Optimization of high-speed railway station location selection based on accessibility and environmental impact

Roy, Sandeepan; Maji, Avijit Working Paper Optimization of high-speed railway station location selection based on accessibility and environmental impact ADBI Working Paper Series, No. 953 Provided in Cooperation with: Asian Development Bank Institute (ADBI), Tokyo Suggested Citation: Roy, Sandeepan; Maji, Avijit (2019) : Optimization of high-speed railway station location selection based on accessibility and environmental impact, ADBI Working Paper Series, No. 953, Asian Development Bank Institute (ADBI), Tokyo This Version is available at: https://hdl.handle.net/10419/222720 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/igo/ ADBI Working Paper Series OPTIMIZATION OF HIGH-SPEED RAILWAY STATION LOCATION SELECTION BASED ON ACCESSIBILITY AND ENVIRONMENTAL IMPACT Sandeepan Roy and Avijit Maji No. 953 May 2019 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI’s working papers reflect initial ideas on a topic and are posted online for discussion. Some working papers may develop into other forms of publication. Suggested citation: Roy, S. and A. Maji. 2019. Optimization of High-Speed Railway Station Location Selection Based on Accessibility and Environmental Impact. ADBI Working Paper 953. Tokyo: Asian Development Bank Institute. Available: https://www.adb.org/publications/high-speed-railwaystation-location-accessibility-environmental-impact Please contact the authors for information about this paper. Email: [email protected], [email protected] Sandeepan Roy is a PhD student at the Department of Civil Engineering of the Indian Institute of Technology Bombay in India. Avijit Maji is an associate professor at the Department of Civil Engineering of the Indian Institute of Technology Bombay, India. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Working papers are subject to formal revision and correction before they are finalized and considered published. Asian Development Bank Institute Kasumigaseki Building, 8th Floor 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, Japan Tel: +81-3-3593-5500 Fax: +81-3-3593-5571 URL: www.adbi.org E-mail: [email protected] © 2019 Asian Development Bank Institute ADBI Working Paper 953 Roy and Maji Abstract High speed railway (HSR) planners aim to select locations that optimize the overall utility or benefit of HSR stations by satisfying various desirable requirements. Among other factors, accessibility and environmental impact are important considerations for HSR station location selection. The desirable requirements of these two factors include improved access to, and intermodal integration with, existing transportation facilities and services (like airports, train stations, and bus stops); avoidance of environmentally sensitive areas (such as water bodies, wetlands, and forest) and land with higher right-of-way costs; and accommodation of strategic necessities (for example, proximity to city centers and socioeconomic development hubs). This study quantifies the overall utility of an HSR station by analyzing the extent to which a location satisfies these desirable requirements. For this, suitable utility functions were developed and evaluated. To obtain individual utility scores, appropriate weights were assigned based on relative importance. The overall utility of a location was then estimated as the weighted summation of these utility scores. A GIS-based analytical framework was specifically developed for geo-processing, mapping, and visualization of the geospatial data analysis and result representation. This utility-based quantification and identification process would be useful to planners in assessing an area and determining the most suitable station locations for an HSR project. The proposed model was used to identify the potential station locations along the Mumbai-Ahmedabad HSR corridor in India and to compare the obtained results with the planned locations of the project. Keywords: high-speed rail stations, geographic information systems, environmental impact, accessibility, utility functions JEL Classification: L92, R11, R41, R58 ADBI Working Paper 953 Roy and Maji Contents 1. INTRODUCTION ......................................................................................................... 1 2. LITERATURE REVIEW ............................................................................................... 1 3. DESIRABLE REQUIREMENTS AND CONSTRAINTS FOR HSR STATION LOCATIONS .............................................................................. 2 4. PROBLEM FORMULATION ........................................................................................ 4 5. METHODOLOGY ........................................................................................................ 4 6. CASE STUDY ............................................................................................................. 6 7. RESULTS .................................................................................................................... 8 8. CONCLUSION .......................................................................................................... 12 REFERENCES ..................................................................................................................... 14 ADBI Working Paper 953 Roy and Maji 1 1. INTRODUCTION High speed railways (HSR) are the rail services that operate at speeds in excess of 200 km/h, on exclusive or grade-separated rights-of-way (European Union 1996). They provide short and competitive travel time between strategically important locations. HSR planners identify regions or major cities that have adequate GDP, population, and ridership potential, and satisfy interstation distance and travel-time requirements (Takeshita 2012). An HSR line is developed by identifying appropriate locations for terminal and intermediate stations, and connecting them with a suitable alignment. Determination of station locations is not always a straightforward problem. In addition to optimizing ridership and travel time, planners aim to select locations that optimize the overall utility or benefit of the stations for the adjacent environment and population. This is achieved by satisfying various desirable requirements such as: improved access to, and intermodal integration with, existing transportation facilities and services (airports, train stations, bus stops, etc.); avoiding environmentally sensitive land parcels (water bodies, wetlands, forest, etc.) and land with higher right–of-way costs; and meeting strategic necessities such as proximity to city centers and socioeconomic development hubs. The existing station location identification process is manual in nature and carried out during the planning stage of HSR development. It involves identification of locations by overlaying maps of the study area with relevant information regarding locations of the transportation facilities, residential population distribution, land-use details, geographic features, etc. This approach indirectly factors in certain desirable requirements but cannot always guarantee a station location with maximum utility because not all feasible locations will be evaluated and no exact quantification of utility is available. Geographic information systems (GIS), with their advanced mapping, geo-processing, and visualization capabilities, could be used in the spatial analysis of potential station locations. The GIS data, in the form of land use and land-cover maps, property-data maps, and maps showing other facility locations within the study area, could be utilized in the process. A model that quantifies the desirable requirements and presents as a utility score would greatly benefit HSR planners by helping them identify optimal station location within potential HSR regions. Hence, the objective of this paper is to develop a GIS-based HSR station location optimization model. For this purpose, an analytical model is specifically developed to identify a pool of candidate locations for HSR stations by quantifying the desirable requirements. Overall, it helps in identifying the station location with the highest utility. The desirable requirements of HSR station locations are estimated using utility functions and integrated into the GIS-based analytical framework. A real-world case study is presented to demonstrate the efficacy of the proposed model. 2. LITERATURE REVIEW Station location identification is a facility location decision or analysis problem. The aim of this type of problem is to find the optimal feasible location for a facility that satisfies various predetermined selection criteria. The easiest way to identify feasible locations for stations can be done by using the simplest suitability analysis or map-algebra approach (McHarg and Mumford 1969). The map-algebra approach for a potential location involves measuring some form of accessibility score (and/or available utility value) (Cervero et al. 1999) using distance decay functions (exponential, power, binary, kernel form, etc.) (Kronbak and Rehfeld 2000; Skov-Petersen 2001; Hipp and Boessen 2017) based on the existing residences (or services and facilities) located elsewhere. There are various studies on location decision problems with small numbers of ADBI Working Paper 953 Roy and Maji 2 pre-identified feasible locations (Vorhauer and Hamlett 1996; Baban and Parry 2001; Vlachopoulou et al. 2001). In such studies, no further analysis or modeling was necessary apart from the suitability analysis. However, it becomes difficult to select one alternative over another when problems have large numbers of available feasible locations. Extensive location allocation modeling, apart from the suitability analysis, is necessary in such cases (Murray 2010). These models attempt to find the best facility locations by optimizing one or more objectives (minimum weighted distance, minimax distance, maximum utility, capacity constraint, etc.) (Fisher and Rushton 1979). Researchers obtained the optimal location for a facility by minimizing the maximal service distance required to reach the facility (Church and ReVelle 1974), by maximizing the expected profit of a convenience store in a region (Ghosh and Craig 1984), by maximizing the utility measured as a function of facility attributes and distance to the location (Drezner 1994; Drezner and Drezner 1996), by maximizing the total budget share of retail facilities under budget constraint (Drezner 1998), and by minimizing the weighted distance from demand points to the facility location (Yeh and Chow 1996; Church 1999). Numerous possible combinations should be examined to obtain the best solution to solve these types of problems. Previous studies assumed possible location of stations as a priori information (Bruno et al. 2002; Schöbel 2005; Laporte et al. 2011). Also, the local attribute details of a study area, such as right-of-way cost details, accessibility from existing public transportation facilities, environmental and geographically sensitive locations, and availability of sufficient land for station location, were excluded to simplify the problem (Bruno et al. 2002; Schöbel 2005; Repolho et al. 2013). These simplifications of relevant information could yield sub-optimal results. Certain studies integrated study area information from an urban rail perspective. However, there was no exclusive literature on HSR station locations. Therefore, a methodology to identify a feasible station location would be greatly useful in the HSR planning process. It should include various desirable requirements and constraints to assist in quantifying the desirable requirements by means of utility functions and thus, help in identifying the station location with the most utility. GIS can be particularly useful in developing such a methodology due to its advanced geo-processing, mapping, and visualization capabilities in managing various data types (such as property data, land use and land-cover maps, maps showing other important facility locations, and demographic information). Hence, the aim of this paper is to develop a GIS-based HSR station location identification model that considers relevant desirable requirements and constraints. 3. DESIRABLE REQUIREMENTS AND CONSTRAINTS FOR HSR STATION LOCATIONS HSR station locations typically need to satisfy certain desirable requirements based on accessibility, environmental concerns, geographic/spatial concerns, and physical requirements and conditions. These requirements can be represented mathematically and used in developing suitable utility functions to check the feasibility of a candidate site location. In this study, the main focus is on environmental and accessibility-based requirements. These desirable requirements, its mathematical representations and utility functions, are stated as follows: • Stations should avoid environmentally sensitive areas (for example, forests and wetlands), topographically infeasible areas (for example, lakes and rivers), and historically sensitive areas (for example, cemeteries, places of worship, historical ADBI Working Paper 953 Roy and Maji 3 sites, and ruins). Let ∁𝑠𝑠𝑠𝑠 and S𝑖𝑖𝑖𝑖 be the study area and the set of infeasible locations or areas, respectively. Then the feasible set of station locations 𝑆𝑆𝑖𝑖 can be represented as given in equation 1. 𝑆𝑆𝑖𝑖=�∁𝑠𝑠𝑠𝑠⋂S𝚤𝚤𝑖𝑖 � � � � � (1) Where, 𝑆𝑆𝑖𝑖= set of feasible station locations. A sharp threshold value can be assigned to avoid the environmentally sensitive regions. This type of model is known as isochronic definition (Cervero et al. 1999) or cumulative opportunities measure (Handy and Niemeier 1997). A binary model can be introduced to assign a fixed value of 1 and 0 to the locations which are feasible and infeasible, respectively. Let 𝐺𝐺𝑖𝑖 denote the candidate station location. The binary model can be represented as shown in equation 2. 𝛾𝛾𝑖𝑖=�1 if 𝐺𝐺𝑖𝑖∈𝑆𝑆𝑖𝑖 0 if 𝐺𝐺𝑖𝑖∈𝑆𝑆𝑖𝑖𝑖𝑖 (2) • Terminal stations (stations at both ends of the corridor) should be located close to the city center or downtown area of large regional cities to enhance the ridership potential (Menéndez et al. 2002). Let 𝐷𝐷𝑖𝑖 be the distance of candidate station 𝐺𝐺𝑖𝑖 from the downtown area, and 𝐷𝐷𝑇𝑇ℎ be the threshold distance from downtown. This distance should be less or equal to the threshold distance as indicated in equation 3. 𝐷𝐷𝑖𝑖≤𝐷𝐷𝑇𝑇ℎ (3) Sharp threshold value can be used to model the proximity of station locations to downtown area/city center. A binary model can be formulated as shown in equation 4 to assign a fixed value (1 or 0) to the locations closer than the given threshold value. 𝑈𝑈𝑖𝑖1=�1 if 𝐷𝐷𝑖𝑖𝑖𝑖≤𝐷𝐷𝑇𝑇ℎ 0 if 𝐷𝐷𝑖𝑖𝑖𝑖>𝐷𝐷𝑇𝑇ℎ (4) • Stations should avoid locations with extensively developed neighborhoods that have very high right-of-way costs. Since the region encompassing the station locations might have a high variance of land-cost values, a utility function based on a normalized cost (the values would be in the range [1,0]) can be formulated as equation 5. 𝑈𝑈𝑖𝑖2= (1 −𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖−𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑖𝑖𝑖𝑖𝑖𝑖 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑖𝑖𝑖𝑖𝑖𝑖−𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖_𝑖𝑖𝑖𝑖𝑖𝑖) (5) Where, 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = Cost of land or right-of-way cost for candidate station 𝐺𝐺𝑖𝑖; 𝐶𝐶𝑀𝑀𝑖𝑖𝑀𝑀_𝑖𝑖𝑖𝑖𝑖𝑖 = Minimum cost of land or right-of-way cost; 𝐶𝐶𝑀𝑀𝑠𝑠𝑀𝑀_𝑖𝑖𝑖𝑖𝑖𝑖 = Maximum cost of land or right-of-way cost; • Stations should be located near existing transportation facilities (such as airports, railways, bus stops, and highways) for ease of accessibility and intermodal integration. Let 𝐷𝐷𝑖𝑖𝑚𝑚 be the distance of candidate station 𝐺𝐺𝑖𝑖 from the existing ADBI Working Paper 953 Roy and Maji 4 transportation facility m, and 𝐷𝐷𝑤𝑤 be the threshold average walking distance, then, as per the accessibility requirements, it is represented as equation 6. 𝐷𝐷𝑖𝑖𝑚𝑚≤𝐷𝐷𝑤𝑤 (6) Distance decay functions are commonly used to model accessibility of facilities in spatial analysis (Skov-Petersen 2001). Hence, a utility function can be modeled using equation 6, which assigns the maximum utility value, i.e., 1, on satisfying the accessibility criteria, and a continuously decreasing utility value up to 0, with increasing distance. It is shown in equation 7. 𝑈𝑈𝑖𝑖3=�1 if 𝐷𝐷𝑖𝑖𝑚𝑚≤𝐷𝐷𝑤𝑤 1 𝑒𝑒(𝐷𝐷𝑖𝑖𝑚𝑚 𝐷𝐷𝑤𝑤−1) if 𝐷𝐷𝑖𝑖𝑚𝑚>𝐷𝐷𝑤𝑤 (7) 4. PROBLEM FORMULATION The objective of the study is to optimize station location by satisfying the desirable requirements. A positive utility score can be assigned to each location in the study area, which is estimated using the utility functions developed for each desirable requirement. Relevant weights 𝑊𝑊𝑖𝑖 are assigned to each desirable requirement j. The summation of all weights is equal to 1. Therefore, the utility score for each location would be the weighted summation of positive scores, based on the number and extent of desirable requirements satisfied. The problem can thus be formulated as the maximization of this total utility score for candidate station locations in the study area. The station location identification is thus formulated as a mixed integer programming problem, as shown in equation 8, with constraints in equations 9 and 10. 𝑀𝑀𝑀𝑀𝑀𝑀∑ ∑ 𝛼𝛼𝑖𝑖∗𝛾𝛾𝑖𝑖∗𝑊𝑊𝑖𝑖∗𝑈𝑈𝑖𝑖𝑖𝑖 𝑀𝑀𝑖𝑖 𝑖𝑖=1 𝑀𝑀𝑛𝑛 𝑖𝑖=1 (8) Subject to ∑𝑊𝑊𝑖𝑖 𝑀𝑀𝑖𝑖 𝑖𝑖=1 = 1, 0 < 𝑊𝑊𝑖𝑖< 1 (9) 0≤𝑈𝑈𝑖𝑖𝑖𝑖≤1 (10) Where, 𝛼𝛼𝑖𝑖 = �1 if location 𝑖𝑖 is selected 0 otherwise 𝑈𝑈𝑖𝑖𝑖𝑖 = utility score based on desirable requirement j for location i 𝑊𝑊𝑖𝑖 = weightage given to requirement j 𝑛𝑛𝑛𝑛 = total number of candidate locations in study area 𝑛𝑛𝑛𝑛 = total number of desirable requirements for station location 5. METHODOLOGY Infeasible locations are identified a priori and screened out from the study region. Subsequently, the feasible region is divided into grids of sizes equal to station location areas. A positive utility score is assigned to each feasible grid location for each desirable ADBI Working Paper 953 Roy and Maji 11 Figure 7: Variation of Total Utility Scores for Different Weightage Assignment for Mumbai ADBI Working Paper 953 Roy and Maji 12 Table 3: Variation of Total Utility Scores for Different Weightage Assignment Desirable Requirement Desirable Requirement Values Individual Utility Score Weightage Assignment Total Utility Scores Close Proximity to City Center (Km) 0.36 𝑈𝑈𝑖𝑖1=1.00 S1 0.930 S2 0.850 S3 0.931 Avoiding High ROW Cost (INR Crores per Acre) 87.92 𝑈𝑈𝑖𝑖2=0.768 S4 0.904 S5 0.824 S6 0.884 Accessibility to Existing Transport Points (Walking Distance) (Meters) 300 𝑈𝑈𝑖𝑖3=1.00 S7 1.00 S8 0.768 S9 1.00 It can be observed from Table 3 that the station location selected is at close proximity to the city center (less than 3 km), and is within accessible walking distance from the existing transportation points (within 400 m). Hence, the station location was given the highest possible individual utility score i.e., 1, for both desirable requirements (𝑈𝑈𝑖𝑖1,𝑈𝑈𝑖𝑖3). It also can be seen that the right-of-way cost for the selected station location is 87.92 INR crores per acre, which is neither the lowest (0.368 INR crores per acre) nor the highest (300 INR crores per acre) cost. Hence, the utility score 𝑈𝑈𝑖𝑖2 was neither 1 nor 0. Further, it shows the variation of utility scores as they pertain to different assigned weightage for the planned HSR station location in Mumbai. It is evident from Table 3 that the station location selected for this case study reports high utility scores for each of the assigned weightage, the lowest being 0.768 for S8, where the right-of-way cost has the highest weightage. This station location completely satisfies two desirable requirements (i.e., accessibility and proximity to the city center) and partially satisfies the third desirable requirement (i.e., right-of-way cost). Also, the selected station location in Mumbai is not on environmentally sensitive land. Hence, it can be concluded that the station locations selected for the given case study should provide high utility to the adjacent population based on accessibility, land cost, city-center proximity, and environmental impact factors. 8. CONCLUSION HSR station locations are vital, as they provide access to the riders, serve as multi-modal transportation hubs (with connections to regional and local transit), and are prime locations for Transit-Oriented Development. This paper presents a GIS-based analytical model that optimizes station-location specific, desirable requirements. The paper states the desirable requirements for HSR station locations, which include (though not necessarily limited to) improved accessibility and intermodal integration with existing transportation facilities and services, avoidance of environmentally sensitive areas and land with higher right-of-way costs, and other strategic necessities. Suitable utility functions are developed to estimate the utility of a candidate location associated with its respective requirements, which are integrated into the station locations identification process. Appropriate weights are assigned based on relative importance of each requirement. The overall utility of a location is then estimated as the weighted summation of these utility scores. In other words, this study quantifies the overall utility of an HSR station by analyzing the extent to which a location satisfies these desirable requirements, using appropriate utility functions and weightages. These vital components were mostly ignored in previous HSR models. ADBI Working Paper 953 Roy and Maji 13 The developed methodology demonstrates how an available GIS database can be used in the real-world planning stage of the development of an HSR project. Station location identification is modeled and covered in this methodology, which is a primary aspect of HSR development. This utility-based quantification methodology has the capability of easily identifying feasible station locations in the corridor for HSR. Such quantification can be used by the planners for further analysis and station location selection. This study demonstrates the applicability of the methodology in HSR planning, using the city of Mumbai, India as a case study. The results obtained are compared with the planned realworld station location identified for the city of Mumbai and show promising results. The developed methodology is expected to help the planners in station-location identification, in particular, and the overall planning of HSR, in general. The future scope of work in this methodology could be inclusion of additional socioeconomic requirements relevant for station locations, modeling, and examining subsequent steps of HSR development. These steps include using the configuration of stations to develop an HSR alignment for the corridor. ADBI Working Paper 953 Roy and Maji 14 REFERENCES 99acres web portal, https://www.99acres.com/ (accessed 20 October 2017). Baban, S.M. and Parry, T. 2001. Developing and Applying a GIS-assisted Approach to Locating Wind Farms in the UK. Renewable Energy, 24(1): 59–71. Bhuvan web portal, http://bhuvan.nrsc.gov.in/ (accessed 26 October 2016). Bruno, G., Gendreau, M. and Laporte, G. 2002. A Heuristic for the Location of a Rapid Transit Line. Computers & Operations Research, 29(1): 1–12. 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