What influences Metro station ridership in China? Insights from Nanjing

Cities 35 (2013) 114–124

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What influences Metro station ridership in China? Insights from Nanjing Jinbao Zhao a,b,⇑, Wei Deng a, Yan Song b, Yueran Zhu a a b

School of Transportation, Southeast University, China Department of City and Regional Planning, University of North Carolina at Chapel Hill, United States

a r t i c l e

i n f o

Article history: Received 12 January 2013 Received in revised form 1 June 2013 Accepted 1 July 2013

Keywords: Urban rail transit Metro ridership Pedestrian catchment area Multiple regression analysis Nanjing

a b s t r a c t China is undertaking one of the most ambitious rail expansions in the world. This paper investigated the impacts of factors on ridership within Metro stations’ pedestrian catchment area (PCA) in Nanjing, China. Direct ridership model was developed to explain the ridership at 55 Metro stations using a Geographic Information System (GIS) and multiple regression analysis. Independent variables included factors measuring land use, external connectivity, intermodal connection, and station context. Six variables were found to be significantly associated with Metro station ridership at the 0.05 level: population, business/office floor area, CBD dummy variable, number of education buildings, entertainment venues and shop centers. Five variables were proved to be related to station ridership at the 0.01 significance level: employment, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. In particular, CBD dummy variable, the number of education buildings, entertainment venues and shop centers, and bicycle P&R spaces were found to be significantly connected to Metro station ridership in the present study. The results not only confirm some findings from previous studies but also show distinct differences regarding some variables specific to the Chinese context. Ó 2013 Elsevier Ltd. All rights reserved.

Introduction Urban rail transit (URT) is widely considered a preferred public transportation option for major metropolitans worldwide. Faced with the emerging social and environmental problems of urban sprawl, overpopulation, traffic congestion, air pollution, and climate change, an increasing number of Chinese cities have taken steps to develop or expand URT. Over the past decades, China’s ongoing urbanization (governing demand) and economic growth (governing affordability) has allowed more and more cities to meet the requirements to build URT systems (see Appendix A). Improved by URT lines, the overall public transportation network can promote a significant modal shift from cars to transit and create opportunities for transit-oriented development (TOD) (Dittmar & Ohland, 2004; Messenger & Ewing, 1996; Mu et al., 2011, 2012). In addition, a new EPC (engineering, procurement and construction)/Turnkey contract pattern has greatly facilitated the successful investment and implementation of URT projects in China (Mu et al., 2012). By the end of 2012, 16 cities in mainland China had built or were operating URT systems, with an additional 25 URT projects (expanding existing lines or building new ones) under planning or construction (NDRCC, 2012) (Fig. 1). In a word,

⇑ Corresponding author. Address: School of Transportation, Southeast University, Sipailou #2, Nanjing 210096, China. Tel.: +86 15850526918. E-mail address: [email protected] (J. Zhao). 0264-2751/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cities.2013.07.002

China today is experiencing some of the most rapid and ambitious URT growth in the world. In URT planning and development, ridership modeling and forecasting is crucial for the analysis of project viability over time. A host of models have been developed to forecast ridership during the past decades. The most widely used of which are the aggregate zone to zone (four-step) model and the disaggregate (individual, activity based) model (Bowman & Ben-Akiva, 2001; McNally, 2007). These models have some strengthens in offering a common ground to discuss policy, but their weaknesses are also obvious; they are costly, insensitive to land use, require excessive auxiliary data, and often have low accuracy. They can also slow down the response to the modeling results (Cardozo et al., 2012; Gutiérrez et al., 2011). In recent years, a growing but still limited amount of research has been conducted to investigate factors affecting URT station ridership based on sketch planning or direct forecasting models (Cardozo et al., 2012; Gutiérrez et al., 2011; Kuby, Barranda, & Upchurch, 2004; Sohn & Shim, 2010). As an alternative to the complex and costly four-step model and activity based model, direct models estimate ridership as a function of the characteristics of the stations and their service catchment areas (Gutiérrez et al., 2011). They have some distinct advantages over those traditional models, including simplicity of use, ease of interpretation and low cost. Based on multiple regression models, most policy makers can easily understand the process and make quick response to the modeling results (Cervero, 2006; Gutiérrez et al., 2011; Marshall &

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Fig. 1. The distributions of Chinese cities have built or proposed URT.

Grady, 2006). In addition, such investigating and analyzing processes can provide a firm foundation for decision makers to promote the prudent and sustainable development of URT systems and illustrate the critical elements of adopting TODs. Direct forecasting models have been adopted to explain factors affecting URT station ridership in some cities, including Madrid, Seoul, and some US cities (Gutiérrez et al., 2011; Kuby et al., 2004; Sohn & Shim, 2010). However, few studies have been done on the factors affecting URT station ridership in Chinese cities, which has become one of the world’s largest URT markets. In this paper we endeavor to quantify the impacts of different factors on URT ridership at the station-level in the city of Nanjing. The remainder of this article is structured as follows: The next section defines the size of pedestrian catchment areas (PCAs) of Metro stations and describes candidate variables that are hypothesized to affect Metro ridership at the station level. The third section presents study context, data source, and preprocessing methodology. Next, the model regression fit and results are discussed. That is followed by a presentation of some planning implications of, and limitations to, the analysis. The final section concludes the paper. Factors influencing ridership Direct forecasting models involve adjusting a multiple regression model where the dependent variable is station ridership and the independent variables are the characteristics of the stations and of their service catchment areas delimited with GIS tools (Gutiérrez et al., 2011). A critical assumption for using direct forecasting models to assess the factors driving ridership at the station level is how to define the catchment area of a station (Kuby et al., 2004). A station’s catchment area is usually determined by the ‘‘maximum’’ walk distance or the area within which a majority of users arrive by foot (Chalermpong & Wibowo, 2007; Jiang et al., 2012; Zhao & Deng, 2013). Therefore, the catchment area of a station is usually referred to as the

pedestrian catchment area (PCA). A number of different PCA sizes have been applied in previous literatures (Estupiñán & Rodríguez, 2008; Kuby et al., 2004; Sohn & Shim, 2010). For rail station-level ridership and property analysis, researchers usually focus on an area within an 800 m walking or radius distance (Hess & Almeida, 2007; Kuby et al., 2004). Sohn and Shim (2010) used a radial distance of 500 m as the radius of Metro stations’ PCAs in Seoul. In order to capture the variation in rail transit station ridership beyond the 500 m radius, we expanded the radial distance used by Sohn and Shim (2010) to 800 m as the radius of Metro stations’ PCAs. The annual average weekday station ridership (including passenger boardings and deboardings) at each Metro station in Nanjing was chosen as the dependent variable in order to reflect the ridership generation and attraction of Metro station. Based on previous studies (Cervero, 2006; Estupiñán & Rodríguez, 2008; Kuby et al., 2004; Sohn & Shim, 2010) and our insights, factors affecting ridership were classified into four types in the present study: (1) land use; (2) external connectivity; (3) intermodal connection; and (4) station context. The positive correlation of transit ridership with some land use variables, such as population and employment, has been fairly well established (Gutiérrez et al., 2011; Kuby et al., 2004; Rosenbloom & Clifton, 1996). A recent study carried out by Sohn and Shim (2010) found that the floor area of buildings within PCA is also related to station ridership. In this study the floor area was categorized into the following three types: ‘residential area’, ‘business/official area’, and ‘other area’. A CBD dummy variable was chosen by Kuby et al. (2004) as an independent variable, and it was also hypothesized to be related to Metro ridership in this paper. In addition to the above independent variables, we introduced six factors related to mixed land use, including the number of major educational buildings, hotels, restaurants, entertainment venues, shopping centers, and hospitals within the PCA as a means to explore the impacts of land use diversity on Metro station ridership.

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The characteristics of external connectivity are also relevant for explaining transit ridership. Sohn and Shim (2010) introduced six external connectivity variables: closeness centrality, betweenness centrality, and straightness centrality calculated respectively based on analysis of Metro and highway networks. However, none of these variables showed a significant association with Metro ridership in Seoul. Estupiñán and Rodríguez (2008) suggested that connectivity can be simply measured by road density and the number of intersections. We chose one of these two measures, road density, together with the distance from a station to the city center to reflect the impact of external connectivity on station ridership. Many studies have addressed variables related to intermodal connection. In particular, feeder bus lines and car P&R spaces were found to affect rail ridership significantly (Kuby et al., 2004; Sohn & Shim, 2010). In many Chinese cities, feeder bus lines are connected to Metro stations, but there are rarely large-scale parking facilities installed around Metro stations because of high land prices in the vicinity of Metro stations. Another distinct difference from previous case studies is that, in the Chinese context, people who ride bikes to Metro stations account for a significant portion of all metro commuters. For instance, around 2% of riders in Nanjing and 5% in Shanghai bike to Metro stations (NICTP, 2010; SH.EASTDAY, 2012). Correspondingly, bicycle P&R spaces are easily found near Metro stations (Fig. 2). Therefore, we assumed that the number

of bicycle P&R spaces is an influential factor in determining Metro station ridership in China. This hypothesis will be examined later in this paper. The station context category in previous studies included several dummy variables, such as ‘‘terminal or not’’ and ‘‘transfer or not’’ (Kuby et al., 2004; Sohn & Shim, 2010). Kuby et al. (2004) found that the terminal dummy variable is significantly and positively connected to light rail station boardings in US cities. Compared to the terminal dummy variable, even more evidence indicated that the transfer dummy variable is significantly associated with station ridership (Cardozo et al., 2012; Gutiérrez et al., 2011; Kuby et al., 2004; Sohn & Shim, 2010). Both terminal and transfer dummy variables were hypothesized to be associated with Metro station ridership in the present study. In addition to these two dummy variables, a dummy variable indicating that a station is elevated or not (underground) was also hypothesized to be related to Metro station ridership at the station level. Elevated stations and underground stations may deal with different pattern of passenger ridership, as elevated stations are usually located in suburban areas with upward sloping density gradients, while underground stations are usually surrounded by highly concentrated residential, commercial, and office land use areas within a few blocks. Generally, previous research on URT station ridership using direct forecasting models attempted to add not only new case

Fig. 2. Bicycle P&R spaces for an elevated Metro station (top) and an underground Metro station (below) in Nanjing.

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studies in new geographical regions but also some new variables to be considered. In addition to revision of some independent variables, such as using the number of major educational buildings instead of a university dummy variable, we also added some new independent variables that were not considered in previous studies, such as bicycle P&R spaces and an elevated dummy variable. All these factors’ impacts on Metro station ridership were investigated within the existing Metro system in the city of Nanjing, and the results can be used to examine the factors which influence URT ridership at the station level.

Study context and data

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In response to rapid urban expansion and travel demand growth, a rail transit plan was proposed in 1984. The construction of Nanjing Metro Line 1 began in 2000 and was completed in September 2005. The original Line 1 was 22 km long (14.3 km underground and 7.7 km elevated) with 11 underground stations and 5 elevated stations. A 25 km long extension to south was introduced in 2010, bringing the total length of Line 1 to 47 km with 31 stations. Line 2 began to operate in May 2010 and its full length is 38 km, with 18 underground stations and 8 elevated stations. Metro Lines 1 and 2 intersect at two stations (Fig. 3). Of the 55 stations, 5 are terminal stations, 2 are transfer stations, 20 are elevated stations, and the remaining stations are underground. Each transfer station with multiple platforms is treated as a single station.

Study context Data Located at the Yangtze River Delta Region, Nanjing is the capital of Jiangsu province in eastern China. Like many other Chinese cities, Nanjing is experiencing dramatic and rapid urbanization, economic growth, and motorization. Between 1980 and 2010, Nanjing’s gross domestic product (GDP) grew by more than 10% per year (in real terms), and its population increased from 4.7 million to 8.0 million (NSB, 2011). By the year 2020, the projected population will increase to 10.6 million, 86% of which will have urban registration (NPB, 2009).

All 55 Metro stations were taken into consideration during data collection. Annual average weekday station ridership was obtained from Nanjing Metro Corporation (NMC). The population and employment within each station’s PCA was calculated based on 2010 census data provided by the Nanjing Planning Bureau (NPB). However, the boundaries of transportation analysis zones (TAZ) obtained by NPB are not exactly consistent with the PCA s (Fig. 4). To estimate the population and employment within a

Fig. 3. Study area and station context.

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Fig. 4. The distributions of population and employment along Nanjing Metro Line 1.

Fig. 5. Building use (left) and shopping centers (right) within the PCA of Nanjing Maigaoqiao Metro station.

PCA, we assumed that population and employment are uniformly distributed within each urban block in a TAZ. Building floor area information comes from the 2010 NPB building use survey. The survey recorded building types, number of floors, and the area of each floor (Fig. 5, left). The number of major educational buildings, hotels, restaurants, entertainment venues, shopping centers, and hospitals within a station’s PCA were counted using a web mapping service application ‘‘Baidu Map’’. For example, by typing in ‘‘shopping centers’’ in the search box, major shopping centers would be labeled and displayed on Baidu Map (Fig. 5, right), and thus the number of shopping centers within the targeted PCA could be counted. Road length within the PCA and the distance from a station to the city center were measured using GIS. The number

of feeder bus lines connecting to a Metro station was available from NMC. Bicycle P&R spaces were not directly available creating the need for counting by our researchers at each site. Each station’s context was judged by visual inspection or from Nanjing Metro map. A detailed description of the data used is provided in Table 1. Modeling and discussion Modeling Multiple ordinary least squares (OLS) regressions were run to evaluate the impacts of the hypothesized independent variables on Metro ridership at the station level. OLS regressions can address

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both numerical and dummy variables, and are very flexible to use and easy to understand (Ceccato & Oberwittler, 2008; Jiang et al., 2012; Kuby et al., 2004; Sung & Oh, 2011). To isolate the influence of each variable category on Metro station ridership, the basic form of OLS model was adopted as follows:

Ri ¼ f ðLUi ; ECi ; ICi ; SCi Þ; ei

ð1Þ

where Ri is the ridership of Metro station i, LUi is a vector of land use variables of Metro station i, ECi is a vector of external connectivity of Metro station i, ICi is a vector of intermodal connection of Metro station i, SCi is a vector of station context variables of Metro station i, and ei is a random error term. The regression was run using SAS and estimated results were summarized in Table 2. The whole model was significant at the 0.001 level, with degrees of freedom at 35, R2 at 0.979, and the value of F 85.69. Seven independent variables were found to be insignificantly related to Metro station ridership. They are: residential building area, other-use building area, number of major labeled hotels, restaurants and hospitals, distance from a station to the city center, elevated dummy variable, and terminal dummy variable. The remaining eleven variables were estimated to be significantly associated with Metro station ridership. The relationship of annual average weekday station ridership and these eleven independent variables was shown in

RIDERSHIP ¼ 143; 71 þ 0:177  POPULATION þ 0:241  EMPLOYMENT þ 0:0055  BU OFF AREA þ 82; 186  CBD þ 1019  EDUCATION þ 26  ENTERTAINMENT þ 141  SHOP CENTER þ 1:571  ROAD LENGTH þ 987  FEEDER BUS þ 12  PARK AND RIDE þ 8447  TRANSFER

ð2Þ

Discussion The results of the OLS regression were compared with the outcomes of models built by Kuby et al. (2004) and Sohn and Shim (2010) and the main findings were summarized in Table 3. The R2 value (0.979) and the F value (85.69) in the present study were higher than corresponding statistics from Kuby et al. (2004) and of Sohn and Shim (2010). The initial model of Sohn and Shim (2010) presented an R2 value of 0.634 and an F value of 15.215 with seven significant variables at the 0.067 level. Kuby et al. (2004) proposed a final model with 12 significant variables at the 0.054 level, an R2 value of 0.634 and an F value of 56.69 after the correction for heteroscedasticity. Cervero and Murakami (2008) also proposed a series of regression models to predict Metro ridership in Hong Kong. Their results showed that the number of independent variables ranged from 7 to 10 and the value of R2 ranged from 0.601 to 0.746. The relatively high R2 value of the present study might result from the intrinsic property (e.g. the problem of multicollinearity) of multiple regression models, particularly the potential correlation between the CBD dummy variable and other variables such as employment and land-use diversity. This potential multicollinearity will be discussed in detail later. On one hand, using regression analysis prevented us from exploring other indirect or reciprocal relationships between variables. On the other hand, there were still many useful findings obtained from using the multiple OLS regression, and the rationale of estimated results would be discussed based on intuitive comparison between the outcomes of this study and of previous research as well as on insightful understanding of Nanjing’s realities. Land use density is a critical driver of transit ridership (Gutiérrez et al., 2011). The significance of density is that the more people that live and/or work in close proximity to transit, the

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greater the likelihood the service will be used (Murray et al., 1998). Correlation coefficients between Metro ridership and density variables (population, employment, and business/office building area) were found to be significant at the 0.05 level in explaining Metro station ridership. The b-Coefficients implied an increase of 17.7 passengers for every additional 100 population, 24.1 passengers for every additional 100 jobs, and 5.5 passengers for every additional 1000 m2 business/office building area within the PCA. There results were inspiring, particularly considering the fact that Nanjing as well as many other Chinese cities which have built or have been approved to build URT are still undergoing urbanization and rapid economic growth. Unlike many low-density cities in the U.S. where the primary concern in promoting transit is that ridership cannot be guaranteed, most cities in China can expect a steady increase in ridership in the near future (Kuby et al., 2004). A distinct difference from Kuby et al. (2004) was found in the CBD dummy variable. Sohn and Shim (2010) suggested that variables such as employment, land-use diversity, and centrality could fully address differences in location. This hypothesis generally proves to be accurate, but cannot be easily applied to Nanjing. On one hand, if we run the OLS model without a CBD dummy variable, the R2 value and the F value will decrease from 0.979 and 85.69 to 0.893 and 16.73, respectively, indicating that serious multicollinearity may exist between the CBD dummy variable and the other variables, particularly population, employment, and business/office building area. On the other hand, however, the CBD dummy variable was found to be one of the most significant variables and the one with the highest coefficient (+82,187 ridership) in Nanjing. This ‘‘contradiction’’ should be interpreted carefully. First, as Kuby et al. (2004) pointed out, some factors that may affect CBD ridership have been left out, including parking costs and congestion, venues for sports, arts, retail, and entertainment, and a much greater population density in the vicinity of the station. Second, unlike many polycentric US cities, Nanjing has only one CBD around the Xinjiekou Metro station, which exerts a strong central agglomeration effect. This effect can be demonstrated by the much higher level of ridership generated at Xinjiekou station (189,904), compared to significantly lower ridership (all below 100,000) at all other stations. Third, Nanjing’s CBD area experiences a large amount of contingent travelers and non-commuters every day, and the land prices within the CBD are too high to build adequate parking lots, therefore Metro and/or bus is likely to be the most reliable travel mode. Our analysis revealed that three of the land-use mix (diversity) variables: the number of major education buildings, entertainment venues, and shopping centers were significantly associated with Metro ridership at the 0.05 level. Diversity produces a more balanced demand for public transportation over time (reducing differences between peak and off-peak periods) and in space (in terms of direction of flow) (Cervero, 2004). The main trip purposes are work and school for the morning peak period, and returning home for the afternoon peak period (NICTP, 2010). The positive impact of educational buildings on Metro ridership also proved to be significant in Sohn and Shim (2010) where the variable was measured as a simple dummy variable. Personal/social purposes (including shopping, recreation, dining, etc.) account for more than 20% of residential travels in Nanjing (Fig. 6), which usually occur during off-peak periods. Shopping centers and entertainment venues can boost land use diversity and balance travel demand for public transport, increasing Metro ridership. Considering that Metro ridership’s share of Nanjing’s residential travel increased rapidly from 0.68% in 2006 to 5.38% in 2010, while bus ridership share decreased slightly during the same period (Table 4), it is likely that the Metro will continue to increase its ridership share in the future, partly subject to land use diversity levels within the PCA.

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Table 1 Description of variables and descriptive statistics. Variable

Description

Mean

Std. dev.

Min. value

Max. value

Source

Dependent RIDERSHIP

Annual average weekday ridership at stations

29,195

30,070

1760

189,904

NMC

Land-use within the station’s PCA (radius = 800 m) POPULATION Population EMPLOYMENT Employment RESI_AREA Total floor area of residential buildings (m2) BU_OF_AREA Total floor area of business/office buildings (m2) OTHER_AREA Total floor area of other-use buildings (m2) CBD CBD (0 for ordinary, 1 for CBD) EDUCATION Number of education institutions HOTEL Number of hotels RESTAURANT Number of restaurants ENTERTAINMENT Number of entertainment venues SHOP_CENTERS Number of shopping centers HOSPITALS Number of hospitals

25,419 13,874 574,022 435,265 227,020 N/A 5 25 29 7 6 5

34,362 23,793 449,872 838,430 197,124 N/A 3 17 15 11 10 4

945 139 83,072 10,188 20,656 0 1 1 2 0 0 0

166,779 94,647 1,914,096 3,843,007 1,218,173 1 13 61 53 56 54 14

NPB NPB Measured in GIS Measured in GIS Measured in GIS NPB Baidu Map Baidu Map Baidu Map Baidu Map Baidu Map Baidu Map

External connectivity ROAD_LENGTH DIST_TO_CENTERS

Road length within the station’s PCA (m) The distance from a station to the city center (m)

10,076 8176

4548 5348

3612 0

22,206 20,624

Measured in GIS Measured in GIS

Intermodal connection FEEDER_BUS PARK_AND_RIDE

Number of feeder bus lines stopping at a station Park-and-ride spaces for non-motor vehicles

16 328

11 250

1 107

50 1649

NMC Survey

Station context ELEVATED TRANSFER TERMINAL

Elevated station (0 for underground, 1 for elevated) Transfer station (0 for ordinary, 1 for transfer) Terminal station (0 for ordinary, 1 for terminal)

N/A N/A N/A

N/A N/A N/A

0 0 0

1 1 1

Observed Local Metro map Local Metro map

Table 2 Estimation results from OLS model.

** ***

Independent variable

b-Coefficient

t-Ratio

Significance

Std. error of estimate

(Constant)

14371.4782

2.27

0.0294**

6326.0001

Land-use POPULATION EMPLOYMENT RESI_AREA BU_OF_AREA OTHER_AREA CBD EDUCATION HOTEL RESTAURANT ENTERTAINMENT SHOP_CENTERS HOSPITALS

0.1769 0.2406 0.0033 0.0055 0.0044 82186.845 1019.9824 58.9687 169.2785 26.3026 141.0544 636.4298

2.15 3.32 1.01 2.43 0.87 11.94 2.03 0.48 1.50 2.10 2.39 1.51

0.0388** 0.0021*** 0.3207 0.0202** 0.3915 0.0001*** 0.0499** 0.6337 0.1433 0.0416** 0.0214** 0.1398

0.0533 0.1121 0.00325 0.0023 0.0051 11070.0814 502.2111 122.6723 113.0673 11.6259 54.8780 421.2705

External connectivity ROAD_LENGTH DIST_TO_CENTERS

1.5704 0.0564

3.97 0.15

0.0003*** 0.8797

0.3959 0.3701

Intermodal connection FEEDER_BUS PARK_AND_RIDE

987.7457 12.0843

9.36 4.01

0.0001*** 0.0003***

108.3318 5.5127

Station context ELEVATED TRANSFER TERMINAL

2589.2363 8447.5284 695.3069

0.92 2.80 0.81

0.3647 0.0082*** 0.423

2819.0755 2191.4326 324.7655

Model statistics DF F-value p-value R-square C.V.

35 85.68 0.0001 0.979 18.56

p < 0.05, significant at the 0.05 level. p < 0.01, significant at the 0.01 level.

Regarding external connectivity variables, the road length within a station’s PCA was found to be significantly related to Metro ridership at the 0.01 level. Estupiñán and Rodríguez (2008) found that road density can predict connectivity measures with a factor

loading >0.9. Longer road length within a station’s PCA increases the number of potential path choices for access to a station, improving the potential for higher accessibility to a rail station. However, connectivity/accessibility involves a number of

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J. Zhao et al. / Cities 35 (2013) 114–124 Table 3 Comparison of the multiple regression results from rail station ridership analysis. Independent variable

Authors (Transit system, City) Kuby et al. (2004) (LRT, US cities)

Land-use/Built environments within the PCA Population Employment Total floor area of residential buildings Total floor area of office/commercial buildings Total floor area of other-use buildings CBD dummy variable Number of education institutions Number of hotels Number of restaurants Number of entertainment venues Number of shopping centers Number of hospitals Airport International border University dummy variable Land-use diversity index Total length of automobile-dominated road The number of dead end points at which pedestrian-friendly streets are blocked The number of intersections crossed by only pedestrian-friendly streets The ratio of population to unit residential floor area External connectivity Road length within the station’s PCA The distance from a station to the city center Closeness calculated based on Metro network Betweenness calculated based on Metro network Straightness calculated based on Metro network Closeness calculated based on highway network Straightness calculated based on highway network The average number of transfers across the itinerary from a station to all other stations Intermodal connection Number of feeder bus lines Park-and-ride spaces Other rail lines Number of trunk bus lines Station context Elevated station Transfer station Terminal station Normalized accessibility Percentage of PMSA employment covered by system Socioeconomic Percent renters within the PCA Citywide variable Heating and cooling degree-days Model statistics F-value R-square

** **           ***    

Sohn and Shim (2010)a (Metro, Seoul)

*** ** **         

This studyb (Metro, Nanjing) ** *** ** *** **

** **      

 

 

       

***

**

*** ***  

*** 

 *** *** ***



**

     

*** ***  

***   

 

***





***





56.69 0.727

15.22 0.634

85.68 0.979

Note:  refers to this variable was not examined; refers to this variable was examined but not significant; ** refers to significant at the 0.05 level; *** refers to significant at the 0.01 level. a The radius of PCA is 500, with all variables in the Table 2 of the Sohn and Shim (2010) study. b The radius of PCA is 800, multiple regression results without the correction for heteroscedasticity.

attributes, such as sidewalk quality, sidewalk continuity, and bicycle friendliness. Estupiñán and Rodríguez (2008) found that support for walking and barriers to car use are positively connected with BRT ridership. Further research regarding their impacts on Metro ridership should be conducted. The significance of intermodal connections carries very important message to URT planners: multi-modal transit matters a lot. The significant and positive relationship between Metro ridership and feeder bus lines confirms the argument that convenient transfer between Metro and bus is an effective promoter of metro usage. In addition to providing more bus services for Metro stations, numerous other promotion measures can be carried out.

One effective measure, for example, is to guarantee free transfers between bus and Metro lines, as implemented in Seoul, South Korea. As a result, the coefficient of the feeder bus variable in our study was only 987.7 for both boardings and deboardings, in comparison with 1382.6 just for boardings in Seoul (Sohn & Shim, 2010). Although a before-and-after study on the impact of free transfers on Metro ridership in Nanjing is not possible due to the fact that this measure has not been implemented, statistics from Chongqing indicate that the city’s daily URT ridership increased by about 10% after implementing free transfers between bus and rail (Ifeng News, 2013). The Nanjing local government is planning to implement free transfers between buses (but not including Metro) in

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Fig. 6. Purpose split of residential travels in Nanjing from 2005 to 2010.

Table 4 Mode share of residential travels in Nanjing from 2005 to 2010. Source: NICTP (2010). Year a

2005 2006 2007 2008 2009 2010 a

Walking

Bicycle

Bus

Metro

Taxi

Motorcycle

Official vehicle

Private vehicle

Others

24.10 26.81 26.32 25.81 25.43 25.53

41.10 42.65 40.14 38.95 37.61 36.57

22.60 18.61 19.30 18.96 19.11 17.63

NA 0.68 2.16 2.6 2.75 5.38

1.32 3.88 2.09 4.99 6.89 7.94

3.90 1.25 1.44 2.52 2.58 2.41

3.47 2.28 3.66 1.50 1.13 0.88

3.02 2.57 4.31 4.14 4.01 3.28

0.47 1.27 0.58 0.53 0.49 0.38

Data released in mid-term 2005, Metro mode share was not available.

Fig. 7. Mode share of access trips (left) and egress trips (right) of Nanjing Metro.

the near future, as long as bus riders transfer from one bus to another within two hours (China News, 2013). Based on our insights, however, the free transfer between bus and Metro should also be provided to establish a successful and sustainable multi-modal transit system and create more opportunities for TOD. Another important finding from the present study is that bicycle P&R spaces were significant in explaining Metro ridership at the 0.01 level. The estimated result of multiple OLS regression indicated an increase of 6 riders for every additional one bicycle P&R space. This result was a useful signal for cities, in particular for those where transit development is accompanied by a high share of bicycle ridership. Despite ongoing motorization, cycling is still one of the most popular transportation modes in China. In Nanjing, for example, 36% of commutes in 2010 were by bicycles (Table 3). The mode share of bicycle P&R was 2.8% for access trips and 1.0% for egress trips (Fig. 7), in comparison with 5% for overall ingress and egress trips in Shanghai (SH.EASTDAY, 2012). In addition, biking is a healthy and green travel mode. Promoting measures, such

as providing adequate bicycle P&R spaces around Metro stations, can help increase Metro ridership and relieve dependence on automobiles. The significant relationship between station ridership and the transfer dummy variable does not need to be discussed repeatedly in this study, since its positive and significant impact on rail transit ridership has been examined in many previous studies (Kuby et al., 2004; Sohn & Shim, 2010). Instead, substantial attention was given to the dummy variable that classifies a station as elevated or not, as we were curious about elevated stations’ ability to generate ridership in low-density suburban areas. As Fig. 3 shows, elevated stations are located in suburban areas which usually present an upward sloping density gradient (Fig. 8, left), while most underground stations are in downtown areas with concentrated work and residential facilities within a few blocks (Fig. 8, right). Indeed, average ridership at the 20 elevated stations was only 21,018, much less than at underground stations (33,868). However, based on results from our regression analysis, we found no evidence that

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Fig. 8. Density gradient patterns of an elevated station (left) and an underground station (left) in Nanjing. Source: EDUSHI (2012).

elevated stations were strongly connected to ridership, due in part to the fact that their impacts may have been offset indirectly by other variables, particularly land use variables. Implications and limitations Research implications This research carries several valuable implications for URT development, particularly for cities sharing similar morphology (i.e. high density, non-automobile oriented and mono centric) with Nanjing. First, by adopting a direct station-level ridership forecasting model, the present study proposed 11 significant variables to explain Metro station ridership in Nanjing. Some of them were examined in previous studies, while some were not. In particular, the number of major educational buildings, entertainment venues and shopping centers (reflecting land use diversity), the CBD dummy variable (reflecting one city center morphology), and bicycle P&R (reflecting the integration between bicycle and URT) are significantly associated with Metro ridership in Nanjing. These factors carry important messages to URT travel demand modelers in travel demand analysis. More consideration needs to be given to these factors during analysis so that substantial URT ridership improvements can be made in the future. Second, for URT planners, urban density is the critical driver of transit ridership. URT planners should prioritize areas with higher population and employment density in developing URT. The results also suggest that areas with diverse land use attract substantial non-residential and non-employment related ridership. These areas should also draw more attention from URT planners to meet travelers’ demand for URT. Third, for TOD, URT itself is inadequate. Numerous riders access URT stations via buses and bicycles. Mu et al. (2012) found that the modal split after system integration between bus and rail transit tilts more strongly towards transit. A cooperative multi-modal transit system integrating bus, rail, taxi, and public bicycles (while the impacts of taxi and public bicycles were not investigated in the present study, their function should not be neglected) should be established to reduce over-reliance on automobiles. Research limitations This research has several limitations. First, this paper focused on factors affecting rail transit ridership at the station level by

adopting a direct forecasting model. Factors influencing rail transit ridership at the station-to-station level, individual level, or metropolitan scale, were omitted. Second, many other factors influencing transit ridership at the station level were not included due to lack of data, particularly the urban form variables and socioeconomic variables within the PCA. Nevertheless, previous studies have shown them to be significantly connected to transit ridership (Estupiñán and Rodríguez, 2008; Kuby et al., 2004). Third, the indirect or cyclic relationships between variables cannot be examined by the regression analysis, whereas structural equation model (SEM) may perform well (Sohn & Shim, 2010). Conclusions This study shed light on factors affecting Metro ridership at the station-level in China, which is undertaking one of the most ambitious rail transit construction projects in the world. Land use, external connectivity, intermodal connection, and station context may influence density, diversity, and design, thus the station-level Metro ridership. We tested this hypothesis with data from various sources, collected for 55 Metro stations along two Metro lines in the city of Nanjing. Direct ridership model based on GIS and multiple regression analysis was adopted to explore the relationship between Metro station ridership and variables measuring land-use, external connectivity, intermodal connection, and station context. The legitimacy of estimation results were discussed based on intuitive comparison between the outcomes of the present study and that of previous research as well as insights about Nanjing. The results suggested that 11 variables are significantly related to Metro station ridership within the PCA: population, employment, residential building area, number of major education buildings, entertainment venues and shopping centers, road length, feeder bus lines, bicycle P&R spaces, and transfer dummy variable. The newly introduced bicycle P&R, and the number of major educational buildings, entertainment venues and shopping centers are highly significant variables as well. In addition, the CBD dummy variable is significantly associated with Metro station ridership in Nanjing and produces the highest coefficient. These results not only confirm the findings of the existing literature on this topic, they also show distinct differences regarding some variables specific to the Chinese context. Our findings have several implications. For URT ridership forecasting and modeling, URT modelers should incorporate variables that are significantly associated with station-level ridership, in order to improve their URT demand analysis. For urban planners, the

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priority of areas with high land-use density and diversified landuse mix should be guaranteed in installing URT stations. For TOD, establishing a multi-modal transit system can provide more opportunities. Acknowledgements This research has been supported by the National High-tech R&D Program of China (863 Program) (No. 2007AA11Z202), the Research and Innovation Project for PhD Candidates of Jiangsu Province (CXZZ11_0165), and the Fundamental Research Funds for the Central Universities of China. We gratefully acknowledge the digital land and transportation data provided by Nanjing Planning Bureau. In addition, we wish to express our gratitude to Professor Ali Modarres from California State University at Los Angeles, Yichun Tu from University of North Carolina at Chapel Hill, and Elliot Ward from New York University for their editorial and linguistic support and to several anonymous reviewers for their critical comments. Appendix A. Quantitative thresholds on developing URT systems in China

Category Economic power (billion, CNYa)

Urban population (million)

Travel demandb

Government revenue GDP Light rail 100 Metro 100

600 150 1000 300

10,000 30,000

Source: SCC (2003). a CNY, or China Yuan, is the currency of China. At the time of writing, the exchange rate is CNY 1 to USD 0.161 approximately. b Expected corridor ridership per hour during peak hours.

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