Planning for Railway Station Network Sustainability Based on Node–Place Analysis of Local Stations

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Article Planning for Railway Station Network Sustainability Based on Node–Place Analysis of Local Stations

Joon-Seok Kim and Nina Shin *

College of Business Administration, Sejong University, Seoul 05006, Korea; [email protected] * Correspondence: [email protected]

Abstract: We principally focus on evaluating the local and entire network performance of railway stations for sustainable logistics management in South Korea. Speciﬁcally, we aim to address the issue of dealing with vulnerability in logistics dependent on the degree of connectivity. To resolve this issue, we investigate (i) the current level of local railway station sustainability performance from the perspectives of the value of the station (node) and the geographical location (place), and (ii) how railway station network management can prepare for imminent internal and external risks. Integrating node–place analysis and social network analysis approaches, we demonstrate a means of assessing (i) local railway station performance by comparing how one station’s value differs from that of other stations, and (ii) overall railway network performance by measuring the degree of connectivity based on the centrality characteristics. Consequently, we recommend improvement in planning orders considering the degree of local performance and network vulnerability for railway station network sustainability.

Keywords: railway station; railway network sustainability; local station performance; railway network performance; node place analysis

Citation: Kim, J.-S.; Shin, N. Planning for Railway Station Network Sustainability Based on 1. Introduction Node–Place Analysis of Local Stations. Sustainability 2021, 13, 4778. Recently, the COVID-19 pandemic has caused sudden supply chain disruptions in https://doi.org/10.3390/su13094778 many countries. To prevent the spread of COVID-19 effectively, several countries, such as the UK, France, and China, have placed lockdowns in populated areas. These lockdowns Academic Editor: Arijit De include mass quarantines or stay-at-home orders, which can limit the activities and move- ments of people in communities. Under the pandemic lockdown, only supplying activities Received: 8 March 2021 for basic needs and services are allowed. Hence, general supply chain networks might Accepted: 22 April 2021 be disrupted around the lockdown area during a certain period [1]. Since disruptions Published: 24 April 2021 are often observed in global supply chains, supply chain vulnerability or disruption has been attracting increasing attention in recent literature. Most related research focuses on Publisher’s Note: MDPI stays neutral mitigating the detrimental effects of supply chain disruptions [2]. with regard to jurisdictional claims in There are many reasons for supply chain disruptions, including natural disasters, published maps and institutional affil- accidents, and lean production [3]. Regardless of the causes of these disruptions, chain iations. reactions over the entire supply chain cause shortfalls in materials, parts, products, and services. In particular, in the global pandemic era, these calamities can be more critical to firms that are globally operated. Due to supply chain disruptions, many firms face a critical financial crisis. Mitigation strategies or alternative solutions for supply chain Copyright: © 2021 by the authors. disruption should be developed to reduce catastrophic damage. Before we establish Licensee MDPI, Basel, Switzerland. mitigation strategies for supply chain disruption, potential risks in the existing supply This article is an open access article chain should be identified and analyzed. The supply chain network’s performance should distributed under the terms and also be evaluated. conditions of the Creative Commons Railway networks are regarded as one of the most reliable and economic logistics Attribution (CC BY) license (https:// means in many countries. They are also vulnerable to supply chain disruptions. For creativecommons.org/licenses/by/ example, the entire railway network for passenger and freight services was locked down 4.0/).

Sustainability 2021, 13, 4778. https://doi.org/10.3390/su13094778 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 4778 2 of 12

for 45 days in 2020 due to COVID-19 in India. As railways are one of the core logistical tools in India, the railway network lockdown caused massive damage to Indian supply chains [4]. Supply chains for global sourcing have also been affected because India has many production facilities for global companies. In case of disruptive events driven by risks related to supply, demand, logistics, and catastrophe, the status and degree of interconnection becomes even more important in creating a balance between vulnerability and the overall supply chain, such as railway networks’ performances [5–7]. Successful risk management strategies and vulnerability controls (i.e., resource limits or lead-time pressure) are considered a part of supply chain capabilities (i.e., ﬂexibility or velocity), ultimately leading to high supply chain resilience, or the ability to cope with and adapt and quickly respond to disruptions, as well as the ability to maintain high situational awareness [8–10]. Thus, dual aims in logistics network resilience include the minimization of vulnerability and the maximization of capability, and these are subsequently proven to create sustainable competitive advantage by adapting to and recovering from overall consequences faster than competitors [9,11–14]. As a part of disruption and risk management approaches, many studies have focused on identifying the means to integrate railway and real estate development (known as transit-oriented development) [15,16]. However, there is a limited understanding of the sustainability of railway station management. Based on the aforementioned research gap and motivation, this study principally focuses on evaluating the local and entire network performance of railway stations in Korea. This study aims to answer the following research questions: (i) What is the current level of local railway station sustainability performance from the perspective of the value of the station (node) and of the geographical location (place)? (ii) How can railway station network management prepare for imminent internal and external risks? The vulnerability arises from risks that are derived from the internal process and system, internal to the railway network, and external from the environment. To achieve a high level of transportation service excellence, the systematic examination and minimization of exposure to potential risks is a vital step from both individual station and network perspectives. This study had several implications. First, it contributes to a recent stream of transit- oriented development research by adopting node–place analysis to shed light on the case study of Korea. Second, it provides a network-level analysis of the overall railway station network’s performance based on the system attribute of centrality. Finally, this study proposes station improvement prioritization based on an integrative framework of node place and network analysis. The remainder of this paper is organized as follows: Section2 presents a literature review. We present our methodology in Section3, and Section4 assesses the railway station’s local and network performance using node–place and network analyses. Finally, Section5 discusses our ﬁndings, addresses theoretical and practical implications, and concludes with Section6 with the study’s limitations and opportunities for future research.

2. Research Background Vulnerability, in general, is a complex and multidimensional concept in different research fields, and this study is particularly interested in adopting the conceptual framework of supplier network vulnerability for building resilient railway systems. Supplier network vulnerability is defined as the susceptibility of a network to the probability and potential outcome of disruptions [9]. The nature of disruptions will continuously expose the system to a variety of risks, consequently making the entire network vulnerable regardless of how well the network is managed [11]. Thus, Wagner and Neshat [17] proposed how vulnerability should be measured and analyzed regarding exposure to environmental, network, and organizational risks. To quantify and measure vulnerability, Christopher and Peck [11] identified that the fundamental issue of supply chain vulnerability is due to treating the chain as a linear Sustainability 2021, 13, x FOR PEER REVIEW 3 of 13

gardless of how well the network is managed [11]. Thus, Wagner and Neshat [17] proposed how vulnerability should be measured and analyzed regarding exposure to envi- Sustainability 2021, 13, 4778 ronmental, network, and organizational risks. 3 of 12 To quantify and measure vulnerability, Christopher and Peck [11] identified that the fundamental issue of supply chain vulnerability is due to treating the chain as a linear structure,structure, ratherrather thanthan aa network network structure. structure. Consequently,Consequently, they they established established three three types types of of risksrisks thatthat cancan bebe interpreted interpreted inin the the following following wayway fromfrom the the railway railway network network perspective: perspective: (1)(1) internal internal risk risk to to the the station station regarding regarding process process and and control, control, (2) (2) external external risk risk to to the the station station andand internal internal risk risk to to the the railway railway network network regarding regarding passenger passenger flow, flow, and (3)and risk (3) that risk is that exter- is nalexternal to the railwayto the railway network, network, such as environmentalsuch as environmental risk. Moreover, risk. Moreover, Erol et al. [Erol18] suggested et al. [18] thesuggested following the calculationfollowing calculation of vulnerability: of vulnerability: the level ofthe railway level of network railway vulnerabilitynetwork vulner- is definedability is as defined the likelihood as the likelihood of a disruptive of a disruptive event and theevent intensity and the of intensity its consequence. of its conse- To mitigatequence. theTo mitigate effect of the disruption, effect of disruption, social network social analysis network has analysis gained has significant gained significant attention asattention a methodology as a methodology for measuring for measuring an individual an in firm’sdividual role firm’s in the role structure in the structure network. net- In thework. railway In the network railway context,network the context, methodology the meth couldodology aid could in investigating aid in investigating the role ofthe each role railwayof each stationrailway in station the network. in the Thenetwork. two primary The two social primary network social analysis network metrics analysis are degreemetrics centralityare degree and centrality betweenness and betweenness centrality. centrality. IfIf thethe node provides provides a a higher higher number number of ofconnections connections to different to different nodes, nodes, then then the node the nodeis considered is considered to have to a have higha degree high degree of centralit of centrality.y. A node Athat node has thata high has level a high of centrality level of centralityis often visualized is often visualized with a larger with size, a larger thus size, becoming thus becoming more visible more in visible the map in theof a map network of a network[19–21]. [When19–21 ]the. When other the nodes other are nodes dependent are dependent on a particular on a particular node to node connect to connect to the rest to theof the rest network, of the network, this node this is node considered is considered to have to a have high a betweenness high betweenness centrality centrality [19,22]. [19 High,22]. Highbetweenness betweenness centrality centrality also indicates also indicates the level the of level intermediary of intermediary or brokerage or brokerage roles that roles the thatnode the plays node within plays the within network. the network. If a node If with a node high with betweenness high betweenness centrality centrality fails to provide fails to providea stable aconnection, stable connection, the network the network will be exposed will be exposed to overall to overalldisconnection. disconnection.

3.3. MethodologyMethodology ToTo consider consider both both local- local- and and network-level network-level analyses analyses of of railway railway stations, stations, we we integrate integrate node–placenode–place analysis analysis and and network network analysis, analysis, as as shown shown below below (Figure (Figure1 ).1).

FigureFigure 1. 1.Research Research frameworkframework for for an an integrative integrative local local and and network network performance performance analysis. analysis.

ForFor the the local local station station sustainability sustainability analysis, analys weis, we performed performed node node and placeand place analyses analyses [23] based[23] based on the on indicatorsthe indicators listed listed in Table in Table1. The 1. The node node index index is deﬁnedis defined as as the the quantitative quantitative measurementmeasurement ofof stationstation values, values, such such as as the the accessibility accessibility of of stations stations and and the the availability availability of of traintrain connections. connections. In In this this study, study, the the node node index index value value is assignedis assigned based based on theon followingthe following six datasix data sets: sets: number number of train of train arrivals arrivals and and departures, departures, number number of train of train connections connections departing depart- froming from a station, a station, number number of nearby of nearby subway subway stations, stations, number number of available of available bus stations bus stations within approximately 300 m, number of nearby buses, and number of available parking spaces. Sustainability 2021, 13, 4778 4 of 12

Table 1. Description and measurement of node and place attributes.

Description Measurements of Indicators Average number of arriving and departing trains Frequency of train services N 1 at the station Average number of train connections that are Number of train connections served N 2 made at the station Number of subway stations that are within 300 Number of nearby subway stations N 3 m of the train station Number of bus stations that are within 300 m of Number of nearby bus stations N 4 the train station Number of busses that stop at train station main Number of available bus services N 5 bus stop Number of ofﬁcial parking spaces available at Number of available parking spaces N 6 the train station

Number of residents P1 Number of residents in the province Number of employees in the eight Number of employees P 2 economic sectors Standard deviation of number of employees for Degree of multifunctionality P3 each economic sector for n sectors at station j where n = 1, . . . , 8 and j = 1, . . . , 15

Place index is deﬁned as the quantitative measurement of activities in a place, such as the size of a population, labor activities, and tour activities. Place index is measured based on two datasets: the number of households in the district and the approximation of employees in nine sectors in the district. The data collection details are listed below, and the ﬁnalized data table is given in Table2.

Table 2. Collected data of node and place attributes.

Node P1 P2 P3 N1 N2 N3 N4 N5 N6 Station 1 (Seoul) 228,865 5,379,000 1.00 99 7 4 14 182 1337 Station 2 (Yeongdeungpo) 369,024 5,379,000 1.00 4 6 2 10 126 1187 Station 3 (Suwon) 197,291 6,991,000 0.81 8 6 2 19 145 1353 Station 4 (Gwangmyeong) 335,112 6,991,000 0.81 72 6 1 8 66 1769 Station 5 (CheonanAsan) 306,452 1,336,000 0.68 58 7 1 7 75 524 Station 6 (Osong) 254,022 884,000 0.73 53 7 - 4 25 1821 Station 7 (Daejeon) 233,240 799,000 0.95 112 6 1 13 50 217 Station 8 (Gimcheon) 143,095 1,491,000 0.66 34 7 - 5 74 448 Station 9 (Dongdaegu) 351,218 1,284,000 0.80 109 6 1 8 122 322 Station 10 (Singyeongju) 258,280 1,491,000 0.66 33 7 - 3 32 598 Station 11 (Ulsan) 218,853 606,000 0.68 1 7 - 5 40 1065 Station 12 (Miryang) 108,219 1,775,000 0.67 14 8 - 5 73 105 Station 13 (Gupo) 308,067 1,758,000 0.86 14 8 1 7 46 64 Station 14 (Busan) 89,079 1,758,000 0.86 107 6 1 11 69 529

• Frequency of train services (N1): The ofﬁcial Korail site provides a timetable for KTX and other train services. Based on the Gyeongbu line timetable, we extracted the average number of train arrivals and departures at each station per day. • Number of connections served (N2): Similarly, the average number of train connections provided by each train station per day was measured using the Gyeongbu line timetable data from the ofﬁcial Korail site. • Number of nearby subway stations (N3): Subway stations provide timely transportation services that conveniently complement the train station usage experience. While Sustainability 2021, 13, 4778 5 of 12

some train stations benefit from the closeness of one or even three linked subway stations using the same metro card (mostly divided by a single gate), some stations are unfortunately faced with limited accessibility because of the unavailability of subway services. • Number of available bus stations within 300 m (N4): The bus also plays a significant role in providing accessibility to railway services. The bus stations within approximately 300 m were measured and counted. • Number of buses (N5): Frequent bus services near train stations are often highly valued, as they are key to easy transferability. • Number of parking spaces (N6): The number of parking spaces was collected from the official Korail site. Parking spaces represent readiness to assist service users, who are interested in half-day or even longer travel, in using train stations. The availability and reliability of parking spaces are vital for enhancing the overall quality of train stations. • Number of residents in the area (P1): The number of residents within the city where train stations are located was measured based on Korean Statistical Information Service data. With the exception of Station 4 (Gwangmyeong), Station 5 (CheonanAsan), Station 8 (Gimcheon), Station 10 (Singyeongju), and Station 12 (Miryang), residents in the cities where train stations are located were measured. For others, due to limited data availability, the number of residents in the provinces is used instead. • Number of employees in the corresponding province (P2): Bertolini [24] analyzed four economic clusters: (i) retail, hotel, and catering; (ii) education, health, and culture; (iii) administration and services; and (iv) industry and distribution. In this study, we utilized eight economic clusters: (1) agricultural forestry and fisheries; (2) mining and manufacturing; (3) manufacturing; (4) social overhead capital and other services; (5) construction; (6) wholesale, retail, and accommodation; and (8) electricity, transportation, telecommunication, and finance. The data were obtained from the Korean Statistical Information Service. Our datasets represent provinces or cities, depending on the availability of the data. • Degree of multifunctionality (P3): While most stations in Korea primarily serve the purpose of providing train access to consumers, some of the stations also provide impeccable convenience for both consumers and retail stores. For example, Station 2 (Yeongdeungpo) is connected to a large department store and its underground shopping mall. Not only do train stations enhance functional quality from the transportation perspective, but they also demonstrate significant contributions to happiness through their facilitation of other social activities that meet consumers’ needs. Thus, the degree of multifunctionality allows for the integration of both elements in the assessment of place value.

4. Analysis of Local Station Performance Assessment In this section, we perform two analyses: correlation and node–place analyses. While correlation analysis (Table3) is expected to provide a quantitative understanding of the relationship between indices, node–place analysis is useful in understanding the level of sustainability of local stations. The number of nearby subway stations (N3) and degree of multifunctionality (P3) showed the highest correlation index, with a p-value less than 0.001. This indicates that stations with various functions besides train services are also connected with a higher number of subway stations, enabling access to a variety of consumers via efﬁcient and convenient accessibility. Moreover, the numbers of subway stations (N3), available bus stations (N4), and buses available near the station (N5) are highly correlated with the number of employees in the area. This may be an intuitive ﬁnding, as for the higher number of employees, a higher number of transportations may be necessary. Lastly, the number of nearby buses (N5) is also positively correlated with the degree of multifunctionality (P3). This indicates that the common objective of transportation to a station may be expanded Sustainability 2021, 13, 4778 6 of 12

beyond train services. Service users often utilize buses to reach train stations for other functions, such as retail and dining activities. In Table4 and Figure2, the analysis results of the node–place analysis are shown.

Table 3. Correlation table of node and place indices.

P1 P2 P3 Number of Residents Number of Employees Degree of Multifunctionality

N1 0.001 −0.109 0.313 Frequency of train services (0.998) (0.711) (0.277) N 2 −0.226 −0.398 −0.345 Number of train connections (0.436) (0.159) (0.227) served N 3 0.214 0.656 * 0.790 ** Number of nearby subway (0.462) (0.011) (0.001) station N 4 −0.062 0.617 * 0.624 * Number of nearby bus (0.832) (0.019) (0.017) stations N 5 0.100 0.648 * 0.555 * Number of available bus (0.734) (0.012) (0.039) services N 6 0.249 0.603 * 0.153 Number of available parking (0.392) (0.022) (0.602) spaces ** signiﬁcant at 0.01; * signiﬁcant at 0.05.

Table 4. Values of place and node for the stations.

Station Value of Place (xip) Value of Node (yin) Station 1 (Seoul) 0.80 0.87 Station 2 (Yeongdeungpo) 0.92 0.64 Station 3 (Suwon) 0.78 0.53 Station 4 (Gwangmyeong) 0.91 0.51 Station 5 (CheonanAsan) 0.57 0.35 Station 6 (Osong) 0.51 0.34 Station 7 (Daejeon) 0.56 0.45 Station 8 (Gimcheon) 0.42 0.31 Station 9 (Dongdaegu) 0.65 0.57 Station 10 (Singyeongju) 0.53 0.53 Station 11 (Ulsan) 0.45 0.31 Station 12 (Miryang) 0.41 0.33 Station 13 (Gupo) 0.65 0.54 Station 14 (Busan) 0.45 0.45

Based on the node–place analysis, the well-balanced stations are Station 9 (Dong- daegu), Station 13 (Gupo), and Station 3 (Suwon), as they fall right on the diagonal line and are in between the dependency and stress regions. Both the sustained node and place functions are well aligned with the overall usage of the train station. While Station 1 (Seoul) may appear to be the most efﬁcient station for utilizing the potential of both node and place functions, one must be careful as Station 1 (Seoul) is equally exposed to vulnerabilities. For example, Station 1 (Seoul) is exposed to a variety of conﬂicts that may arise from train services, retail services, and social activities that are offered. Noticeably, dining and retail spaces in Station 1 (Seoul) are extremely small, and many mini food stands can be observed, unlike other stations with comfortable seats. Moreover, service users often lack spaces for waiting at the station and are forced to enter coffee shops to sit down and wait. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13

Sustainability 2021, 13, 4778 and are in between the dependency and stress regions. Both the sustained node and 7place of 12 functions are well aligned with the overall usage of the train station.

Figure 2.2. Sustainability level based onon placeplace valuevalue (x-axis)(x-axis) andand nodenode valuevalue (y-axis).(y-axis).

However,While Station Station 1 (Seoul) 8 (Gimcheon), may appear Station to be 12 the (Miryang), most efficient Station station 11 (Ulsan), for utilizing Station the 14 (Busan),potential Station of both 6 (Osong),node and Station place 5functions, (CheonanAsan), one must and be Station careful 7 (Daejeon)as Station are1 (Seoul) exposed is toequally dependency exposed issues. to vulnerabilities. These stations For show example, a lack of Station sustainability 1 (Seoul) that is willexposed ultimately to a variety cause relianceof conflicts on eitherthat may government arise from assistance train services, or larger retail train services, stations and that social provide activities services. that These are stationsoffered. needNoticeably, to improve dining the and overall retail usage spaces of in space Station through 1 (Seoul) the appropriateare extremely allocation small, and of spacemany tomini commercial food stands businesses, can be therebyobserved, enhancing unlike other the overall stations functionality with comfortable of the station. seats. Moreover,Station service 10 (Singyeongju), users often lack Station spaces 2 (Yeongdeungpo), for waiting at the and station Station and 4 are (Gwangmyeong) forced to enter havecoffee the shops most to unbalancedsit down and node wait. and place values. While Station 10 (Singyeongju) has a high nodeHowever, value, Station the interactivities 8 (Gimcheon), at the Station stations 12 are(Miryang), limited. Station Despite 11 the (Ulsan), high potential Station for 14 business–consumer(Busan), Station 6 (Osong), or social Station interactions 5 (Cheon thatanAsan), are ready and to unfold Station at 7 the (Daejeon) station, theare station’sexposed functionsto dependency are not issues. ready toThese meet stations the needs show of unavailable a lack of sustainability retail or dining that opportunities. will ultimately On acause similar reliance note, Stationon either 2 (Yeongdeungpo) government assistance and Station or larger 4 (Gwangmyeong) train stations providethat provide excessive ser- opportunitiesvices. These stations for human need interactions to improve whenthe ov thereerall usage is a low of availabilityspace through of trainsthe appropriate and train connectionallocation of services. space to commercial businesses, thereby enhancing the overall functionality 5.of Analysisthe station. of Network Performance Assessment Station 10 (Singyeongju), Station 2 (Yeongdeungpo), and Station 4 (Gwangmyeong) While connections among train stations may benefit each by bringing one service have the most unbalanced node and place values. While Station 10 (Singyeongju) has a user from one station to another, it also exposes each connected train station to other’s high node value, the interactivities at the stations are limited. Despite the high potential vulnerabilities. For example, passenger A needs to begin his trip from Station 14 (Busan) to for business–consumer or social interactions that are ready to unfold at the station, the Station 1 (Seoul). On the way, the passenger would like to stop at Station 7 (Daejeon) to buy station’s functions are not ready to meet the needs of unavailable retail or dining oppor- the famous local bread. The passenger would most likely choose to take KTX rather than tunities. On a similar note, Station 2 (Yeongdeungpo) and Station 4 (Gwangmyeong) pro- buses or planes for travel convenience. Through this decision, Station 14 (Busan), Station 7 vide excessive opportunities for human interactions when there is a low availability of (Daejeon), and Station 1 (Seoul) benefit from passenger A’s service usage. However, this trains and train connection services. also implies that if any of these three stations face technical failures that affect passenger A’s travel plan, all three stations will be exposed to penalties regardless of the source of the 5. Analysis of Network Performance Assessment problem. Thus, the consideration of each railway station’s performance is vital for overall networkWhile management. connections among train stations may benefit each by bringing one service user Asfrom shown one station in Figure to 3another,, Station it 12 also (Miryang) exposes haseach direct connected connections train station with Stationto other’s 9 (Dongdaegu)vulnerabilities. and For Station example, 13 (Gupo).passenger Comparatively, A needs to begin Station his trip 1 (Seoul) from Station maintains 14 (Busan) a high numberto Station of 1 direct (Seoul). connections On the way, with the Station passenger 2 (Yeongdeungpo), would like to stop Station at Station 4 (Gwangmyeong), 7 (Daejeon) to etc.buy Stationthe famous 12 (Miryang) local bread. and The Station passenger 1 (Seoul) would may bemost considered likely choose as having to take low KTX and rather high degreesthan buses of connectivity, or planes for respectively. travel convenience. The degree Through of station this connectivity decision, Station is defined 14 (Busan), by the number of connections made from or to the station [25]. Sustainability 2021, 13, x FOR PEER REVIEW 8 of 13

Sustainability 2021, 13, x FOR PEER REVIEW 8 of 13

Station 7 (Daejeon), and Station 1 (Seoul) benefit from passenger A’s service usage. How- ever,Station this 7 also (Daejeon), implies and that Station if any 1of (Seoul) these three benefit stations from passenger face technical A’s servicefailures usage. that affect How- passengerever, this A’s also travel implies plan, that all if three any ofstations these willthree be stations exposed face to technicalpenalties failuresregardless that of affect the sourcepassenger of the A’s problem. travel plan, Thus, all the three consideratio stations willn of be each exposed railway to penaltiesstation’s performanceregardless of isthe vitalsource for overallof the problem.network management.Thus, the consideratio n of each railway station’s performance is vitalAs for shown overall in network Figure 3,management. Station 12 (Mirya ng) has direct connections with Station 9 (Dongdaegu)As shown and in Station Figure 13 3, (Gupo). Station Comparat12 (Miryaively,ng) has Station direct 1 connections(Seoul) maintains with Station a high 9 number(Dongdaegu) of direct and connections Station 13 with (Gupo). Station Comparat 2 (Yeongdeungpo),ively, Station Station 1 (Seoul) 4 (Gwangmyeong), maintains a high Sustainability 2021, 13, 4778 etc.number Station of 12 direct (Miryang) connections and Station with Station1 (Seoul 2) (Yeongdeungpo),may be considered Station as having 4 (Gwangmyeong), low and high 8 of 12 degreesetc. Station of connectivity, 12 (Miryang) respectively. and Station The 1 (Seoul degre) emay of station be considered connectivity as having is defined low and by thehigh numberdegrees of of connections connectivity, made respectively. from or to The the degrestatione of[25]. station connectivity is defined by the number of connections made from or to the station [25].

Figure 3. Example of low degree (left) and high degree (right) of station connectivity. Figure 3. Example of low degree (left) and high degree (right) of station connectivity. Figure 3. Example of low degree (left) and high degree (right) of station connectivity. Figure4 demonstrates two exemplary cases: Station 3 (Suwon) and Station 7 (Daejeon). Figure 4 demonstrates two exemplary cases: Station 3 (Suwon) and Station 7 (Dae- jeon).Station StationFigure 3 (Suwon) 43 demonstrates(Suwon) provides provides two connections exemplary connection tocases:s Stationto Station Station 7 (Daejeon), 7 3 (Daejeon), (Suwon) Station and Station Station 9 (Dongdaegu),9 (Dong- 7 (Dae- and daegu),Stationjeon). Stationand 14 (Busan)Station 3 (Suwon) 14 from (Busan) Seoulprovides from and connection Seoul Station and 2s (Yeongdeungpo). Stationto Station 2 (Yeongdeungpo). 7 (Daejeon), On the Station one On hand,the9 (Dong- one passengers hand,woulddaegu), passengers be and able Station towould reach 14 be(Busan) any able destination tofrom reach Seoul any without anddestination Station having without2 (Yeongdeungpo). to go having through to go On Station through the one 3 (Suwon). StationWehand, can 3passengers conclude(Suwon). We thatwould can Station concludebe able 3 (Suwon)to that reach Station any holds destination3 (Suwon) a very low holds without betweenness a very having low betweennessto centrality go through position in centralitytheStation network. 3 position (Suwon). On in theWe the other cannetwork. conclude hand, On Stationthethat ot Stationher 7 hand, (Daejeon) 3 (Suwon) Station noticeably holds7 (Daejeon) a very maintains noticeably low betweenness a main- highly central tainsposition,centrality a highly asposition do central Seoul, in the position, Station network. 2as On (Yeongdeungpo), do the Seoul, other hand,Station Station Station 2 (Yeongdeungpo), 7 (Daejeon) 4 (Gwangmyeong), noticeably Station main- and4 Station (Gwangmyeong),5tains (CheonanAsan). a highly centraland Station The position, betweenness 5 (CheonanAsan). as do centralitySeoul, The Station betweenness of a station2 (Yeongdeungpo), centrality is deﬁned of a station asStation the is number 4 of definedconnections(Gwangmyeong), as the madenumber and through of Station connections the 5 (C station.heonanAsan). made Simply throug Theh put, the betweenness itstation. quantiﬁes Simply centrality the put, effect itof quantifies a of station a node is in terms defined as the number of connections made through the station. Simply put, it quantifies theof effect network of a managementnode in terms of [26 network]. management [26]. the effect of a node in terms of network management [26].

Figure 4. Example of low betweenness centrality (left) and high betweenness centrality (right) of stations.FigureFigure 4. 4. ExampleExample of low of lowbetweenness betweenness centrality centrality (left) and (left high) andbetweenness high betweenness centrality (right centrality) of (right) ofstations. stations. Figure 5 illustrates that the rail network is based on the degree of centrality and be- tweennessFigure centrality. 5 illustrates Station that that the the 1rail rail(Seoul), network network Stationis based is based on4 the(Gwangmyeong), on degree the degree of centrality ofStation centrality and be-5 and be- (CheonanAsan),tweennesstweenness centrality.centrality. Station Station9 (Dongdaegu),Station 1 (Seoul),1 (Seoul), and Station Station Station 4 14 (Gwangmyeong), (Busan) 4 (Gwangmyeong), connect to Station a high Station 5number (CheonanAsan), 5 ofStation(CheonanAsan), stations, 9 (Dongdaegu),while Station Station 89 and(Gimcheon),(Dongdaegu), Station 14Stat and (Busan)ion Station 10 (Singyeongju), connect 14 (Busan) to a connect highStation number to11 a (Ulsan), high of number stations, and while StationStationof stations, 12 8 (Miryang) (Gimcheon), while Station show Station a8 low(Gimcheon), degree 10 (Singyeongju), of Stat centrality.ion 10 (Singyeongju), Station Interestingly, 11 (Ulsan), onlyStation Station and 11 Station(Ulsan), 1 (Seoul), 12 and (Miryang) showStation a 12 low (Miryang) degree show of centrality. a low degree Interestingly, of centrality. onlyInterestingly, Station only 1 (Seoul), Station Station1 (Seoul), 9 (Dong- daegu), and Station 14 (Busan) show high betweenness centrality, while others show low betweenness centrality. The results and potential indications are summarized in Table5.

Table 5. Degree and betweenness centrality comparison matrix.

Low Degree High Degree Station 8 (Gimcheon) Station 4 (Gwangmyeong) Station 10 (Singyeongju) Low Station 5 (CheonanAsan) Station 11 (Ulsan) betweenness Station 7 (Daejeon) Station 12 (Miryang) centrality XConnections are redundant, passengers may bypass XLeast exposed to vulnerability No stations Station 1 (Seoul) High Station 9 (Dongdaegu) Station 14 (Busan) betweenness Critical stations that enable network ﬂow. Highly centrality X exposed to vulnerability XMost important stations to vulnerability Sustainability 2021, 13, x FOR PEER REVIEW 9 of 13

Sustainability 2021, 13Station, 4778 9 (Dongdaegu), and Station 14 (Busan) show high betweenness centrality, while 9 of 12 others show low betweenness centrality. The results and potential indications are summarized in Table 5.

Figure 5. FigureRailway 5. stationRailway network station modelnetwork based model on based (A) the on degree (A) the of degree centrality of centrality and (B) the and betweenness (B) the be- centrality. tweenness centrality. 6. Discussion Table 5. Degree and betweenness centrality comparison matrix. For sustainable railway station network management, we proposed integrating two decision Low Degree perspectives: local and network performance. High Degree First, local performance is decided Stationbased 8 (Gimcheon) on the陈 assessment of both the nodeStation and 4 (Gwangmyeong) place value of陈 the station. The node Station 10 (Singyeongju)陈 Station 5 (CheonanAsan)陈 Low陈 value assessment determines the functional performance of the station, while place value Station 11 (Ulsan)陈 Station 7 (Daejeon)陈 betweenness assessment determines the ultimate value of the station’s service offerings as a place and Station 12 (Miryang)陈 centrality its location. Such local performance assessment is expected to help railway station network management by comparing how oneConnections station’s are value redundant, differs passengers from that may of other stations.  Least Second,exposed to network vulnerability performance is determined basedbypass on the intensity of involvement in the No stations陈 Station 1 (Seoul)陈 High陈 network structure, such as the degree and betweenness centrality of the station. The degree Station 9 (Dongdaegu)陈 of station accessibility helps in understanding the accessibility level of each station, while Station 14 (Busan)陈 betweenness陈 the betweenness centrality quantitatively represents the intensity of the brokerage role of  Critical stations that enable network flow. centrality the station. Highly exposedThe to vulnerability results of the local- and network-levelMost important performance stations to vulnerability are listed in Table6. Pri- marily, the sustainability of local stations must be achieved and valued prior to overall 6. Discussionnetwork improvement. Thus, we first ranked stations based on the local performance For sustainableassessment, railway and station then assigned network anma improvementnagement, we order proposed based integrating on each station’s two exposure decision perspectives:level to network local and vulnerability. network performance. First, local performance is decided based on the assessmentOne of of the both imperative the node managementand place value concerns of the station. in terms The of node resilience value management assessment determinesinvolves distinguishing the functional aperformance specific network of the station’s station, (orwhile a firm’s)place value poor andas- potentially sessment determinesexponentially the ultimate worsening value performance. of the station’s For service example, offerings Blos et as al. a [27 place] identified and its poor-quality location. Suchgoods local asperformance one major problem assessment forthe is expected electronics to industry,help railway in which station defective network parts are found later in production, rather than in an earlier sourcing process. Adopting this view of domino effects, the identification of a critical path or a logistics route is useful in highlighting the most threatening fatal path within the network. This approach is typical when specific Sustainability 2021, 13, 4778 10 of 12

stations (or suppliers) are represented as a network in which the ﬁrms belong to a connected path exposed to a set of uncertain events.

Table 6. Example of improvement order based on local and network performance assessment.

Network Recommended Improvement Local Performance Station Rank Vulnerability Order for Sustainable Railway Assessment Exposure Station Network Management Station 8 (Gimcheon) Dependency 1 Low 3 Station 11 (Ulsan) Dependency 1 Low 3 Station 12 (Miryang) Dependency 1 Low 3 Station 1 (Seoul) Stressed 1 High 1 Station 4 (Gwangmyeong) Stressed 1 Mixed 2 Station 2 (Yeongdeungpo) Unbalanced 2 Mixed 2 Station 3 (Suwon) Unbalanced 2 Mixed 5 Station 6 (Osong) Unbalanced 2 Mixed 2 Station 7 (Daejeon) Unbalanced 2 Mixed 2 Station 9 (Dongdaegu) Unbalanced 2 High 4 Station 13 (Gupo) Unbalanced 2 Mixed 5 Station 14 (Busan) Unbalanced 2 High 1 Station 5 (CheonanAsan) Balanced - Mixed 2 Station 10 (Singyeongju) Balanced - Low 3

Based on a visual identiﬁcation of critical railway stations in Figure6, the recommended pathways for balanced and sustained railway station management are listed in Table6. Station 1 (Seoul) should consider lowering the value of its node, while Station 2 (Yeongdeungpo) and Station 4 (Gwangmyeong) should reconsider their place value. Most importantly, Station 12 (Miryang), Station 8 (Gimcheon), Station 11 (Ulsan), Station 10 (Singyeongju), Station 7 (Daejeon), Station 6 (Osong), and Station 5 (CheonanAsan) Sustainability 2021, 13, x FOR PEER REVIEWshould undergo signiﬁcant improvements in planning so as to improve both node11 of and 13

place values.

Figure 6. Improvement guideline for reaching railway station network sustainability. Figure 6. Improvement guideline for reaching railway station network sustainability. 7. Conclusions 7. Conclusions Since 2019, global supply chains have suffered because of the COVID-19 pandemic. ThisSince has attracted 2019, global growing supply interest chains in have supply suffered chain vulnerabilitybecause of the and COVID-19 disruption. pandemic. World- This has attracted growing interest in supply chain vulnerability and disruption. World- wide companies simultaneously face catastrophic disasters, and many upstream and downstream supply chains have been stopped or delayed. Various supply chain networks should be prepared via mitigating or alternative solutions to prevent or reduce massive damage from unpredicted disruptions. This study makes two main contributions to railway station network management as related to supply chain vulnerability. First, as a parallel effort to various attempts at opti- mizing networks through efficient allocations of the train and financial resources, this study highlights the importance of both local and network sustainability management practices. Based on the resilience practice suggested by Christopher and Peck [11], railway station network management could benefit from the following process: proactive preparation against both internal and external risks. Similarly, we propose the following approach to planning for railway station network sustainability: (i) integrate resilience practice rather than treating it as a separate effort; (ii) coordinate a high level of collaboration among the stations; (iii) collectively understand the value of agility, and finally, create a risk management culture within the network to enhance railway network resilience continuously. Second, from a managerial perspective, both local station management and entire railway station network management are equally important. While local station managers should continuously observe and utilize local station data to achieve an overall balance of node and place values, network managers should identify the vulnerabilities of the network, and simulate possible size and timing effects on the entire network derived from a single station. This study has several limitations. First, node–place analysis can be improved using more extended attributes and regional data (versus city data), as suggested in other studies [15,16,28,29]. Second, for an additional level of network analysis, the arrival time and travel time may be considered for nodes and edges, respectively. However, we did not include such data, as such an analysis would go beyond the scope of this study. Lastly, structured interviews with rail network management could aid future studies in integrating both network and rail business perspectives. Sustainability 2021, 13, 4778 11 of 12

wide companies simultaneously face catastrophic disasters, and many upstream and downstream supply chains have been stopped or delayed. Various supply chain networks should be prepared via mitigating or alternative solutions to prevent or reduce massive damage from unpredicted disruptions. This study makes two main contributions to railway station network management as related to supply chain vulnerability. First, as a parallel effort to various attempts at opti- mizing networks through efficient allocations of the train and financial resources, this study highlights the importance of both local and network sustainability management practices. Based on the resilience practice suggested by Christopher and Peck [11], railway station network management could benefit from the following process: proactive preparation against both internal and external risks. Similarly, we propose the following approach to planning for railway station network sustainability: (i) integrate resilience practice rather than treating it as a separate effort; (ii) coordinate a high level of collaboration among the stations; (iii) collectively understand the value of agility, and finally, create a risk management culture within the network to enhance railway network resilience continuously. Second, from a managerial perspective, both local station management and entire railway station network management are equally important. While local station managers should continuously observe and utilize local station data to achieve an overall balance of node and place values, network managers should identify the vulnerabilities of the network, and simulate possible size and timing effects on the entire network derived from a single station. This study has several limitations. First, node–place analysis can be improved using more extended attributes and regional data (versus city data), as suggested in other studies [15,16,28,29]. Second, for an additional level of network analysis, the arrival time and travel time may be considered for nodes and edges, respectively. However, we did not include such data, as such an analysis would go beyond the scope of this study. Lastly, structured interviews with rail network management could aid future studies in integrating both network and rail business perspectives.

Author Contributions: All authors contributed to the conceptualization, methodology, validation, resources, and writing of the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5A8045955). Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: http://www.letskorail.com, accessed on 23 April 2021. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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