Rethinking bus-to-metro accessibility in new town development: Case studies in Shanghai

Cities 94 (2019) 211–224

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Rethinking bus-to-metro accessibility in new town development: Case studies in Shanghai

T

Shan-shan Wua, Yu Zhuanga, , Jiayu Chenb, Wei Wangc, , Yunxi Baid, Siu-ming Lob ⁎

⁎

a

Department of Architecture, Tongji University, Shanghai, China Department of Architecture and Civil Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong c School of Architecture, Southeast University, 2 Sipailou, Nanjing, Jiangsu Province，China d School of Architecture, Harbin Institute of Technology, Shenzhen, Guangdong Province, China b

ARTICLE INFO

ABSTRACT

Keywords: Accessibility Public transportation New town Social network analysis Evaluation Optimization

With the rapid urbanization in cities around the world, new towns that are close to the existing urban fringe have been developed to accommodate the increasing population. However, due to the long development time of the infrastructure systems in these new towns, the establishment of public transportation services usually lags behind the population expansion. Therefore, to ensure urban accessibility, governments utilize bus networks to bridge the connectivity gaps of metro systems. To assist the design and decision-making required for bus and metro interconnectivity and optimize public transportation networks, this study proposed a quantitative network-based framework. The proposed framework extended the existing social network analysis theory and identified five indicators to assess and optimize the network design. To validate the proposed method, nine typical cases in Shanghai were examined. The results based on the proposed analysis framework suggest that more edges between access points (bus stops within the walkable area of a metro station entrance) and other nodes can improve the accessibility of the study area and create a well-integrated system. Therefore, this study is able to provide an insightful understanding of intermodal transportation coordination and transport facility arrangement.

1. Introduction With rapid economic development, China's urbanization process has also accelerated over the past few years, and accordingly, the population of major cities in China has significantly increased. In particular, the populations of Beijing and Shanghai have increased from 15.81 million to 21.73 million1 and 19.64 million to 24.20 million2 in ten years, respectively. To accommodate the population growth and reduce the pressure from urban expansion, the government started to convert rural areas in the urban fringe into new towns (Lau & Chiu, 2013) under the concept of transit oriented development (TOD). According to TOD, the urban areas are compact areas that are usually located within walking distance of transportation stations (Cervero, 1995). Due to the distance between the new towns and the city center and the irreversible spatial mismatch of employment and housing (Zhou, Chen, & Zhang, 2016), accessibility becomes an important issue in new town development. Public transportation systems, such as mass transportation

systems (MTSs) and bus systems (BSs), are expected to cover the majority of the travel demand in the new town areas. However, the dispersed population and low density of MTS stations result in relatively low accessibility in new towns. Accessibility improvement includes many tactics, such as densifying the MTS network and improving access by allowing transfers between different transportation modes. The former approach is difficult to implement in the short-term since the construction period of an MTS station is comparatively long. Additionally, the location of MTS stations is affected by the demographic and economic conditions, while improving access to MTS stations through buses, cycling and walking is more realistic. The flexible bus, cycling and walking system operates in collaboration with the fixed MTS system and improves access to nonMTS areas. For example, new transportation systems, such as feeder buses (Almasi, Sadollah, Mounes, & Karim, 2015; Kuan, Ong, & Ng, 2006; Lin & Wong, 2014; Verma & Dhingra, 2006), are used to connect the MTS stations and non-MTS areas. Nevertheless, this approach is not

Corresponding authors. E-mail addresses: [email protected] (Y. Zhuang), [email protected] (W. Wang). 1 Data resource: Beijing Municipal Bureau of Statistics, http://www.bjstats.gov.cn/tjsj/ 2 Data resource: Shanghai statistical yearbook, http://www.stats-sh.gov.cn/html/sjfb/tjnj/ ⁎

https://doi.org/10.1016/j.cities.2019.06.010 Received 18 May 2018; Received in revised form 2 June 2019; Accepted 5 June 2019 0264-2751/ © 2019 Published by Elsevier Ltd.

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economically viable in low-density areas. Other than feeder buses, there are other potential options for improving the transfer service of existing transportation systems (Song, Chen, Xian, & Sun, 2012; Sun, Sun, Li, & Gao, 2013). The methods based on the commonly used and well-accepted MTSs and BSs require less effort. Thus, the integration of MTSs and the original BS could be an optimal solution for improving new town accessibility. Transfer accessibility—especially bus-to-MTS transfer accessibility (B2MTA)—is essential in new town development because it affects citizens' access to city centers and services. Many studies have revealed that the interconnections between two transportation systems determine the population coverage of the available public services (Seriani & Fernández, 2015; Wang, Chen, & Xu, 2016) and capital investment in transportation infrastructure (Fisch-romito & Guivarch, 2019; Lee & Miller, 2017). Therefore, a feasible, reasonable, and efficient assessment tool is significant and necessary for assisting with decision making and inter-transfer system design. However, the transportation networks operating in city fringe areas were usually regarded as the analysis boundary in previous studies (Bigotte, Krass, Antunes, & Berman, 2010; Chen, Yu, Zhang, & Guo, 2009). To fill this research gap, this research targets new towns close to city boundaries and integrates multiple systems as a whole, such as the bus, metro, and bicycle systems. Through formulating a quantitative analysis framework, the interconnectivity needed for new towns can be quantified, assessed, and optimized. This study extended the research of social network analysis (SNA) and greedy algorithms and composed an indicator-oriented network optimization approach based on the network structures, such as the network density, network robustness, closeness centrality, betweenness centrality, and degree and eigenvector centrality. In summary, this study makes the following contributions to the body of knowledge.

quantity of the destinations (Novak & Sullivan, 2014). Correspondingly, accessibility studies usually fell into three categories: the accessibility of origins where the travelers were located (Vasconcelos & Farias, 2012; Xu, Zhang, & Li, 2017), cost/accessibility of different travel modes (Jackiva & Budilovich, 2017; Kim, Lee, Park, & Song, 2018), and accessibility of destinations (Fan, Xu, Yue, & Chen, 2017; Li & Liu, 2017). According to Geurs and van Wee (2004), accessibility included infrastructure-based, location-based, person-based, and utility-based measures. Additionally, two typical issues in all the categories, travel distance and time, were widely used in previous studies (Kim, 2018; Lin et al., 2018; Yu & Fan, 2018) depending on the transportation system network, such as road or rail. Sagphpour et al. used regression models and incorporated accessibility measures to enhance active transport demand modeling (Saghapour, Moridpour, & Thompson, 2018). Papa and Bertolini (2015) used node indices (total number of nodes) to represent the infrastructure-based measures and place indices (average density of inhabitants and jobs) to represent the location-based measures. Xu et al. (2017) developed an expected locational accessibility index to determine the reachable stations with restricted transfer times of all the transit stations on the network. Additionally, comprehensive indices could be derived from the basic indices to evaluate the specific accessibility problem. For example, Novak and Sullivan (2014) derived the critical closeness accessibility (CCA) metric from the closeness centrality metric and used it to evaluate the accessibility of links to emergency services on a road network. Chen, Ni, Xi, Li, and Wang (2017) used integrated accessibility metrics to assess the accessibility distribution of the public transport system in Nanjing, which illustrated the disparities in accessibility accurately (Chen et al., 2017). Yu and Fan (2018) integrated a transportation accessibility assessment with the walking, parking and transfer caused by road traffic and high-speed rail and indicated that accessibility not only depends on the networked transport, such as rail and subway systems, but also on the connectivity to surrounding facilities (Moyano, Moya-Gómez, & Gutiérrez, 2018). Chandra, Bari, Devarasetty, and Vadali (2013) developed an accessibility model based on fixed-service route transit and demand responsive transit. Seo and Nam (2018) studied the trade-off relationship between public transportation and the economy through an analysis of MTS access values by housing size (Seo & Nam, 2018). Accessibility evaluation studies can provide useful tools to compare the available options for accessibility optimization and facilitate strategic improvements. For example, Curtis & Scheurer (2010) (Progress in Planning, 2010) developed a spatial network analysis for a multimodal urban transport system that incorporated a set of accessibility measures. Some of the generative optimization methods further extended the abilities of an accessibility evaluation. Manout, Bonnel, and Bouzouina (2018) developed a method that can generate transit stops and routes based on high-resolution spatial data. The predicted total boarding of the new method was close to the observed data. SNA is also a good choice in many studies. Although SNA previously appeared in sociology research (Barry, 1988), it has also been adopted in transportation studies. Macroscopic studies consider the worldwide or nationwide transportation system as a network. For example, Guimera, Mossa, Turtschi, and Amaral (2005) and Cheung and Gunes (2012) considered airlines as a network and selected the characteristics of an airline network, such as the small world properties and degree distribution, to characterize the roles of the cities in the global air transportation network. Tsiotas and Polyzos (2015) identified network mobility centrality in a case study of the Greek interregional transportation using SNA. Other studies have analyzed smaller transportation systems, such as metros or subways. For example, Latora and Marchiori (2002) investigated the small-world characteristics of the Boston subway system. Gattuso and Miriello (2005) compared 13 metro systems of various metropolitan areas. In addition, Derrible and Kennedy (2010) compared 33 metro networks in world cities to study the characteristics of their state, form, and structure, which are defined as network indicators in SNA and can be used to evaluate the metro system during

(1) This study proposed a novel, comprehensive, and adaptive framework for assessing and optimizing B2MTA services to enhance the accessibility of new towns in urban fringes; (2) This study integrates SNA and a greedy algorithm for the multistep analysis of B2MTA systems and validates the approach with nine actual cases of metro lines in the city of Shanghai; (3) This study reveals heuristic insights gained from evaluating MTSs in urban areas to broaden the studies of networked urban systems and support sustainable urban design. The remainder of the paper is organized as follows. Section 2 provides a brief review of accessibility issues and the current evaluation and optimization studies. Section 3 presents the methodology of this study, including the formation and indicators of bus networks and also the evaluation and optimization processes. Section 4 presents the indicator comparison and optimization processes. Section 5 discusses the implications and limitations of this study, and the final section concludes this study. 2. Background Urban public transportation has the potential to reduce automobile dependence, readdress numerous vexing contemporary urban problems (e.g., traffic congestion, air pollution, greenhouse gases, and so on), and facilitate urban development (Gulhan, Ceylan, Özuysal, & Ceylan, 2013). It is important and imperative to build an attractive and accessible public transport system for sustainable urban development (Yang, Zhou, Shyr, & Huo, 2018). Accordingly, transportation accessibility has significant impacts on both individuals and human society. Niemeier (1997) proposed one of the most commonly used definitions, which defined accessibility as the ease with which desired destinations may be reached (Niemeier, 1997). Transportation accessibility usually captures three basic components, the traveler, the cost of travel (from the traveler's origin to the proposed destination), and the quality/ 212

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Fig. 1. An example of network formation. a. A map of the research area from Baidu (https://map.baidu.com/); b. Bus stops and MTS stations within the research area; c. The 200 m radius zone around the MTS entrance and the specific access points from other bus stops; d. All the bus stops connected with edges according to the actual bus route; e. Network with nodes and edges.

the planning stage. Pereira analyzed the benefits of transport infrastructure expansion by reorganizing bus lines to improve the accessibility to urban destinations (Pereira, 2018).

node in the network. This new index has proven helpful in finding the community characteristics. 3.1. Network formation

3. Methodology

The formation of a network consists of the nodes and edges. In this study, the nodes represent the bus stops in the research area, and the edges represent the connections (bus routes) between the bus stops. To emphasize the accessibility of the BS to MTS stations, the bus stops are specified as access points if they are within a walkable 200 m radius of the MTS entrance (Fig. 1). In the TOD-related study, a quarter mile (approximately 400 m) is commonly used to assess the walkable distance to MTS stations (Nasri & Zhang, 2014). However, the distance is not suitable for transportation transfers. Chen and Chang (2015) used 100 m as the appropriate distance to green spaces in Hong Kong (Jiayu Chen & Chang, 2015). In our study, the distance between the MTS stations and the nearest bus stop are usually larger than 100 m and less than 200 m. As a result, a 200 m radius is used to assess the access points. The location information of the bus stops, bus networks, and MTS entrances are based on Baidu maps.

This study uses network connectivity and robustness as measures of accessibility for investigating the bus networks in the buffer areas of metro stations. A novel research framework for evaluating and optimizing B2MTA accessibility is proposed, and the framework involves the formation of bus networks (abstractions of the nodes and edges), the identification of appropriate indicators, and the evaluation and optimization of case studies. The SNA method was utilized to evaluate the bus networks and then optimize the bus networks with low accessibility. SNA is a data processing scheme that deals with the structural features of the network and the dynamic interactions within the system (Jiayu Chen & Chang, 2015). Combe, Largeron, Egyed-Zsigmond, Géry, and Od Egyed-Zsigmond (2010) suggest three significant issues in SNA: graph visualization, computation of various indicators, and community detection. The indicators include the vertex and edge characteristics (degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality) and network characteristics (network density). Community detection identifies the communities (nodes in the same range), and the indicators can be combined and developed to represent other properties, such as the small-world properties and degree distribution (Porta, Crucitti, & Latora, 2006). In addition to the conventional indicators, some recent studies have proposed new indicators to expand the analysis. For example, Latora and Marchiori (2007) proposed delta centrality, which evaluates the relative importance of a

3.2. SNA indicators This step identified the important indicators to investigate the accessibility of the local bus network. Two kinds of indicators were considered: the network indicators and the access point indicators. The former evaluated the accessibility characteristics of the entire network in the study area, including the network density and the network robustness, while the latter evaluated the performance of the access 213

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points, including the closeness centrality, betweenness centrality, and degree and eigenvector centrality. These indicators are introduced in the Appendix. Since precise demographic data were not available for the case study areas, the framework of this study was constructed based on the first assumption: that the existing transportation system had fully considered the demand-supply correlation in the regional or urban area. This assumption suggested that the bus stops were located at places with the maximum demands. Some studies utilized passenger volume data (literature) or survey data for transportation users (Lin et al., 2018) to represent the overall accessibility. However, this study did not use those types of datasets since (a) there were no official data for the bus stops or routes, and although it was possible to count the passengers on-site, the results may be affected by the population adjacent to the bus stop rather than the network itself; (b) the survey may be affected by the locations and travel behaviors of the respondents. In this study, the connectivity and robustness of the network were analyzed and compared based on the second assumption, which stated that transportation systems that have operated for a longer time perform better than new transportation systems. This assumption was established based on the idea that the transportation agencies would have collected data, such as the origin-destination matrix, through years of operation and adjusted the transportation network according to the data. Thus, the network would be more efficient at the local scale than a newly opened one.

becomes insignificant. Thus, the optimum number of additional links and the added links can be obtained as outputs. 4. Description of the case studies Nine zones in three new towns in Shanghai are selected as case studies (Fig. 3). As shown in Table 1, District A is a new town that has recently opened MTS Line 17. The total area of the new town is 670.14 km2, and the population density is approximately 1813 person/ km2. The MTS lines in Districts B and C were opened between 2012 and 2013, much earlier than the one in District A. The areas of Districts B and C are 464.2 and 605.64 km2, respectively, and both districts have a population of approximately 3000 person/km2. The population densities in the three new towns are far less than those in the city center (approximately 20,000 person/km2),4 which indicates that the populations of the new towns are more dispersed than that of the city center. Furthermore, the nine cases include three geographical positions. Positions A1, B1, C1 are at the end of MTS lines. Positions A2, B2 and C2 are located at the center of the new towns. Positions A3, B3 and C3 are located at the junctures of new towns and the city center. Thus, the nine cases can describe the characteristics of the local bus networks at different locations. In addition, the 1000 m catchment area of the metro station was identified as the case study area. 5. Results

3.3. Network optimization

5.1. Network analysis

The basic optimization process requires a fixed number of additional links to compare the results of a different combination of new links. However, the number is difficult to determine before the analysis. As a result, this study uses an alternative method, which measures the improvement of the bus network after adding one new link to the original network. The link that generates the largest improvement is selected and added to the network. Then, a second link can be generated based on the new network in the same way. The process continues until adding another new link cannot significantly improve accessibility. This method is called the greedy algorithm, which is used to find one optimal edge at each iteration. The improvement of the network at each iteration is reflected as a delta function (∆uv), which describes the normalized changes in the indicators as follows: uv

=

XL + 1 XL XL

Figs. 4, 5, and 6 show the network formation of the nine case studies, and Table 2 lists the results for the number of nodes, edges and access points, and also the network indicators (network density and robustness of the network). The average values do not indicate that the developed cases have higher values than the developing cases. For example, the average number of nodes and edges and the network robustness of District A are smaller than those of District B but larger than those of District C. The average network density of District A is equal to that of District C and larger than that of District B. The possible explanation for this phenomenon is that the number of nodes and edges and the network density were largely dependent on the travel conditions of the entire region. The current conditions could satisfy the transportation needs in the developed district, which results in comparatively smaller values. The larger number of access points in District A indicates the intention of linking the BS with the MTS. The indicator of robustness is slightly different, and from the overall network robustness perspective, the average robustness of the cases in District A is larger than that of the cases in District C but smaller than that of the cases in District B. According to the data, the robustness indicator is not significant in the evaluation. However, as shown in Table 3, the average minimum value of the node robustness in District A is the smallest of the three cases (0.48), followed by that of District C (0.58). The smallest robustness values also appear in District A (A1 and A3, 0.27 and 0.33, respectively). The data indicate that the networks in A1 and A3 contain a very vulnerable node (bus stop). A disruption on the node may cause the paralysis of the system. As a result, the robustness indicator is selected for optimization. Table 4 shows the access point indicators of the nine cases. The access point indicators show a clearer tendency for the developed cases to have higher values than the developing cases. The average closeness centrality is the only exception, in which the average closeness centrality of District A is equal to that of District C and larger than that of District B. This result may be due to the formation of the network. The metro stations are considered as the center of the study areas, which

(1)

where u and v are the vertex indices that the new edge connects, X represents the indicators that are selected for optimization (for example, the closeness centrality), L is the network with l edges (or the original network), and L + 1 is the network with one additional edge. Fig. 2 shows the process of our study, which includes three major steps. The first step is network formation. The bus stop data and the bus route data are represented by the node set and the edge set to form a network. Furthermore, the access points are also identified according to the geographical relationship between the nodes and entrances to the MTS station. In the second step, this study determines the indicators of the analysis and comparison. First, the important indicators are identified based on the SNA and previous studies. Then, the indicators are analyzed and compared in the cases3 to show their importance in relation to accessibility. After the indicators are selected, the overall accessibility of the cases based on those indicators will be compared. The case with the worst performance will be used for optimization in the next step. The third step involves network optimization. Under the additional rules that restrict the process, the greedy algorithm will generate an additional link that will maximize the delta value at each iteration. The process will end when the change of the delta value 3

4 Data resource: Shanghai statistical yearbook, 2017. http://www.stats-sh. gov.cn/html/sjfb/tjnj/

The cases for analysis are introduced in Section 4 214

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Fig. 2. Flowchart of the research process.

means that the access points are more or less at the center of the network. Therefore, the average closeness centrality differs little between Districts A and B. The lower value in case B is mainly caused by cases B1 and B2. There are distinctive differences between the District B and C cases. There are more points in District C than District B (3 compared to 2). In case B1, one of the access points only links to one other access point, making it meaningless as an access point. These facts may have caused the extremely low value of B1 (0.1). Furthermore, the cases that are located at the junctures of new towns and the city center (B3 and C3) have a higher accessibility than the other cases. For example, the average betweenness centrality and

average eigenvector centrality in B3 (0.13) and C3 (0.15) are larger than those in B1 (0.04), B2 (0.07), C1 (0.12), and C2 (0.10). The robustness of B3 (0.99) is also better than that of B1 (0.85) and B2 (0.97). However, the difference of the three types of cases (the cases at the end of the MTS lines, at the new town centers, and at the junctures of the new towns and the city center) are not clear in the two tables. To illustrate their differences, a multicriteria analysis process is used to give scores to the nine cases (Gattuso & Miriello, 2005). It is assumed that the three indicators are equally important. For each indicator, the maximum value (XMAX) and the minimum value (XMIN) are identified. Then, the score of the ith case (SiX) can be calculated using the formula: 215

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Fig. 3. The locations of the nine cases. Table 1 Basic information of the new towns. District code

Name

MTS line no.

MTS open date

Area (km2)

Population density (person/km2) (by 2016)

A B C

Qingpu Songjiang Jiading

Line 17 Line 9 Line 11

2017.12.28 2012.12.30 2013.10.16

670.14 605.64 464.2

1813 2914 3403

Note for State of MTS development: The MTS in District A is newly opened. Cases in this district are defined as developing cases. The MTS in District B has been open for more than 5 years. Cases in this district are defined as developed cases. The MTS in District C has been open for more than 4 years. Cases in this district are defined as developed cases.

SiX =

Xi XMIN × WX XMAX XMIN

means that the accessibility of case A2 has not reached an appropriate level. As a result, the accessibility of A2 needs to be optimized.

(2)

where Wx is the weight of indicator X. In this study, the weights of all the indicators were assigned the same value. This means that the indicators were equally important. The scores of each indicator and the total scores of the nine cases are presented in Table 5. Table 5 reveals a strong tendency that the cases located at the junctures of the new towns and the city center have larger scores (2.52 and 2.67) than those in the new town center (1.65 and 1.85), and they are both larger than the scores of the cases located at the end of the MTS lines (0.30 and 1.29) in Districts B and C. Additionally, the score of case B1 (0.30) is the lowest, but it may be due to the small population or another demographic issue. However, the cases in District A are the opposite (with scores of 1.35, 1.00, and 0.77 in cases A1, A2, and A3, respectively). Case A3 has low scores possibly because the entrances to the MTS station are not fully operational. As shown in Fig. 7, node 3 is not an access point yet, and the opening of the new entrance could possibly improve the accessibility of case A3. The score of case A1 (1.35) is close to that of C1 (1.29), which means that the accessibility of A1 is similar to that of cases in developed areas. The score of case A2 (1.00) is much smaller than that of B2 (1.65) and C2 (1.85), which

5.2. Optimization results As discussed above, the robustness of the network (R), betweenness centrality of the access points (BCi), and eigenvector centrality (ECi) are the three important indicators used to evaluate the accessibility of the bus network with the MTS stations. These three indicators are selected for optimization in the A2 case study in the new town. This section presents the optimization process and results. The optimization process updated the network with one optimal edge at each iteration. The delta values of the possible networks were calculated with every potential additional edge considered. The network with the maximum delta value was selected for the next iteration. A curve that described the delta value changes was drawn based on the iterations. The “elbow method,” which states that the indicators do not give much better results at certain points than the points before, was used to determine the “elbow” point. The additional edges at the “elbow” point were used as the optimal results of the optimization. Two rules were used to generate the additional edges in the optimization process: (1) the additional 216

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Fig. 4. The map information and network structures of the three cases in District A. Above: The Baidu map of the area. Below: The networks of the three cases.

edges were generated using two nodes, which did not include the access points; and (2) the additional edges were generated by both the access points and the other nodes. Fig. 8 shows the matrix of the delta values generated at each iteration. The color of the cell in the matrix represented the delta value of connecting the two nodes (the node numbers were marked on the grid). The gray color indicated that the two nodes cannot be linked, for instance nodes that were already connected. There were more gray cells under rule (1) than rule (2). The various shades of green represented different values of the delta value. The maximum delta value under rule (2) was larger than under rule (1), and the number of green cells under rule (2) was also larger. The results suggested that the access points played an important role in bus-to-metro accessibility. Better connections between the access points and the other nodes would create a highly integrated bus-metro transportation system. Thus, a vibrant and efficient transportation structure could be formed by effectively connecting the intradistrict transportation (bus) system and interdistrict transportation (metro) system. Fig. 9 shows graphs of the delta values for different trials. According to the elbow method, the elbow point of rule (1) was one (additional edge), and of rule (2) was five (additional edges). The added edges are marked in Fig. 10. In the optimization process under rule (1) two nodes were connected (nodes 21 and 24) to form a new network. In the optimization process under rule (2), five edges were added (access point 2 and node 9, access point 2 and node 11, access point 3 and node 14, access point 3 and node 16, access point 3 and node 23). All of the additional edges were linked to access points 2 and 3, which had three edges in the original network. The edges linked to the two points increased to 5 and 6, respectively. Thus, the two access points could

operate as transfer hubs that linked many bus routes. As a result, rule (2) not only increased the delta value in only one iteration but also provided more opportunities to add new edges to create transfer hubs and raised the accessibility of the network. The optimization process and results showed that the proposed method can produce reasonable results based on the existing bus network and MTS station. It proved that our research framework can be used to increase the accessibility of the bus network to the MTS, which was helpful in satisfying the intradistrict transportation needs of the local people. 6. Discussion and limitations This study developed a novel network-based framework for evaluating and optimizing the bus-to-MTS transfer accessibility (B2MTA) in the metro station catchment area using case studies in Shanghai. Nine cases were selected in this study from three new towns with different operating periods. The results show that the total accessibility evaluation for the newly opened metro line (case line A) is lower than that for the developed cases (lines B and C). The average accessibility score of the three line A stations is only 1.04, while they are 1.49 and 1.94 for lines B and C, respectively. To increase accessibility, the proposed framework can help optimize the bus service to the MTS network. One case study (A2) in the new town was selected for further optimization and used to validate the effectiveness of the framework. In addition, the methodology proposed in this study for testing and optimization a novel application in global urbanization of using an SNA to integrate a bus network with the MTS in new towns during. On the one hand, the methodology can be easily applied to evaluate and optimize a local bus network. The access points are the important intermediaries between 217

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Fig. 5. The map information and the network structure of the three cases in District B. Above: The Baidu map of the area. Below: The network structures of the three cases.

the BS and the MTS, and this study provides useful insights into the systematic design of multimode transportation planning. On the other hand, this study is more suitable for short-term rather than long-term optimization. However, the transformation process is a continuous way to enhance MTS accessibility and urban transportation. As a result, short-term optimization is appropriate according to the dynamic characteristics of urban development. Additionally, the optimization process should consider the demographic data to adjust the positions of the existing nodes (bus stops) or when creating new nodes (bus stops). In the future, the above-mentioned short-term optimization models will be studied by tracking the development of District A (Qingpu District) in Shanghai. For future works related to city planning or MTS development, this study provides referential insights for how to evaluate and optimize urban facilities at the operational and design stages rather than only the MTS. For example, future work can apply the proposed framework to evaluate and improve green sites or commercial sites, and future work can also integrate bike sharing into the MTS or bus network systems to improve accessibility and make full use of the transportation facilities to facilitate urban planning. The accessibility of public transportation is important in urban development, especially for the residents of new towns in China, and the low density of MTS station distribution leads to low accessibility to other urban districts. Previous studies have paid more attention to the optimization of only one public transportation mode (Ghatee & Hashemi, 2008; Huang, Liu, Huang, & Shen, 2010; Zhao & Zeng, 2008)

or the systematic integration of transportation on the macro scale. However, this study proposed one framework to investigate the accessibility evaluation and optimization of bus systems and metro systems and validated this framework with nine case studies in Shanghai. According to the Shanghai statistical bureau (2018), the total turnover volume of buses has decreased gradually while that of the MTS has increased. However, the BS and the MTS should not be in competition with each other but rather work cooperatively. Thus, the advantages of the MTS (large capacity and fast speed) and the BS (flexible routes and wide distribution) can be combined to form an efficient transportation network. The study aimed to fill a research gap by improving public transportation integration, which has been discussed in both developed and developing countries. Nonetheless, previous studies emphasized the access/egress time of certain transportation systems (Djurhuus, Sten Hansen, Aadahl, & Glümer, 2016; Kumar, Arora, & Singhal, 2017) and the catchment areas of stations (Seriani & Fernández, 2015; Wang et al., 2016). This study emphasized the importance of the integration interface between different transportation systems (Fig. 11). Seriani and Fernández (2015) considered the interface of two transportation systems as the distance for the interchange. This study further expanded the interfaces of bus and metro systems by including not only the distance between the nearest bus stops and metro stations but also the bus network in the metro catchment area. Thus, our model can effectively improve transportation integration. This study also has some limitations. First, the number of cases used

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Fig. 6. Map information and network structure of the three cases in District C. Above: The Baidu map of the area. Below: The network structure of three cases. Table 2 Comparison of the network indicators in the nine case studies. District A

Total no. of nodes Total no. of edges no. of access points Network density Robustness of the network

District B

A1

A2

A3

Average

B1

B2

B3

Average

C1

C2

C3

Average

12 13 3 0.20 0.91

24 31 5 0.11 0.97

10 10 2 0.22 0.90

15.33 18.00 3.33 0.18 0.92

23 27 2 0.11 0.85

23 30 2 0.12 0.97

17 24 2 0.18 0.99

21.00 27.00 2.00 0.13 0.94

14 13 3 0.14 0.81

13 17 3 0.22 0.95

14 16 1 0.18 0.93

13.67 15.33 2.33 0.18 0.90

in the study is not sufficient. The characteristics of the bus networks and other transportation systems may be different in Shanghai or other cities in China. Second, the number of public transportation users is not considered in this study since the data are difficult to collect; however, the on-site population and user response to MTS accessibility or bus networks are very important for transportation optimization. Third, this

Table 3 The maximum and minimum value of node robustness in the nine case studies.

Maximum Minimum

District C

A1

A2

A3

B1

B2

B3

C1

C2

C3

1.00 0.27

1.00 0.83

1.00 0.33

0.91 0.68

1.00 0.86

1.00 0.88

0.92 0.54

1.00 0.75

1.00 0.46

Table 4 Comparison of the access points in the nine case studies. District A

Avg. Avg. Avg. Avg.

degree closeness centrality betweenness centrality eigenvector centrality

District B

District C

A1

A2

A3

Avg.

B1

B2

B3

Avg.

C1

C2

C3

Avg.

2.33 0.04 0.18 0.09

2.80 0.02 0.06 0.04

3.00 0.06 0.00 0.07

2.71 0.04 0.08 0.07

2.50 0.01 0.04 0.04

5.00 0.02 0.26 0.07

7.00 0.04 0.37 0.13

4.83 0.02 0.22 0.08

3.67 0.04 0.30 0.12

3.67 0.04 0.28 0.10

5.00 0.04 0.53 0.15

4.11 0.04 0.37 0.12

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Table 5 The three indicator scores of the nine case studies.

Robustness of the network Indicators of access points Total score

Avg. betweenness centrality Avg. eigenvector centrality

A1

A2

A3

B1

B2

B3

C1

C2

C3

0.56 0.34 0.34 1.35

0.89 0.11 0.11 1.00

0.50 0.00 0.00 0.77

0.22 0.08 0.08 0.30

0.89 0.49 0.49 1.65

1.00 0.70 0.70 2.52

0.00 0.57 0.57 1.29

0.78 0.53 0.53 1.85

0.67 1.00 1.00 2.67

Fig. 7. If the new entrance opens, node 3 can be identified as an access point.

Fig. 8. Example of delta values under two different rules.

study identified three importance indicators, network robustness, betweenness centrality of the access points, and eigenvector centrality; however, this study did not emphasize the importance weights while assessing accessibility, which requires further exploration. To overcome these limitations, cases from different cities will be studied and compared to illustrate the appropriate framework for integrated transportation systems in future new town developments in China. Future work should also fully consider the number of public transportation users and their behavior in the optimization model. Moreover, although the framework in this study can achieve good performance in evaluating and optimizing B2MTA scenarios, other algorithms (e.g., genetic algorithms) as well as real road situations and networks will be considered to compare the optimization performance in urban transportation studies in the future.

7. Conclusion Access to transportation is crucial for new town residents within China. This study proposed a framework based on social network analysis and greedy algorithm to evaluate and optimize local bus networks and enhance the accessibility of bus-to-MTS transfers. Through a comparison of nine case studies in Shanghai, we found that the robustness of the network, average betweenness centrality and average eigenvector centrality of the access points (bus stops adjacent to the MTS station entrances) are important to consider in the integration of the two transportation systems. The results of the multicriteria decision analysis illustrate that some cases in District A, which has newly opened MTS lines, have lower scores than those in the developed districts. Furthermore, the results indicate that the local bus network should be 220

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Fig. 9. Delta and indicator values under two different rules.

Fig. 10. Edges added by the optimization process under two different rules: a. Edges added by the optimization process under rule (1); b. Edges added by the optimization process under rule (2).

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Fig. 11. Implication of transportation integration.

optimized when a new transportation system opens so that it can be incorporated into an efficient multimodal transportation system. One case study (A2) in a new town was selected for further optimization under two different rules. The results show that generating additional edges between the access points and other nodes has a higher delta value than when the access points are excluded. Thus, generating more edges between access points and other nodes can improve the accessibility of the study area and create a well-integrated system. Additionally, the methodology proposed in the study is easily applied in practical situations. This study provides useful insights into the integration of different transportation modes. Intermodal coordination not only relates to the demographic and travel behavior characteristics of the city but also depends on the integration interface. Correspondingly, the analysis of transfer accessibility is an essential step in realizing an efficient and

sustainable transportation system. In the future, more optimization algorithms will be adopted in solving MTS accessibility problems in new towns, and more cases will be considered in different cities. In addition, the transfer accessibility of MTSs, BSs and other transportation systems will be studied to achieve intermodal coordination on different scales. Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Acknowledgments Research Grants Council, University Grants Committee > of the Hong Kong Special Administrative Region, No. CityU 11300815.

Appendix A Network density The density of the network is defined as the ratio of the number of edges to the number of possible edges in a network with given nodes. The network density gives a distinct description of how the network is connected. With the maximum value of 1, all the nodes are connected with each other to form a complete graph. In our study, the network density describes how the bus stops connect with each other. The network density can be calculated by the following equation:

Dnet =

2l n (n 1)

(1)

where Dnet is the network density, l is the number of edges, and n is the number of nodes. Network robustness The robustness of a network refers to its ability to withstand disturbances. In a bus network, disturbances are traffic jams, car accidents and other events that can block the streets and affect the bus schedule. This concept can help to understand the vulnerabilities of transportation systems. The robustness of a system could be improved either by raising the connectivity of the network or by using bus systems to bridge the vulnerable nodes of the network. The robustness of the network is the average of the node robustness, which can be expressed as follows: Ni

R=

n

1

(2)

n

where R is the robustness of the network, and Ni is the number of nodes of the largest connected component (LCC) when node i is deleted from the network. Closeness centrality Closeness centrality measures the inverse sum of the distance from a node to all the other nodes in a network. It describes the ease of getting to other nodes from a selected node. Specifically, in our study, the higher the closeness centrality of an access point is, the shorter the distance is between the other bus stops and the access point. Thus, the distance between those bus stops and the MTS stations is also small. The equation of closeness centrality is written as follows: 222

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CCi =

2

Ai n

1

1 Ci

(3)

where CCi is the closeness centrality of node i, Ai is the number of reachable nodes from node i (not counting i), and Ci is the sum of the distances from node i to all the reachable nodes. Betweenness centrality The betweenness centrality measures how often certain nodes appear on the shortest path of the other two nodes. The betweenness centrality of an access point refers to the level of accessibility and connectivity within the network. In our study, the access points connect other bus stops to the MTS station. If an access point has a higher value of betweenness centrality, it indicates that it is easier to get to the MTS station from the other bus stops and vice versa. The betweenness centrality is given as follows:

BCi = s, t i

lsti lst

(4) i

where BCi is the betweenness centrality of node i, lst is the number of shortest paths from s to t that pass-through node i, and lst is the total number of shortest paths from s to t. Eigenvector centrality The eigenvector centrality measures the importance of a node, based on the idea that nodes connected to nodes with higher degrees will have more importance in the network. The degree of the node measures the number of links that connect the node to other nodes. In our study, the importance of the bus stops is decided not only by the degree but also by the bus stops connected to it. If the bus stops with a high degree do not connect to an access point, then the bus network and the MTS are separated from each other. As a result, the eigenvector centrality of the access points should have a higher value than the other bus stops. The eigenvector centrality of an access point can be estimated by the following equation:

ECi = (1

d) + d

ECj (5)

lij

where ECi is the eigenvector centrality of node i, lij is the number of edges between node i and j, and d is a damping parameter.

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