcomes. Specifically, the thesis constructed a TOD indicator framework to measure the level of existing TOD in Beijing and Singapore in the dimensions of density, diversity, design and connectivity. Using station-level ridership as the outcome of TOD, I modeled the relationship between TOD indicators and ridership in the two cities. In this way, the thesis was therefore able to answer the following research questions.

1. What is the level of existing station-level metro-based TOD in Beijing compared to Singapore?

Overall, this thesis identified that TOD, as measured by observable physical characteristics typically applied in the Western context, exist in both Beijing and Singapore. Taking transit nodes as key factors for TOD, both Beijing and Singapore have strong TOD characteristics in the urban rail station area measured from the perspective of density, diversity, design and connectivity. Indeed, both cities have a development density much higher than many cities in Western nations

Singapore generally has better TOD outputs for its MRT stations than Beijing due to a higher population density, higher FAR density gradient, higher job density, higher ground-floor retail density and better transit connection. But some stations in Beijing's CBD, such as Guomao, have extremely strong TOD characteristics and a TOD score higher than any Singapore station. As for the detailed four dimensions, Singapore has higher density and better transit connectivity surrounding the MRT stations while Beijing has slightly more diversified land use and better indicators of design than Singapore.

In the perspective of spatial distribution, both Beijing and Singapore display a TOD score gradient from the inner city to the outskirts. Beijing TOD score in general decreases along the urban radical roads with high-score stations clustering around transit hubs, high-tech company

92 clusters and commercial centers such as Beijing West Station area, Zhongguancun area and

Guomao area. Stations outside the 4 th Ring Road have very low TOD score. Singapore, similarly, has high-score stations all located in the urban center. For the four detailed dimension, Beijing

has most of its population within the 4th Ring Road area while Singapore has a more dispersed population distribution due to the HDB housing supply strategies. However, the diversity, design and connection dimensions still display stronger monocentric characteristics in Singapore than in Beijing.

At macro level, the integration of urban rail plans and urban development also display strong TOD characteristics in Beijing and Singapore. It is evident from a visual comparison between the urban layout and MRT network distribution that the urban rail network serves and connects urban centers and sub centers in both cities, especially in inner urban areas.

2. What are the TOD indicators associated with station-level metro ridership in Beijing and Singapore?

Using metro ridership as an outcome of TOD, Beijing continues to display relatively polycentric characteristics in that metro ridership in Beijing tends to peak around transport hubs and transit terminals in multiple locations. Singapore has stronger monocentric features in that high-ridership stations cluster in Downtown Core.

As for the relationship between TOD outputs and outcomes, TOD indicators from all four dimensions affect ridership in Beijing while only density and connectivity dimensions have significant effect on ridership in Singapore. Beijing ridership is positively driven by population density, number of work POls, density gradient, land use mix, ground-floor retail density , road density, and number of bus stations and negatively influenced by building coverage ratio. For Singapore, population density, density gradient, ground-floor retail density and the number of MRT station entrance have a positive impact on ridership while the number of work POls and number of parking facilities have a negative impact. The reasons for these differences warrant further investigation. Perhaps, for example, Singapore's MRT attracts ridership irrespective of some built environment factors due to the city's road pricing, high car ownership costs, and well-

93 integrated and high quality (and relatively affordable) transit service. For the four shared indicators that affect ridership in both cities, population density and the number of metro station exits have higher impact on ridership in Singapore, ground-floor retail density has higher impact in Beijing while number of work POls has opposite effects in the two cities. The Singapore models present better fit and better predictive power for ridership than Beijing models.

3. Compared to Singapore, what improvements could be made to improve the TOD performance in Beijing?

While Beijing already has strong TOD characteristics, it still has an overall worse TOD score than Singapore. To improve the TOD outputs, Beijing could plan for higher density and transit connectivity, especially on the outskirts outside the 4 th Ring Road, to achieve better overall TOD performance. In the suburban districts of Beijing, land use development and transit infrastructure are still weak, which could be improved in future plans for the decentralization areas within the

4 th Ring Road.

The transit mode share was 48% for Beijing and 63% for Singapore in 2014. To increase metro ridership in Beijing, more diversified land uses in TOD areas could help to avoid unidirectional transit ridership. To encourage transit use, Beijing could also consider policies such as road-use surcharge.

Additionally, non-motorized travel modes like cycling could also be encouraged. The bike- sharing program has gained its popularity in Beijing since 2015. The two biggest bike sharing providers, OFO and Mobike, are still competing extensively to expand market share. Future urban plans could incorporate urban designs with the bike hubs to encourage non-motorized travel mode. This is an area worth further study as bikesharing is relatively new in both cities.

6.2 Challenges to TOD in Beijing

The Beijing models show that various physical environment measures are associated with ridership, so in theory such measures could be pursued at select low-ridership stations to strategically grow rail system ridership. That said, various challenges to intervening in the built 94 environment exist in Beijing, as revealed through semi-structured interviews of various stakeholders in China (see Appendix G). These include the lack of coordination among different departments and agencies and conflicts between the planning code and TOD design principles. For example, In the Code for Transport Planning on Urban Road (GB50220-95), the overall road network density is required to be 7km/km 2. According to this requirement, the average block size is about 400*400m, which is a typical super-block type of road network in conflict with TOD design guidelines. The Beijing models show that building coverage ratio is negatively associated with station ridership; that ratio is reflective of the typical Beijing superblock, which may be negatively impacting ridership. The lack of flexibility in Beijing's planning policies and regulations also hampers TOD implementation in the city; land use plans in suburban areas, for example, may not adapt appropriately when new stations arrive. Furthermore, the absence of some key regulations (such as for underground space land leases) can negatively affect TOD possibilities as planners and developers prefer avoiding potential political mistakes. More generally, the implementation of TOD requires efforts from multiple stakeholders including the local government, real estate developers, urban planners and transit operators, introducing high communication and coordination cost for all sides. The political and personal performance rewards are apparently not perceived to be worth it. Overall, proactive TOD practice remains rare in Beijing.

6.3 Limitations and Future Work

This study has various limitations.

First, this thesis did not differentiate the types of TOD. The location and type of a metro station could affect its TOD-ness. For example, a transfer station in the urban center could differ greatly from a non-transfer station on the outskirts in its station-area attributes and ridership. Future work could include this variable in the TOD score calculation and ridership models.

Second, the methods used to account for spatial autocorrelation assume that spatial dependence exits only in neighboring stations. It is possible, however, that spatial dependence

95 exits along different metro lines. Future models could consider such impacts by introducing dummy variables of metro line information.

Third, the data used for Beijing and Singapore are from different sources and are of different years. Also due to data availability, this thesis used proxies to calculation TOD score, such as POI types for land use types. Better datasets could improve the credibility and the comparability of models.

Finally, this thesis did not discuss the solutions to TOD implementation challenges in Beijing, which could be a future research direction.

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101 Appendix

A. Stations Analyzed in Beijing

Table 0-1 TOD Score and Daily Station Boarding of Beijing Index Station Ridership Density Diversity Design Connectivity TOD 1 Dongzhimen 88493 13.04 5.59 6.12 4.70 29.45 2 Liufang 15581 16.70 5.43 4.68 3.29 30.10 3 Guangxumen 13336 11.33 5.07 5.08 8.21 29.69 4 Shaoyaoju 26599 7.22 4.43 4.45 6.48 22.58 5 Wangjing West 18458 3.07 3.20 4.66 4.53 15.46 6 Beiyuan 14955 0.98 2.88 5.72 2.65 12.23 7 Lishuiqiao 31494 2.25 4.46 7.46 4.81 18.97 8 Huoying 30785 1.98 3.10 6.62 5.07 16.78 9 Huilongguan 34783 1.86 2.96 7.70 5.64 18.15 10 Longze 33749 2.03 4.41 6.22 4.85 17.51 11 Xierqi 62961 9.45 2.63 6.70 3.71 22.49 12 Shangdi 32084 9.80 3.42 4.22 5.53 22.98 13 38272 16.64 4.74 5.08 4.94 31.39 14 Zhichunlu 30399 16.87 4.23 5.76 4.89 31.75 15 Dazhongsi 15114 12.62 5.89 3.46 5.56 27.52 16 Xizhimen 71062 16.59 5.03 5.16 8.03 34.82 17 Shangezhuang 1835 0.73 2.08 5.54 3.04 11.39 18 laiguangying 4968 1.98 3.18 6.32 6.42 17.91 19 Donghuqu 8969 10.56 4.34 5.90 6.95 27.75 20 Wangjing 20346 7.07 5.28 5.88 7.92 26.15 21 Futong 8599 10.99 5.27 5.84 10.51 32.62 22 Wangjing South 9644 11.96 7.15 5.01 7.81 31.93 23 Jiangtai 21787 8.74 5.19 5.37 4.65 23.94 24 Dongfengbeiqiao 3612 4.47 2.70 6.07 3.93 17.18 25 Zaoying 3298 7.13 4.01 6.06 5.90 23.11

102 26 Jintailu 24612 11.90 5.16 4.34 4.48 25.88 27 Dawanglu 58568 29.17 8.81 4.69 13.81 56.47 28 Jiulongshan 8420 11.37 5.24 5.62 4.30 26.52 29 Beigongda West NA 9.37 3.37 4.84 4.26 21.83 Gate 30 Shilihe 37237 11.26 7.24 5.15 4.07 27.71 31 Fangzhuang NA 11.93 5.06 6.58 7.33 30.89 32 Puhuangyu 29240 11.13 4.70 5.29 6.66 27.79 33 Jingtai NA 9.88 5.95 4.52 6.19 26.53 34 Yongdingmenwai NA 10.60 4.01 4.98 5.54 25.13 35 Beijing South 81702 6.42 4.45 5.97 4.93 21.76 Station 36 Xiju 11426 10.53 2.00 5.56 6.58 24.67 37 Qilizhuang 15198 14.16 5.85 4.01 9.62 33.64 38 Dajing 5169 10.77 4.80 3.93 2.40 21.89 39 Guozhuangzi 5554 4.93 3.32 6.44 3.21 17.90 40 Dawayao 5254 3.31 2.78 5.44 4.58 16.10 41 Yuanboyuan 2324 0.21 1.15 8.08 1.74 11.19 42 Zhangguozhuang 6744 0.40 1.69 5.64 1.21 8.95 43 fengbo 11173 1.19 2.75 7.01 4.29 15.25 44 Shunyi 14662 3.91 8.46 7.30 10.10 29.77 45 Shimen 7555 1.97 5.72 6.92 4.13 18.75 46 Nanfaxing 4681 0.67 2.83 5.94 1.86 11.30 47 Houshayu 10531 0.46 1.82 6.22 4.01 12.51 48 Hualikan 6079 0.79 2.22 6.86 2.48 12.35 49 Guozhan 7314 0.66 3.04 6.44 2.65 12.78 50 Sunhe 4046 0.13 1.74 6.95 2.61 11.43 51 Maquanying 10096 0.64 6.05 7.58 1.40 15.68 52 Cuigezhuang 6590 0.71 2.08 5.89 1.27 9.95 53 Guangzhuang 3136 7.57 3.66 5.37 3.29 19.88 54 Dadunludong 22990 6.97 5.70 5.44 11.66 29.76 55 Anlilu 8536 15.38 5.44 5.35 8.64 34.81

103 56 Olympic Park 19008 8.17 2.30 6.09 4.23 20.79 57 Beishatan 7127 7.49 4.44 5.29 5.25 22.47 58 Liudaokou 9746 10.06 3.52 4.03 4.28 21.88 59 Qsinghua East Road 5937 11.26 3.22 6.42 2.57 23.46 West 60 Sihui East 26293 8.44 3.62 5.66 4.64 22.36 61 Sihui 35862 18.14 3.64 5.80 9.03 36.62 62 Guomao 71757 35.15 6.33 5.61 11.56 58.65 63 Yonganli 32803 26.60 7.66 5.86 6.53 46.66 64 27742 21.74 6.05 5.37 12.08 45.24 65 Dongdan 34745 15.98 7.16 3.76 11.77 38.68 66 41834 16.34 8.91 4.56 8.96 38.76 67 Tian'anmen East 31173 8.63 3.78 4.65 6.65 23.71 68 Tian'anmen West 18500 10.16 2.64 5.45 5.41 23.66 69 61669 12.74 12.05 4.73 9.47 39.00 70 31599 14.50 5.27 5.08 10.89 35.75 71 Nanlishilu 20493 14.23 5.88 5.71 9.82 35.65 72 Muxidi 16280 9.39 4.43 5.29 5.63 24.74 73 Junshibowuguan 19559 7.71 4.58 4.61 10.08 26.97 74 15991 9.11 7.74 6.48 11.67 35.00 75 Wanshoulu 19431 11.22 8.81 4.66 5.52 30.21 76 Wukesong 34273 13.15 3.82 5.87 5.38 28.22 77 Yuquanlu 27267 8.02 3.77 4.40 7.45 23.64 78 Babaoshan 24671 5.59 3.83 6.87 5.09 21.37 79 Bajiao Amusement 16891 11.09 3.31 3.81 3.35 21.56 Park 80 Gucheng 20317 6.05 6.65 6.13 2.82 21.65 81 Pingguoyuan 38440 5.08 4.09 6.64 7.71 23.51 82 Jishuitan 47205 11.72 5.78 4.37 4.91 26.79 83 Guloudajie 21558 9.78 4.61 5.26 5.93 25.58 84 18621 11.70 4.72 5.49 5.09 27.01

104 85 Yonghegong Lama 24863 14.03 3.74 4.81 7.30 29.88 Temple 86 Dongsishitiao 36024 26.45 5.82 5.54 4.05 41.87 87 Chaoyangmen 59134 21.83 5.45 6.43 3.53 37.25 88 Beijing railway 85377 13.37 5.09 5.69 13.46 37.60 station 89 46251 14.15 6.62 4.52 11.59 36.88 90 Qianmen 30215 7.17 6.00 5.58 8.19 26.95 91 Hepingmen 12958 10.00 5.58 4.18 7.99 27.75 92 26176 11.24 5.28 4.41 10.98 31.93 93 Changchunjie 30936 11.06 4.57 4.82 13.06 33.52 94 40335 15.43 7.19 4.12 9.03 35.76 95 Chegongzhuang 24950 19.15 5.54 4.87 8.22 37.78 96 Ciqu 4555 0.23 4.80 6.04 2.31 13.38 97 Ciqu South 348 0.18 1.38 5.79 0.76 8.11 98 Jinghailu 9529 0.36 0.22 6.92 1.52 9.00 99 Tongji South Road 15133 7.78 1.64 5.84 2.76 18.02 100 Rongchang East 10574 8.61 1.21 5.79 4.30 19.90 Road 101 Rongjing East Road 7438 6.14 1.67 5.78 3.22 16.80 102 Wanyuanjie 7113 9.98 2.59 4.13 3.95 20.65 103 Yizhuang Culture 4454 6.92 2.74 6.80 4.06 20.52 Park Station 104 Yizhuang Bridge 7939 7.73 2.42 5.27 3.95 19.36 Station 105 Jiugong 12261 4.86 3.42 2.76 4.64 15.68 106 Xiaohongmen 5337 7.61 2.29 4.63 3.04 17.58 107 Xiaocun 5261 6.48 2.93 4.17 3.61 17.19 108 Songjiazhuang 44027 12.06 5.11 5.38 4.83 27.38 109 Gaobeidian 8221 3.11 3.61 4.50 6.21 17.43 110 Communication 17526 7.04 3.88 7.30 2.70 20.92 University of China

105 111 Shuangqiao 19944 2.60 3.49 6.66 3.46 16.20 112 Guangzhuang 15039 2.22 3.29 6.64 4.03 16.18 113 4206 1.75 2.48 5.91 3.19 13.32 114 Tongzhoubeiyuan 15823 2.48 5.45 5.45 6.54 19.91 115 Guoyuan 12435 2.03 4.86 7.42 4.31 18.62 116 Jiukeshu 9793 2.88 5.29 7.76 6.29 22.22 117 Liyuan 16884 2.05 5.81 5.89 5.95 19.70 118 Linheli 7453 1.56 2.89 6.76 4.42 15.63 119 Tuqiao 20415 1.33 2.47 7.17 3.95 14.92 120 Tiangongyuan 17014 0.54 2.15 6.61 2.65 11.95 121 Biomedical Base 7679 0.24 1.93 7.33 2.26 11.75 122 Yihezhuang 3606 0.55 0.64 5.83 1.35 8.36 123 Huangcun Railway 13756 2.09 3.32 7.54 5.59 18.55 Station 124 Huangcun West 15492 2.58 5.61 6.81 7.17 22.17 Street 125 Qingyuanlu 13413 2.15 4.62 6.97 7.12 20.86 126 Zaoyuan 15275 1.83 4.43 6.29 6.50 19.05 127 Gaomidian South 13724 1.67 3.43 6.96 5.42 17.48 128 Gaomidian North 12036 1.43 2.77 7.62 4.63 16.44 129 Xihongmen 27484 6.86 3.53 6.50 2.89 19.78 130 Xingong 23748 5.19 2.35 5.92 1.91 15.37 131 West Bridge 22696 17.36 4.45 4.83 4.47 31.12 132 Jiaomen West 23571 10.22 5.02 3.87 6.15 25.25 133 Majiapu 16364 13.84 4.00 4.26 7.18 29.28 134 Taoranting 23249 12.86 4.68 4.54 4.46 26.55 135 14905 11.65 4.41 3.64 6.31 26.01 136 Linjinghutong NA 10.70 12.15 4.27 4.12 31.24 137 12894 9.67 6.55 2.93 5.43 24.58 138 Pinganli 18365 9.77 6.23 3.00 5.18 24.18 139 Xinjiekou 12678 12.85 7.41 3.04 5.92 29.22 140 26066 9.92 3.79 3.97 9.30 26.98

106 141 National Librabry 19808 13.90 3.29 3.46 5.64 26.28 142 Weigongcun 25553 14.73 4.98 5.68 4.46 29.84 143 Renming University 26951 16.95 5.00 4.97 8.79 35.72 144 Haidianhuangzhuang 43129 20.67 6.43 5.41 7.91 40.41 145 Zhongguancun 34945 20.01 7.53 4.57 10.96 43.08 146 Peking University 15717 13.46 3.25 3.80 7.29 27.81 East Gate 147 Yuanmingyuan 11637 10.82 2.50 6.32 4.62 24.27 148 Xiyuan 15949 7.54 3.72 5.42 6.49 23.16 149 Beigongmen 22946 4.93 2.78 3.78 3.50 14.98 150 Anheqiao North 18626 4.32 2.76 5.70 2.99 15.76 151 Tiantongyuan North 49895 1.76 3.06 6.75 3.56 15.14 152 Tiantongyuan 44812 1.82 4.14 7.50 3.59 17.05 153 Tiantongyuan South 16517 1.97 4.71 6.32 3.31 16.30 154 Lishuiqiao South 17926 7.73 3.99 6.52 4.36 22.60 155 beiyuanlu North 23444 10.08 4.92 5.83 3.62 24.46 156 Datunlu East 22990 9.14 5.72 4.16 11.38 30.40 157 Huixinxijie North 27936 14.49 4.25 5.38 7.00 31.13 158 Huixinxijie South 20043 13.36 3.35 5.50 7.16 29.37 159 Hepingxiqiao 21834 20.20 4.28 3.01 8.57 36.06 160 Hepinglibeijie 16355 12.43 4.35 4.38 5.85 27.02 161 Beixinqiao 14769 13.14 5.85 2.85 4.86 26.69 162 Zhangzizhonglu 10730 12.50 5.76 3.55 4.68 26.49 163 Dongsi 21326 13.86 6.91 3.36 5.26 29.38 164 Dengshikou 22603 15.80 9.38 3.50 4.02 32.70 165 Ciqikou 15632 14.06 5.69 5.07 5.38 30.20 166 Tiantan East Gate 18052 8.32 4.16 3.84 4.02 20.34 167 Liujiayao 32474 13.69 6.01 3.95 8.40 32.05 168 Lucheng 5102 0.55 2.14 5.74 1.46 9.89 169 Dongxiayuan 3100 0.65 1.16 8.08 1.35 11.24 170 Haojiafu 2206 0.33 1.75 6.60 1.46 10.13 171 Beiyunhe West 12496 1.93 3.64 5.78 2.62 13.97

107 172 Tongzhoubeiguan 4801 1.66 2.73 5.82 3.90 14.10 173 Wuzixueyuanlu 19013 1.40 3.04 7.32 1.59 13.35 174 Caofang 29469 0.97 2.19 6.82 2.48 12.47 175 Changying 22601 0.94 2.11 7.24 2.41 12.71 176 huangqu 19157 1.75 3.18 7.42 1.86 14.21 177 Dalianpo 17239 2.40 3.09 6.87 1.87 14.24 178 Qingnianlu 34770 9.05 5.40 4.35 3.35 22.14 179 Shilipu 24064 9.78 3.86 4.94 5.03 23.61 180 Hujialou 31216 19.09 6.11 3.86 9.00 38.06 181 Dongdaqiao 33589 16.52 8.47 3.68 4.17 32.84 182 Xhaoyangmen 59134 21.39 5.60 4.89 3.08 34.97 183 19165 9.37 5.83 3.24 2.91 21.35 184 Beihai North 12472 8.42 3.22 4.17 4.58 20.39 185 Chegongzhuang 11901 14.02 5.02 5.27 4.68 28.99 West 186 Baishiqiao South 22416 15.40 4.68 5.15 4.63 29.86 187 Huyuanqiao 17032 11.66 4.93 4.27 8.44 29.30 188 Cishousi 9476 7.22 2.93 5.77 4.07 19.99 189 Haidianwuluju 31108 7.26 3.81 5.46 6.67 23.20 190 Coking Plant 4653 5.77 2.60 4.75 2.25 15.36 191 Shuanghe 6302 7.18 2.70 3.67 2.31 15.86 192 Happy Valley Scenic 9763 5.15 4.70 5.64 2.45 17.94 Area Station 193 Nanlouzizhuang 5644 6.97 3.52 4.57 3.89 18.94 194 Huagong 508 7.14 2.81 4.36 2.66 16.97 195 Baiziwan 10174 8.24 4.13 5.59 3.38 21.33 196 Dajiaoting 7504 9.58 4.52 5.94 4.48 24.52 197 Guangqumenwai 9834 11.72 4.76 6.49 2.84 25.81 198 Guangqumennei 13339 16.37 6.90 5.77 2.10 31.15 199 Qiaowan 4449 8.78 4.08 3.85 6.28 22.99 200 Zhushikou 8144 8.65 6.62 3.87 8.03 27.16 201 Hufangqiao 6735 9.55 7.22 4.92 7.04 28.73

108 202 Guanganmennei 16986 15.62 4.91 5.20 10.00 35.74 203 daguangying 17324 13.69 4.97 4.80 7.74 31.19 204 Wanzi 13583 11.75 5.77 5.83 11.01 34.36 205 Beijing West Station 98081 7.93 4.14 4.22 19.08 35.37 206 Zhuxinzhuang 16082 1.22 1.69 5.74 1.57 10.23 207 Yuzhilu 6656 1.63 2.94 7.14 4.42 16.14 208 Pingxifu 6163 1.85 2.86 6.77 4.69 16.17 209 Huilongguang East 23082 1.90 3.63 6.99 5.37 17.88 Street 210 yYuxin 20593 3.35 4.42 7.24 3.90 18.91 211 Xixiaokou 8532 2.02 2.67 4.66 4.33 13.68 212 Yongtaizhuang 18303 7.12 3.89 4.73 6.26 22.00 213 Lincuiqiao 7667 2.70 2.36 5.40 2.25 12.71 214 Forest Part South 2409 1.14 1.75 6.35 2.14 11.37 Gate 215 Olympic Sport 11125 3.78 2.77 6.09 6.77 19.41 Center Station 216 Beitucheng 10986 6.13 2.99 4.66 7.81 21.59 217 Anhuaqiao 14284 12.57 4.92 4.74 9.60 31.84 218 Andeli North Road NA 11.86 5.31 4.73 2.34 24.25 219 7252 8.01 6.92 4.50 3.46 22.89 220 Baiduizi 13812 8.03 4.94 5.30 2.93 21.19 221 Liuliqiao East 28417 10.44 5.18 4.09 16.93 36.64 222 Liuliqiao 18732 9.25 4.24 6.35 4.51 24.35 223 Fengtaidongdajie 8104 10.59 5.36 5.79 8.24 29.97 224 Fengtainanlu NA 6.51 4.53 5.26 6.66 22.97 225 Keyilu 9904 8.25 3.63 5.69 4.96 22.53 226 Fengtai Science and 16345 2.23 1.77 6.18 2.93 13.11 Technology Park 227 Guogongzhuang 5267 9.46 1.35 5.24 2.87 18.92 228 Bagou 8627 9.95 4.78 5.40 3.50 23.62 229 Huoqiying 6127 7.30 4.47 5.71 2.29 19.76

109 230 Changchunqiao 17164 8.85 4.78 5.77 3.01 22.40 231 Chedaogou 14160 10.15 3.56 5.56 2.82 22.10 232 Xidiaoyutai 18733 11.67 3.49 5.02 3.71 23.88 233 Liahuangqiao 7585 5.89 4.02 5.73 10.03 25.67 234 Niwa 9191 12.42 3.40 4.67 3.66 24.14 235 Fengtai Station 8431 11.10 4.38 5.89 2.04 23.42 236 Capital University of 15938 11.67 4.10 4.84 2.77 23.37 Economics and Business 237 Jijiamiao 5661 11.03 3.23 3.83 3.12 21.20 238 Caoqiao 10897 8.31 3.32 6.50 3.63 21.76 239 Jiaomen East 19415 9.22 5.25 4.81 4.61 23.89 240 Dahongmen 25479 8.93 6.80 4.68 3.93 24.34 241 Shiliuzhuang 13803 11.25 6.26 6.18 3.53 27.22 242 Chengshousi 16908 6.89 3.64 5.48 3.16 19.16 243 Fenzhongsi 6254 8.73 5.31 3.61 3.39 21.04 244 Panjiayuan 29804 13.48 6.75 4.90 5.53 30.66 245 Jingsong 37422 14.61 7.23 5.71 7.62 35.17 246 Shuangjing 40257 14.24 7.76 5.29 5.63 32.93 247 Jintaixizhao 27848 23.19 5.71 5.99 7.78 42.67 248 Tuanjiehu 37661 15.73 9.00 4.62 6.65 36.00 249 National Agriculture 6832 7.92 5.05 5.68 8.36 27.01 Exhibition Center 250 Liangmaqiao 35697 20.37 6.06 4.69 7.73 38.85 251 Sanyuanqiao 60127 18.49 5.94 6.10 10.86 41.39 252 Taiyanggong 25456 7.42 3.50 6.76 6.71 24.38 253 Huixinxijienankou 20043 13.36 3.35 5.69 7.16 29.57 254 Anzhenmen 15733 13.14 2.92 5.40 6.83 28.29 255 Jiandemen 26041 10.46 3.93 5.10 9.70 29.19 256 Mudanyuan 27336 13.08 5.75 5.41 4.52 28.75 257 Xitucheng 29580 12.21 4.13 3.66 5.53 25.53 258 Zhichunli 13395 20.71 4.28 6.13 6.11 37.23

110 259 Suzhoujie 33023 24.53 4.56 5.29 6.33 40.70 260 Chanhgping West NA 0.32 1.62 6.33 1.35 9.61 Montain 261 Shisanlingjingqu NA 0.46 2.07 7.06 1.29 10.88 262 Changping NA 4.90 11.54 5.63 7.04 29.11 263 Changpingdongguan NA 2.01 5.89 5.44 5.88 19.23 264 Beishaowa NA 0.38 1.38 6.72 1.97 10.45 265 Nanshao 10001 0.36 2.51 5.78 2.86 11.50 266 Shahe High 14331 0.35 1.94 6.00 2.37 10.66 Education Park 267 Shahe 26459 0.93 3.38 5.50 1.63 11.44 268 Gonghuacheng 1558 0.30 0.00 6.13 1.35 7.77 269 Life Science Park 21824 1.70 3.02 6.87 2.63 14.21 270 Dabaotai 6083 3.47 2.48 5.98 2.48 14.41 271 Daotian 4284 0.21 1.12 6.35 1.40 9.07 272 Changyang 13960 0.26 1.61 7.11 1.48 10.46 273 Libafang 5668 0.13 1.27 7.03 1.74 10.17 274 Guangyangcheng 3618 0.58 2.02 7.69 1.80 12.10 275 Liangxiang 4810 0.84 1.01 7.39 1.40 10.65 University Town North 276 Liangxiang 2452 0.45 1.02 5.98 2.03 9.47 University Town 277 Liangxiang 6606 0.60 2.95 7.53 1.76 12.84 University Town South 278 Liangxiangnanguan 4726 1.55 5.53 7.04 4.35 18.48 279 Suzhuang 9762 2.20 4.49 7.52 5.22 19.43 280 Beijing International 6706 0.24 2.01 6.05 0.78 9.08 Airport Terminal 3 281 Beijing International 7350 0.43 2.35 6.92 1.48 11.18 Airport Terminal 2

111 B. Stations Analyzed in Singapore

Table 0-2 TOD Score and Daily Station Boarding of Singapore

Index Station Ridership Density Diversity Design Connectivity TOD 1 STN Admiralty 27966 14.90 1.93 4.86 7.34 29.04 2 STN 19986 23.22 1.88 3.13 4.83 33.07 3 STN Ang Mo 29136 24.82 3.54 4.93 5.93 39.21 Kio 4 STN Bartley 3526 8.18 1.94 3.59 3.25 16.96 5 STN 16463 16.31 3.56 3.20 6.78 29.85 6 STN Bishan 26029 11.76 2.28 4.51 5.41 23.96 7 STN 13420 10.86 2.12 3.28 5.05 21.31 8 STN 29824 13.07 2.77 4.58 8.60 29.02 9 STN Braddell 15360 19.54 0.83 4.29 5.61 30.27 10 STN 5219 12.25 15.53 4.14 12.90 44.82 11 STN 7892 21.49 0.13 5.86 9.68 37.15 12 STN Bugis 41850 18.56 13.72 3.46 12.24 47.99 13 STN 19107 13.79 2.82 3.31 6.55 26.46 14 STN Bukit 16313 15.81 0.13 4.13 4.12 24.19 Gombak 15 STN 13959 23.56 3.44 6.96 6.76 40.73 16 STN Changi 9774 6.55 2.71 1.94 0.87 12.07 Airport 17 STN Chinatown 26043 13.23 13.54 2.93 10.58 40.27 18 STN Chinese 8030 8.47 0.92 4.65 4.87 18.92 Garden 19 STN Choa Chu 25197 31.77 3.00 4.46 9.22 48.46 Kang 20 STN City Hall 47871 11.62 11.86 2.82 9.95 36.25 21 STN Clarke 12009 10.46 14.81 3.38 10.19 38.84 Quay 22 STN Clementi 25987 11.52 3.81 5.73 6.49 27.54

112 23 STN 13088 12.96 2.44 3.88 5.42 24.69 Commonwealth 24 STN Dakota 3704 9.80 1.70 3.71 3.48 18.69 25 STN Dhoby 31897 20.38 10.29 2.32 10.06 43.04 Ghaut 26 STN Dover 12132 12.36 0.00 5.62 3.42 21.39 27 STN Esplanade 4876 22.55 11.81 2.34 11.07 47.76 28 STN Eunos 11870 10.89 2.38 3.24 4.81 21.32 29 STN Expo 6562 15.28 1.71 3.36 5.72 26.06 30 STN Farrer Park 18921 29.37 5.51 2.40 8.11 45.39 31 STN Harbour 36147 14.16 3.01 4.75 5.26 27.19 Front 32 STN 15503 12.88 3.40 3.58 8.20 28.05 33 STN 8459 11.47 0.94 3.53 4.27 20.21 34 STN Jurong East 31052 9.22 1.94 5.70 7.89 24.74 35 STN Kallang 8467 5.91 1.53 4.38 4.71 16.53 36 STN Kembangan 9586 7.72 1.23 4.58 3.59 17.12 37 STN Khatib 18666 15.19 1.57 5.93 4.80 27.48 38 STN Kovan 11095 13.81 1.96 5.57 3.62 24.96 39 STN Kranji 8282 12.05 1.65 6.89 2.76 23.34 40 STN Lakeside 17733 11.54 0.80 4.29 6.23 22.86 41 STN Lavender 17762 11.99 4.02 4.72 6.29 27.02 42 STN Little India 13280 11.32 7.79 2.85 7.70 29.66 43 STN Lorong 5906 13.02 1.35 3.07 3.42 20.85 Chuan 44 STN MacPherson 8974 10.27 0.65 4.96 5.71 21.59 45 STN Marina Bay 4263 15.42 2.79 4.39 5.49 28.09 46 STN 14857 23.27 1.87 5.03 4.74 34.91 47 STN Marymount 5977 20.84 2.44 5.86 4.10 33.25 48 STN 4972 23.47 1.74 5.17 4.65 35.03 Mountbatten 49 STN Newton 10294 11.92 2.85 4.53 4.41 23.71

113 50 STN Nicoll 3101 18.18 6.44 3.75 5.90 34.27 Highway 51 STN Novena 35209 10.85 14.10 3.63 3.81 32.39 52 STN Orchard 50556 7.40 18.59 2.80 7.20 35.99 53 STN Outram 27206 13.76 11.98 2.16 8.55 36.45 Park 54 STN 13517 9.49 2.55 5.32 4.89 22.25 55 STN 17628 13.20 3.93 3.38 6.11 26.62 56 STN Pioneer 20974 20.62 2.21 3.67 6.81 33.32 57 STN Potong 10547 10.31 1.41 4.10 4.64 20.46 Pasir 58 STN Promenade 10702 11.07 4.82 3.74 4.11 23.74 59 STN Punggol 4355 17.19 1.72 4.09 6.70 29.70 60 STN Queenstown 12516 14.76 2.08 3.78 5.26 25.88 61 STN Raffles 73897 10.39 12.05 5.62 11.23 39.29 Place 62 STN Redhill 14373 12.75 2.52 3.94 6.53 25.75 63 STN 29432 13.63 2.49 6.23 8.58 30.93 64 STN Sengkang 20971 13.10 1.82 3.83 11.16 29.90 65 STN 26060 13.06 3.01 2.83 8.19 27.09 66 STN 19005 15.42 2.67 3.16 4.62 25.88 67 STN Somerset 27221 16.84 14.54 2.50 7.21 41.09 68 STN Stadium 2655 24.42 1.96 7.36 3.19 36.93 69 STN Tai Seng 6899 12.90 1.66 7.32 3.93 25.82 70 STN 28251 15.16 3.97 5.48 7.40 32.02 71 STN Tanah 12075 6.29 10.59 3.75 4.66 25.29 Merah 72 STN Tanjong 49235 9.66 8.80 2.67 12.59 33.72 Pagar 73 STN Tiong 22309 12.08 3.02 4.88 6.91 26.89 Bahru 74 STN 26283 13.86 4.06 4.64 6.38 28.95

114 75 STN Woodlands 23069 16.27 2.48 6.30 7.07 32.12 76 STN Woodleigh NA 7.85 1.61 4.28 4.25 17.98 77 STN 27715 21.45 1.80 3.23 6.99 33.47 78 STN Yio Chu 19003 12.50 1.29 3.83 5.38 23.01 Kang 79 STN 34357 16.98 2.44 6.64 7.44 33.50 80 STN Farrer Road NA 12.94 1.99 4.87 1.78 21.57 81 STN Caldecott NA 12.80 1.66 5.94 3.70 24.11 82 STN Bukit NA 5.15 1.30 6.09 0.17 12.71 Brown 83 STN Botanic NA 8.53 0.39 5.38 2.73 17.04 Gardens 84 STN Holland NA 11.36 2.85 3.80 3.68 21.69 Village 85 STN One North NA 12.79 2.69 6.07 7.16 28.70 86 STN NA 7.70 2.50 3.63 5.65 19.48 87 STN Haw Par NA 20.13 1.17 4.34 3.10 28.75 Villa 88 STN Pasir NA 8.95 0.92 4.45 1.96 16.29 Panjang 89 STN Labrador NA 15.26 1.74 4.26 2.92 24.18 Park 90 STN Telok NA 9.19 1.53 5.36 2.47 18.55 Blangah

115 C. Spatial Autocorrelation Report for Beijing: log (Ridership)

Global Moran's I Summary

Type: Queen Contiguity (order =1) Number of Neighbors: see Error! Reference source not found. Row Standardized: Yes

Table 0-3 Global Moran's I Summary for Beijing log (Ridership) Moran's Index 0.2788 Expected Index -0.0038 Variance 0.0351 Z-score 8.0233 P-value 0.0010

Moran's 1 0.278819

(00

oc0* 8.

8*

-6.00 -4.00 -2.00 0.00 2.00 4.00 600 bgRidersh

Figure 0-1 Moran I Test Result for Beijing log (Ridership)

116 D. Spatial Autocorrelation Report for Singapore log (Ridership)

Global Moran's I Summary

Type: Queen Contiguity (order =1) Number of Neighbors: see Error! Reference source not found. Row Standardized: Yes

Table 0-4 Global Moran's I Summary for Singapore log(Ridership) Moran's Index 0.1272 Expected Index -0.0130 Variance 0.0684 Z-score 2.0858 P-value 0.0260

Moran's 1:0.127198

00 0 00 0

* 00 0

-290 -1.80 -070 0.40 150 2.60 IogRidersh

Figure 0-2 Moran I Test Result for Singapore log (Ridership)

117 E. Spatial Autocorrelation Report for Beijing: OLS Model2 Residual

Global Moran's I Summary

Type: Queen Contiguity (order=1)

Row Standardized: Yes

Table 0-5 Global Moran's I Summary for Beijing OLS Model Residual Moran's Index -0.1108 Expected Index -0.0130 Variance 0.0709 Z-score -1.3904 P-value 0.0730

Moran's 10.0766028

* 0

-6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 Resdual

Figure 0-3 Moran's Test Result for Beijing Simple Model 3 Residual

118 F. Spatial Autocorrelation Report for Singapore: OLS ModeI2 Residual

Global Moran's I Summary

Type: Queen Contiguity (order=1)

Row Standardized: Yes

Table 0-6 Global Moran's I Summary for Singapore OLS Model Residual Moran's Index -0.1108 Expected Index -0.0130 Variance 0.0709 Z-score -1.3904 P-value 0.0730

Moran's 1:-0 110831

00

0 00 g 0 0

C. 00~~ 0 00 0

00

-3.60 -220 -0.80 080 2.0 3.40 ResiduaI

Figure 0-4 Moran's Test Result for Singapore Simple Model 3 Residual

119 G. TOD Challenges in Practice

The concept of TOD was introduced to China in the early 2000s. The principles and design guidelines have been discussed since then, while TOD in practice remains difficult. To shed light on the question what challenges exist for TOD implementation in China, I conducted 30 semi- structured interviews during August of 2016 to identify TOD challenges in practice. Interviewees included urban planners, transit operators, real-estate developers, government officials and bankers from six cities in China (see Error! Reference source not found.); 25 were interviewed in p erson and five by phone.33

Distribution of Profession Spatial Distribution

E Planner 9 Beijing * Transit Provider 4 0 Shenz hen

U Developer *Tianjin Government Official Chongqing

* Financial Professional - 0 Kunming

SNGO * Jinan Professor Singapore

Figure 0-5 The Spatial and Profession Distribution of Interviewees While different cities in China face different challenges, they still share many barriers to TOD implementation. This Appendix summarizes major challenges of TOD implementation in Chinese cities, including the lack of coordination among different departments and agencies and planning code conflicts with TOD design principles. While many local governments in other Chinese cities are faced with financial pressure to subsidize TOD projects, it is not a serious issue in Beijing, according to the interview results. Cities such as Chongqing, Tianjin, Jinan and Kunming are trying to solve the financial problem by utilizing social and private capital via Public and

13 The interview process followed the ethical and legal guidelines for conducting studies involving human subjects and was approved by MIT Committee on the Use of Humans as Experimental Subjects (COUHES).

120 Private Partnership (PPP). Beijing has already introduced PPP 34 and "is playing as one of the leading roles in utilizing social capital", according to a transit investor.

1) Lack of Interagency Coordination

* Local government lacks motivation to facilitate TOD

TOD in China has gained wide acceptance. However, the implementation of TOD requires efforts from multiple stakeholders including the local government, real estate developers, urban planners and transit operators, which introduces high communication and coordination costs for

all sides. Among relevant roles, the government should take the lead. While government officials generally accept the concepts of TOD, according to an urban planner, "the short-term effects of

TOD are unobvious and mostly unquantifiable". "It cannot be reflected in the government annual

report," the planner said, "so facilitating TOD is not the primary task for the local government to

consider and usually make ways for other political purpose, such as revenue creation ". A local authority official said that "the best situation is that the mayor could lead the project so that all relevant departments will cooperate".

Beyond the outcome of increasing metro ridership, TOD is also believed to raise land value of areas proximate to transit nodes. However, China has not adopted a property tax and thus the increased land value does not immediately add to the revenue of local governments, another possible reason why local government is not strongly motivated in practice.

* Mismatches exist between the timeline of urban plans and transit plans.

TOD projects involves land use strategies, real estate developments and transit construction. Urban plans, especially urban master plans, are typically made in advance for one or two decades and, once approved, are inflexible to accommodate new changes. However, metro expansion in Beijing happens at a surprisingly rapid speed and therefore creates a mismatch between the timeline of urban land use development and transit construction. A

3 Beijing Subway Line 4, 14 and 16 is financed, constructed and operated through PPP.

121 transit provider said, "it is very typical that the metro line expands to the suburban area very soon, which is not considered in the previous urban plan, and the land use in the area surrounding the metro station doesn't change accordingly."

0 The government and developers are unwilling to sacrifice FAR

The detailed urban plans regulate the FAR of each parcel and the construction of metro stations will use some quota of FAR. "If a parcel of land is leased out before a metro station is planned, the developer is typically unwilling to cooperate with the metro company to construct a new station on that parcel," said a transit provider, "FAR is money."

FAR also means revenue for local government in the form of the land transfer fee. "Constructing more metro entrances on a parcel results in the sacrifice of FAR for residential or commercial building," said a real estate developer, " so the government is not motivated to build more metro exits".

2) Planning Code Conflicts

* The lack of relevant regulations

Some regulations, such as the regulation of underground space planning, are lacking Beijing. TOD includes integrating the surface space and underground space to create a more dynamic land use and urban environment, which involves leasing out different layers of land in the vertical dimension separately. Beijing, however, has not released relevant regulation on the underground space land lease. A planner said, "in such cases, planners and developers will rather not practice TOD principles to avoid potential political mistakes."

* The conflicts between TOD concepts and planning codes.

TOD design, according to Calthorpe (2013), has features including: 1) walkable streets and human scale blocks to enhance pedestrian flow; 2) buildings and land use that support pedestrian safety and convenience; 3) bike-friendly networks to reduce auto dependence; 4) transit- oriented streets and neighborhoods to enhance ridership; 5) mixed-use blocks, neighborhoods and districts to increase local destinations; 6) Integrated open space and public services at 122 walkable distances; 7) energy-efficient buildings and community systems to reduce carbon emissions. However, the above design principles are in conflict with some regulations of the national and local planning codes. For example, In the Codefor Transport Planning on Urban Road (GB50220-95), the overall road network density is required to be 7km/km2. According to this requirement, the average block size is about 400*400m, which is a typical super-block type of road network in conflict with TOD design guidelines.

In practice, according to an official, "it is more sensitive in Beijing than in any other cities in China to revise the planning codes or release new regulations. Every policy change could become a political sign for the whole country". Other cities such as Shenzhen and Shanghai could be more flexible in the planning codes. Beijing, however, "has to be strict in practice".

123 H. Interview Guidebook

Knowledge of TOD

a) Do you know about TOD? What do you think is the main principle of TOD? Can you name some examples of successful TOD projects? b) Do you agree that these main principles could be or should be adopted in the urban planning in China? If yes, at what level? Master plan, regulatory plan, or site plan? c) The principles of TOD involve high-density development, mixed land use and pedestrian friendly design in areas surrounding transit stations. What do you think has been done well in China? And what has been done poorly? d) What are the roles of transit, private cars and walking in urban transportation? e) What stakeholders or actors are involved in TOD projects?

Existing Regulation

f) What is the government attitude toward TOD in China? Are there any policies or regulations that promote TOD? g) What do you think of the recent policy that requires opening up the gated residential community? Is this policy beneficial for promoting TOD in China? h) Do the TOD planning principles meet the requirements of the national planning code and local planning regulations in China? Are there conflicts? i) Do you see other administrative challenges in implementing TOD? is there an institutional structure for better cooperation?

Market Conditions

j) What do you think of current real estate market? k) Is the cost of TOD project higher than other projects in general? What is the challenge of investing on TOD? I) How is TOD financed? What is the challenge for utilizing private capital and adopting public and private partnership (PPP)? m) For different land use (commercial vs. residential), different locations (urban centers vs. suburbs) and different development types (regeneration vs. new development), are there different financial strategies for TOD?

124 I. Interview List

Table 0-7 List of Interviews Profession No. City Gender Date 1 Beijing Male 7/22/2016 2 Beijing Female 7/22/2016 Urban/Transportation 3 Beijing Female 7/23/2016 Planner 4 Beijing Female 7/23/2016 5 Chongqing Male 8/25/2016 6 Jinan Male 8/17/2016 7 Beijing Male 7/24/2016 8 Beijing Female 7/28/2016 Transit Company 9 Kunming Male 8/3/2016 10 Jinan Male 8/17/2016 11 Beijing Male 7/24/2016 12 Kunming Male 8/3/2016 13 Kunming Female 8/4/2016 Developer 14 Kunming Female 8/4/2016 15 Shenzhen Female 7/16/2016 16 Shenzhen Male 7/17/2016 17 Beijing Female 8/29/2016 18 Chongqing Male 8/25/2016 Government Agency 19 Chongqing Male 8/25/2016 20 Chongqing Male 8/26/2016 21 kunming Male 8/4/2016 22 Kunming Male 8/5/2016 23 Tianjin Male 7/30/2016 24 Jinan Male 8/18/2016 25 Jinan Male 8/18/2016

125 26 Singapore Male NA Finance Organization 27 Beijing Male 8/11/2016 28 Beijing Male 8/11/2016 NGO 29 Beijing Female 8/12/2016 University 30 Beijing Male 8/13/2016 Total number 30

126