Decision model for siting transport and logistic facilities in urban environments: A methodological approach

Accepted Manuscript Decision model for siting transport and logistic facilities in urban environments: A methodological approach ´ Alberto Fraile, Emilio Larrod´e, A.Alberto Magre˜na´ n, Juan Antonio Sicilia PII: DOI: Reference:

S0377-0427(14)00568-8 http://dx.doi.org/10.1016/j.cam.2014.12.012 CAM 9918

To appear in:

Journal of Computational and Applied Mathematics

Received date: 14 October 2014 Revised date: 28 November 2014 Please cite this article as: A. Fraile, E. Larrod´e, .A. Magre˜na´ n, J.A. Sicilia, Decision model for siting transport and logistic facilities in urban environments: A methodological approach, Journal of Computational and Applied Mathematics (2014), http://dx.doi.org/10.1016/j.cam.2014.12.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Decision model for siting transport and logistic facilities in urban environments: a methodological approach ´ Alberto Magre˜na´ nb,∗, Juan Antonio Siciliab Alberto Frailea , Emilio Larrod´ea , A. a Departament

b Departamento

of Mechanical Engineering, University of Zaragoza, 50018 Zaragoza, Spain de TFG/TFM, Universidad Internacional de La Rioja, 26002, Logro˜no, Spain

Abstract The aim of this paper is to define a decision model that allow, through a Geographic Information System (GIS) to determine in an urban setting, the possible optimal locations of various facilities that would make up a new use for the transport infrastructure or logistics sector. The proposed methodology is based on the superposition of layers in a GIS software, enabling prioritization or exclusion of certain areas depending on whether or not meet certain requirements defined in each of these layers. The formulation used assigns each area of the map one degree of decision which classified all areas the best solution for supporting the solution in the Jenks optimization method. Five case studies of transportrelated facilities and as an example the location of a hydrogen refuelling station is solved taking into account the proposed methodology. Keywords: GIS, Optimal siting, Urban transport, Decision criteria.

1. Introduction Studies on the location of services are an issue of geographical interest to the extent that any landscape in which man intervenes is the result of diverse and multiple decisions on where to locate a specific equipment. These decisions are made by individuals, public institutions or private institutions, ultimately an agent or “decider” who holds in his hands the power to resolve on where a service to meet specific needs of the population will be installed. If we stop to think about the results that we provide these studies, i.e., the ability to know where to locate certain facilities, then it entered the field of study of regional geography applied which has among its objectives of spatial planning. The foregoing is used to assert that any study on facility siting is a multidisciplinary research, which varied perspectives often difficult to address jointly involved. The planning applications “Geographic Information Systems” (GIS) have been used in a lot of cases for the location of renewable energy facilities such as wind farms, assessing the visual impact of wind farms, photovoltaic electrification the biomass assessment, etc., [10, 19], as well as aid in the process of selection of areas for construction of landfills [22]. One of the most common strategies based on GIS, which are designed to facilitate decision-making in the selection of optimal siting, land suitability analysis, and resource assessment are Multi-Criteria Analysis (MCA) [2, 4]. The method of Analytic Hierarchy Process (AHP), originally developed by Saaty (1980), is a technique flexible and easily implemented MCA and its use has been widely explored in the literature with many examples in the location of facilities [9, 25] and land suitability analysis [29]. A comprehensive literature review of studies that have applied this method in different fields can be found in Vaidya and Kumar in [26]. The popularity of AHP method to provide solutions to a multicriteria problem is attributed to the fact that you consider both tangible and intangible criteria [3]. There are two characteristics that distinguish this approach from other multicriteria methods: the construction of the hierarchical structure and pairwise comparisons between different criteria, in order of weight relative to the overall objective. Another advantage of using the AHP method is employing a consistency that can rule inconsistent judgments [14]. Other interesting paper in routing field with other strategies can be found in [1, 8, 11] and in other fields in [18]. This paper discusses the possibility of determining, in an urban setting, the possible optimal siting of various facilities that would form a new infrastructure applied to the transport sector or logistics arises. For this, the proposed methodology is based on the superposition of layers that allow prioritization or exclusion of certain areas depending on whether or not meet certain requirements defined in each of these layers. Each of these layers represents each of the different decision ∗ Corresponding

author Email addresses: [email protected] (Alberto Fraile), [email protected] (Emilio Larrod´e), [email protected] ´ Alberto Magre˜na´ n ), [email protected] (Juan Antonio Sicilia) (A.

Preprint submitted to Elsevier

November 21, 2014

criteria that are considered for decision making. These could be some of the following: medium density index daily traffic (ADT), accessibility, population density, routes and stops to public transport, shopping centres, industrial estates, general urban planning, intermodality with other media transportation, bike lanes,. . .. Taking into account these decision criteria, several problems have been proposed to study the optimal siting of different private and urban transport-related facilities, and urban logistics: • Public parking for private vehicles: The construction of a new parking requires an important analysis to determine the optimal siting, where among others, the fundamental quality would be the accessibility to other modes of transport. • Bicycle parking underground: Increased use of bicycles in cities makes parkings have to plan to accommodate this increase bicycle and ensure greater security against theft. The location of such facilities should avoid the least crowded places. • Urban Distribution Centre (UDC): The location of such centres for centralized distribution of goods in small cities largely reduces traffic delivery vehicles. The proximity to residential areas and easily accessible by car would be the key to this type of facility. • Hydrogen supply stations and charging points e-vehicles: The new propulsion systems for vehicles that must be done to turn dispensers for these new technologies, whether electricity, hydrogen, biomass, etc. The inclusion of electric vehicles in the current trade is booming, so it was decided to analyze the facilities to recharge these vehicles as a case study. Similarly, hydrogen powered vehicles are making its way into the urban traffic, especially buses and taxis, so have hydrogen supply stations are considered a key to continue the growth of these vehicles use requirement. The size, accessibility and population density are factors to consider. 2. Background of GIS In recent years, GIS has become an increasingly popular tool for the selection of optimal sites of different types of activities and facilities. GIS is not only designed for computer systems design maps, but also powerful tools for geographic analysis. A GIS is a system of hardware, software and procedures to facilitate the acquisition, management, manipulation, analysis, modelling, representation and display of spatially referenced data for solving complex planning and management [7]. Location models try especially hard to study and resolve that are frequent in the process of decision-making in urban and regional planning issues. Consequently, it stands to reason that it is important to use and develop the most appropriate tools to address these problems. For this reason there have been efforts to exploit the advantages of integrating models and GIS location. Despite the above statement, GIS today have a number of serious problems for the application of spatial optimization models. In short, GIS are not designed and therefore do not offer the best performance and functionality for a rigorous approach to the issues involved in optimization problems. Limitations regarding the three main stages in the formation of decisions are noted how: intelligence or understanding of the problem, i.e., how the spatial process of provision and use of work equipment; design alternatives, that is, generating a range of possible solutions to the problem; and evaluating them, in order to identify the best and therefore most recommended. These GIS suitability analysis is based on a tool designed by the landscape architecture and a renowned professor of urban planning at the University of Pennsylvania, Ian McHarg. McHarg was one of the pioneers of GIS, wrote the influential 1969 book, Design with nature, emphasizing the importance of protecting the physical environment and consider the introduction of new developments in the open. The suitability of an SIG for the location of facilities can be seen in several studies, for example in renewable energy installations [5, 24], landfills [12, 23], bicycle rental facilities, in locating and analysing hydrogen supply stations [27]. Today, GIS capabilities are used in various fields, such as the placement of municipal service centres [21], is one of the use of GIS in urban management. In regard to public parking as an important part of modern urban transport system, have a vital task in providing places for extracting stationary traffic in the urban transport system, and as a result of a decrease the traffic density. Selecting the appropriate site of these centres help increase their effectiveness and reduce the problem of parking on the streets. Today, the selection of the location of public car parks in many cities is made using a traditional method, indicating that creates inefficiency in the car parks and even creates traffic problems. Therefore, it is needed the use of new systems that have the ability to analyse a large number of parameters simultaneously in selecting the location of parking. In applied work on the location of parking could be featured Weant studies (1978), in which an analysis is carried out in relation to the needs of some American cities new car parks using GIS. Also conducted any study on the role of parking in optimizing residency status through the use of GIS in the city of Newton [15]. The aim of this study was presented as the selection of the location of a public parking using GIS, in which five regions transit Shiraz city are studied, and thus the study results have been used in the master plan transport of Shiraz. 2

3. Methodology In developing the tool, the program used was ArcView which is a software application of ArcGIS, its number 10 release. ArcGIS is a GIS software designed by Environmental Systems Research Institute (ESRI) for multi-level work. It is a GIS software to view, create, manipulate and manage geographic information, these correspond to landmarks, addresses, positions in the field, urban and rural areas; regions and any land in certain locations. This information is worked systemically, which represents a substantial contribution to the work-related information with drawings and maps, allowing us to explore, view and analyse the data as parameters, relationships and trends that presents our data, resulting in new layers of difference information, maps and new databases. 3.1. Construction of the GIS For the construction of GIS are needed a serie of maps and their attribute tables that correspond to the common layer information for all scenarios as well as different decision criteria. These planes are represented in ArcGIS by “shapefiles” which are a vector format digital storage where the location of geographic features and attributes associated with them are saved. The information provided should be referenced with the same reference system, in our case through GoogleMaps program (georeferenced “datum WGS84 15N”), to represent it through maps in the application and generate the ”shapefiles” corresponding. As common layer information can be used, although the road map of the study area or the level of urban development plan divided in their cadastral areas. In each of these areas must have the following information: ranking and rating, size, cost of land and geolocation. From the different planes will overlap with each other through ArcGIS, so that the optimal siting are being sought according to decision criteria to be determined previously for each case study is selected. This methodology allows us to analyse various facilities to be located within an urban environment with different characteristics. Initially, you must have the layer overview, in case not, you must manually associate the corresponding data in the attribute tables of the most important points of the map. Then we analyse each of the factors taken into account in the selection system, in order to facilitate the resolution on the optimal siting of the different facilities, and some decision criteria (Ci ) pertaining to transport and logistics are set. Each of these criteria, described below, are associated with weights (Wi ) and are represented by planes with data tables. 3.2. Decision criteria (Ci ) Each of these decision criteria are represented in a different system GIS layer. Below lists and analyses the determining factors that will be taken into account for the different installations depending on the conditions of each one.

Figure 1: Set of the decision factors Ci

The Ci factors shown in Figure 1, corresponding to different decision criteria take the value 1 or 0, depending on whether the system recognizes for an installation, a analyzable factor or not respectively. 3

C1 : High ADT Annual average daily traffic (ADT) is the number of vehicles passing by a road for a year divided by 365. It is considered as the traffic intensity that corresponds to the average day of the year. The system analyzes the data for the ADT of different pathways and highlighted by a blue trace of a thickness greater the higher is the value of ADT pathway: ADT > 100000; 60000 < ADT < 100000; ADT < 60000. Areas of high ADT will be considered as areas of preference in the optimal siting. C2 : Accessibility This criterion of accessibility refers to the location of the points of the road network where intersections, junctions and road crossings occur, in order to have easy access to facilities. The system performs an analysis of the associated boxes tracks and locates nodes that belong to more than one track, and the busiest as recorded by ADT. Once located, a new layer in which many areas of preference for the location of facilities such as crosses paths with appropriate access to the city exist in the geographical area under study are represented is created. C3 : Population density Depending on the nature of the facility to be located, for transit, influx or security reasons, it is recommended that sites where they provide a place to satisfy needs and safety criteria set by the regulations in force and development. These security measures to keep minimum distances between facilities and residential areas are proposed. The system will analyse data on the general plan urban, locating qualified as residential plots, and identified with a color scale depending on the population density of the area. The classification in terms of population density can also be helpful in determining the optimal siting, since the higher the population density, and then there is greater likelihood that the traffic density is higher. It is thus found that the population density is correlated with ADT pathways. C4 : Public transport stops (bus, underground, tram, bike) This criterion is particularly responsive to the existence of fixed routes taken by bus lines, metro and other public transport services that make urban transport in cities. It needed to have the information of transport routes in the study area: geolocation stops or stations under the reference system Google and identification of the beginning and end of line. C5 : Shopping areas The shopping areas are often characterized by acting as a traffic concentrator element. On the one hand attracts large numbers of customers and visitors, and on the other in their environment creates a steady flow of traffic created by the workers and by freight required for procurement. In the system the location of areas with increased commercial activity in the area under study is introduced. The information required is georeferenced and identifier to the mall location. Once localized areas of increased commercial activity because it is inhabited areas of special sensitivity to the type of activity being developed, the system creates a new layer in which an area of exclusion or preference is represented depending on the connotations of the facility, with a perimeter of some meters around the shopping area. C6 : Industrial parks Like the commercial areas, industrial parks are characterized by their environment with high movement of vehicles. On the one hand attracts large numbers of customers, and secondly in their environment creates a steady flow of traffic created by the workers and by freight required for procurement. In the system the location of polygons in the study area will be introduced. The requested information concerns and identifier georeferenced siting of the industrial estate. C7 : Land for building development In order to allow the construction of the various facilities, the land on which they would be classified as located in the developable General Plan of Urban Development (GPUD) of the study area. The system will analyse the data associated with the plots related to soil type and create a new layer in which those building plots available are represented. C8 : Existence of bike lane The study of the layout of bike lanes that exist in the study area, used for the study of facilities related to the use of the bicycle as their existence will be a determining factor for the final decision on the location factor. The system will create a layer with the layout of these bike lanes. 4

3.3. Definition of weights (Wi ) The weights of the different factors are to be set within one of the three levels shown in the table below, where they will take the values indicated: Table 1: Default values of the weights associated the different decision criteria

Positive weight Average weight Negative weight

Weights associated to the decision criteria WP WA WN

0.4 0.05 −1

For each case study, the decision criteria will receive a value associated weight. WP may only be one or two case study for determining the criteria because that way you make sure to find the optimal siting. The value between positive, average and negative, applied to each criterion may be modified by the user in order to give priority to other criteria, in accordance with the characteristics that the system must have, for example determined by the type of fleet or vehicle. The weights of the criteria such as the creation of exclusion areas are those that receive negative weight, take the value equal to −1. There will be cases in which a judgment may receive two weight categories, depending on the area it select, since within the same layer, an area can be favorable and other unfavorable. 3.4. Formulation The formulation will be defined from the weights and the decision criteria defined above. When determining the possible siting for the locations of the facilities, and later listed in order of priority, the process will be as follows: The sum of the products of the area factors (Ci ) is performed by its associated weights in areas overlapping areas of preference which will catalog the area with a number degree of decision be called: degree of decision = Σji (Ci × Wi )

(1)

For an area to be considered as possible, degree of decision must be greater or equal to 0.4. In the case of equal to or less than zero is automatically dismissed area to be affected by an exclusion area. When degree of decision is greater than or equal to 0.4, in order to locate the optimal siting, it is determined that the higher the value of this number will be the most optimal siting of the possible location. Following these criteria, the list of possible locations would be made, considering that the result set should know if the area available is sufficient for the installation studied, or otherwise eliminate the area while being developable. For the final selection of the most suitable siting classification is obtained from optimizing Jenk, a classification system by which the thresholds are identified between classes using a statistical formula [13]. The optimization method called Jenk, is implementing different applications under the option of intervals as natural break points of distribution (natural breaks). This method has the dual purpose of obtaining high internal homogeneity classes, with maximum differences between classes to the number of intervals previously specified. It performs the classification based on the test of goodness of fit (Goodness of Variance Fit - GVF) indicating how well describes the whole class. This indicator takes different values according to the clusters that provide higher values. This is an iterative process that calculates the mean of each class with the respective variances, and observations moved between classes to obtain the maximum value of GVF [17]: j Σkj=1 Σi=1 (Zij − Z¯j )2 ¯ 2 ΣN i=1 (Zi − Z)

N

GV F = 1 −

(2)

where zij is the sum of squared deviations of the mean vector; z¯j is the sum of squared deviations between classes (Baz et al, 2009). 3.5. Selection of optimal siting After analysing all the decision criteria are applicable to the selection of these optimal siting which is the object of the system. From these decision criteria, the layers that represent the areas of exclusion (red) and preference (green) for the location of the facilities are created. Exclusion areas have priority over preference, ie, the preference areas that overlap the exclusion would be automatically rejected. 5

4. Cases study Given the decision criteria, have been proposed to study various problems the optimal siting of various facilities related to urban and private transport and city logistics: 4.1. Case 1: Public parking for private vehicles The location of a new public parking is needed planned with a number of factors. This methodology can be seen in Table 2 that the fundamental quality stops would be the proximity to other means of transport as well as the easy access to parking. Being in a building land, high population density, nearby highway commercial area with high ADT and high population density, are other important factors, but not as much as before. In contrast, low population density carries a negative assessment, since it would be inefficient parking in that area. Table 2: Decision criteria and weights for the case 1

Ci C1 = 1 C2 = 1 C3 = 1 C4 = 1 C5 = 1 C6 = 0 C7 = 1 C8 = 0

Wi W1 = WA W2 = WP W3 = WA/N W4 = WP W5 = WA W6 = 0 W7 = WA W8 = 0

4.2. Case 2: Bicycle parking underground This case study has been included due to increased bicycle users you have in the cities, as it has been incorporated as a conveyance of the most widely used in many countries. Therefore, a secure and accessible to a lot of bike parking is necessary in any urban environment. Its location will be, as can be seen in Table 3, with the fundamentals of high population density and location of public transport links nearby. The industrial parks and areas with low density will be qualified with negative weights, since the location of such facilities should avoid the least crowded places.

Table 3: Decision criteria and weights for the case 2

Ci C1 = 0 C2 = 0 C3 = 1 C4 = 1 C5 = 0 C6 = 1 C7 = 0 C8 = 1

Wi W1 = W 0 W2 = W 0 W3 = WP/N W4 = WP W5 = 0 W6 = WN W7 = 0 W8 = WA

4.3. Case 3: Urban Distribution Centres (UDC) The location of such centres for centralized distribution of small commodities in a city largely reduces traffic delivery vehicles creates many jams and conflicts. The proximity to residential areas, ie, areas with high population density, and the easy accessibility by car would be the key to this type of facility, and receive positive weights (WP ). Table 4 reflects the different assignable values. A nearby shopping centre is endowed by a middleweight, and that could stock many shops within walking distance, but as it is farther from the shopping area, weighing becomes negative.

6

Table 4: Decision criteria and weights for the case 3

Ci C1 = 1 C2 = 1 C3 = 1 C4 = 0 C5 = 1 C6 = 0 C7 = 0 C8 = 0

Wi W1 = WA W2 = WA W3 = WP W4 = 0 W5 = WA/N W6 = 0 W7 = 0 W8 = 0

Table 5: Decision criteria and weights for the case 4

Ci C1 = 1 C2 = 1 C3 = 1 C4 = 0 C5 = 0 C6 = 0 C7 = 0 C8 = 0

Wi W1 = P N W 2 = PP W 3 = PA W4 = 0 W5 = 0 W6 = 0 W7 = 0 W8 = 0

4.4. Case 4: Charging points e-vehicles Electric vehicles need like gasoline vehicles, places for recharging their deposits. The placement of these points requires an analysis of potential users therefore the most important factor is the high population density, as there where more people will be residing where by probability, more electric vehicle owners have. Good accessibility will also be important to reach these points quickly. As a negative test in this case would be the road to a very high ADT as they aren’t designed to accommodate road fueling areas or electrical recharging. The following table shows the values for this case. 4.5. Case 5: Hydrogen supply stations The latter case is analysed to be studied completely, making an example in the city of Zaragoza which is developed and explained in the next point number 5 in Table 6, the most crucial and restrictive criteria are shown, here are the industrial parks and development land, areas that are preferred in order to place such facilities recharge hydrogen vehicles, whether private vehicles and public. Negative weights are the areas close to shops and areas with high population density, due to the necessary safety distances. Table 6: Decision criteria and weights for the case 5

Ci C1 = 1 C2 = 1 C3 = 1 C4 = 1 C5 = 1 C6 = 1 C7 = 1 C8 = 0

Wi W1 = WA W2 = WA W3 = WA/N W4 = W A W5 = WA/N W6 = WP W7 = WP W8 = 0

5. Results The selection of the optimal siting of the facilities is the subject of this paper, so once analysed all the decision criteria would proceed to the selection of these locations from the formulation and taking into account the weight of each of the criteria. 7

As an example application of the decision model, we have chosen case 5, the location a hydrogen refuelling station ´ in the neighbourhood Actur city of Zaragoza, as shown in Figure 2 is a large neighbourhood with commercial, residential and industrial areas, with different lanes road and public transport systems throughout. The system will create a layer for each of the factors involved in this case study. Below is described as areas of influence for each factor are analysed.

Figure 2: Location map showing the study area.

C1 (High ADT): The system highlights an area around greater ADT pathways so that the greater distance to the track is 200m. This area is shown in green and will be considered as a preferred area when locating a supply station.

C2 (Accessibility): These areas of preference will be shaped like a circle with centre at the intersections and a radius of 400m. C3 (Population density): Due to the nature of hydrogen, for safety reasons it is recommended that the locations where it is intended to place the supply stations of this element, satisfy safety criteria set by the regulations in force. These minimum safety measures to save between hydrogen refuelling and inhabited areas distances are proposed. In the present case, has filed a condition being less than 300m from residential areas not listed as permitted location of a hydrogen supply station. The system will create a new layer with exclusion areas (in red) that comprise the residential urban areas with an added security perimeter of 300m. C4 (Public transport stops): Once located the principles and end of line, creates a new layer that represents a circular area of preference when it comes to locate the hydrogen refuelling around the principles and end of line with radius 150m.

C5 (Shopping areas): After locating the busiest shopping areas, the system creates a new layer in which an exclusion area is shown (in red) with a perimeter of 500m around the shopping area because it is inhabited areas of special sensitivity the 8

type of activity that takes place in them. However, due to the high concentration of traffic generated in the environment, the system is in turn a new preferably area (green) which locate the hydrogen supply station. This new area will have a crown with a width of 200m around the exclusion area indicated above.

C6 (Industrial Parks): Once located industrial estates, the system creates a new layer representing many areas of preference in which to place the hydrogen refuelling as industrial parks there, and that correspond to extensions that occupy the industrial parks. If the end it chooses to locate a supply of hydrogen station in an industrial area, it should separately analyse the best location within the same, depending on traffic flows, accesses and minimum distances to keep security.

C7 (Land for building development): To make possible the construction of hydrogen supply stations, field which will be located must be classified as building lot in the General Plan of Urban Development (GPUD) area in study, in this case ´ in the neighbourhood of the Actur of Zaragoza. The system will analyse the data associated with referring to the type of land and will create a new layer in which represent those lands for building available

C8 (Existence of bike line): Its not factor relevant for this type of facility so its valuation will be null.

Figure 3: Representation of all layers of decision factors used for the location of the hydrogen supply station.

From these decision factors are generated and overlapping the layers representing the exclusion areas (red) and preference (green) for the location of the hydrogen supply station. In the Figure 4 all these areas are represented. Exclusion areas have priority over preference, i.e., the preference areas overlying the exclusion areas would be automatically dismissed because of their negative valuation. Being the degree of decision an index which represents the sensitivity or vulnerability of each area to accommodate the proposed facility, so that an area will have a value, summations result of the different associated weights. This index varies between −2 and 1.05 which is to impose a limit threshold from which the object function is desirable for the location of the installation in question. It can make a division into 10 classes, so that the higher the index, then more favorable places to place the installation, but this division does not allow to set a critical threshold, since it has 286 possible combinations. To avoid this shortcoming of the model, it has been rated from the optimizing Jenk, a classification system by which the thresholds are identified between classes using a statistical formula as explained in Section 3.4. In Table 7, it can see the classification obtained from the use of Jenks, with 10 ranks. In defining results highlight the range that would be inadmissible as exclusion areas are having any of their weights with negative values. It must also say that it is possible that no area is the optimal class to make maximum objective function; but a map showing the areas bounded on which facilities cant be located (exclusion areas), are very useful for making the final decision to execute similar projects and looking for the best solution to the activity obtained thereof. 9

Figure 4: Representation of the optimum areas for the location of the hydrogen supply stations. Table 7: Rating of influences areas for optimal siting

Assessment [−2] [−1.95] − [−1.65] [−1.6] − [−1.25] [−1.2] − [−0.65] [−0.6] − [−0.25] [−0.2] − [0] [0.05] − [0.35] [0.4] − [0.75] [0.8] − [1] [1.05]

Results Inadmissible Inadmissible Inadmissible Inadmissible Inadmissible Inadmissible Assumable Acceptable Very Good Excellent

Once overlapping all areas of influence are calculated the valuations of areas where converge several influences areas. In the Figure 4 have numbered the most significant areas, taking into account other areas with the same color have the same valuation. Table 8: Assessment of the areas of influence for the optimal siting

Areas 1 2 3 4 5

Degree of decision C1 W1 + C2 W2 + (C6 W6 or C7 W7 ) = WA + WA + WP C1 W1 + C2 W2 + C3 W3 + C5 W5 = WA + WA + WN + WA C1 W1 + C2 W2 = WA + WA C3 W3 + C5 W5 = WN + WN C6 W6 or C7 W7 = WP

Assessment 0.5 −0.85 0.1 −2 0.4

The valuation of areas 1 (dark green) are the highest for this case, as shown in Table 8, which is considered the optimal areas for the location of the facility that is being studied. When choosing one or the other among the four areas with this assessment, we should see the plot economic factors, environmental sustainability or the dimensions necessary depending on the number of suppliers that have this facility. Also be noted that all red areas (areas 3 and 4) received a final negative rating since it belongs to an exclusion area. 6. Conclusions This decision model raised in this article allows finding the best location in a facility in a novel way, analysing all the factors affecting transport and logistics in a city-related facilities. The inclusion of GIS technologies allows to plan the correct location to visualize graphically the solutions and more intuitively. 10

The methodology proposed is based on the superposition of layers to enable the prioritization or exclusion of certain areas according to comply or not certain requirements defined in each of these layers. The decision criteria could be extended if necessary in any installation to locate include it and in the evaluation of the weights would have to assign the range of weight that would acquire. The case studies are posed any problems that have developed city when planning new facilities related to urban transport. The particular case that has been developed, the location of a hydrogen station, in the future it can be very useful because the hydrogen fuel cell vehicles will be introduced gradually in cities. The model gets the best solution to the searched location, supporting on Jenks optimization method, as the valuation of the degree of decision is the highest of all the studied area, dropping areas for security, inefficiency or other negative factors are not viable for the studied facility. A GIS system that reflects the modelling of the transport system and a set of decision criteria help to determine the size, characteristics and location of the network of hydrogen refuelling stations that minimizes the cost function and maximizes the operatively and functionality one. 7. Acknowledges This research is supported in part by UNIR research group: CYBERSECURITICS.es 8. References [1] Amadini, R., Sefrioui,I. , Mauro, J., Gabbrielli, M., 2013, A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing, International Journal of Interactive Multimedia and Artificial Intelligence, 2 (4), 67–75. [2] Aragon´es, P., Pastor, J.P., Garc´ıa, F., Pascual, A., 2010. An Analytic Network Process approach for siting a municipal solid waste plant in the Metropolitan Area of Valencia (Spain). Journal of Environmental Management 91, 10711086. [3] Aras, H., Erdogmus, S., Koc, E., 2004. Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renewable Energy 29, 1383–1392. [4] Awasthi, A., Chauhan, S.S., Goyal, S.K., 2011. A multicriteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling 53, 98-109. [5] Baban, S., Parry, T., 2001. Developing and applying a GIS-assisted approach to locating wind farms in the UK. Renewable Energy 24, 59–71. [6] Baz, I., Geymen, A., Nogay Er, S., 2009. Development and application of GIS-based analysis/synthesis modeling techniques for urban planning of Istanbul Metropolitan Area. Advances in Engineering Software 40 128-140. [7] Carrion, J.A., Estella, A.E., Dols, F.A., Toro, M.Z., Rodriguez, M., Ridao, A.R., 2008. Environmental decisionsupport systems for evaluating the carrying capacity of land areas: optimal site selection for grid-connected photovoltaic power plants. Renewable and Sustainable Energy Reviews 12, 2358–2380. [8] Cueva-Fer´andez, G., Pascual Espada, J., Garc´ıa D´ıaz, V., Gonz´alez-Rodr´ıguez, M., 2013, Kuruma: The Vehicle Automatic Data Capture for Urban Computing Collaborative Systems. The International Journal of Interactive Multimedia and Artificial Intelligence, 2 (2), 28–32. [9] Dey, P.K., Ramcharan, E.K., 2008. Analytic hierarchy process helps select site for limestone quarry expansion in Barbados. Journal of Environmental Management 88, 1384–1395. [10] Dom´ınguez, J., Amador, J., 2007. Geographical information systems applied in the field of renewable energy sources. Computers & Industrial Engineering, 52(3), 322–326. [11] Garca, C. M., Ortega, G. M., 2009, Route planning algorithms: Planific@ Project. The International Journal of Interactive Multimedia and Artificial Intelligence, 1 (2), 57–66. [12] Gemitzi, A., Tsihrintzis, V.A., Christou, O., Petalas, C., 2007. Use of GIS in siting stabilization pond facilities for domestic wastewater treatment. Journal of Environmental Management 82, 155-166 [13] Jenks, G.F., Caspall, F.C., 1971. Error on choroplethic maps: Definition, measurement, reduction. Annals of the Association of American Geographers, 61(2), 217-244. 11

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