Filling Station Site Selection with GIS and MCDM for Planning Studies: A Case of Trabzon, Türkiye

With the rapid development of mobile communication technology, communication networks are becoming more and more complex, which makes the planning of communication networks, especially the selection of station sites, more and more complicated. How to efficiently cluster the areas to be covered and plan the distribution of base station sites so that the benefits can be maximized with the minimum cost is a problem that has received a lot of attention. Station site planning is a combinatorial optimization problem, which is usually solved using intelligent optimization algorithms. In this paper, according to the known conditions, the site planning is carried out in the target area, and the number and location of new macro base stations and micro base stations are reasonably allocated so that 90% of the total service volume of weak coverage points are covered by the planned base stations, and the coordinates of the new sites and the types of base stations at the sites are given. On this basis, considering that the coverage area of each station is a graph consisting of three sectors, the results of the optimal station site and sector angle and the ratio of the total service volume of the weak coverage points that can be covered at most are given by recalculating whether the new stations can cover 90% of the total service volume of the weak coverage points under the conditions of the optimal station site and sector angle.

The previous chapter introduced the development of HSR in China and worldwide. From a theoretical perspective, the impact of HSR on three different spatial scales and levels of development—the region, city and area surrounding the station—is discussed to explain the research ideas, and methods, and to provide the overall framework for the book’s development. This chapter focuses on the relationship between station site selection and the urban development of HSR by analyzing Beijing-Shanghai HSR line as the core case. Station site selection is a complex technical demonstration and political consultation process involving all parties’ interests and various considerations. The final location of the station site is very important for the development of the towns.

Electric vehicle charging station (EVCS) site selection occupies a prominent position in the development of the electric community to solve the hard problem of Electric Vehicle (EV) charging. However, two critical issues have not been solved by the existing research. Firstly, the scope of EVCS site selection only considers the whole city, which deviates from the actual situation. Secondly, the uncertainty and hesitation of decision information is not well expressed. To handle the above problems, this paper builds a comprehensive EVCS site selection decision framework for Residential Communities (EVCSRC) with triangular intuitionistic fuzzy numbers (TIFNs). First of all, the distinctive index system of EVCSRC site selection factors including economy, social, environment, planning and feature portrait of residential communities is established. Then, the TIFNs is utilized for decision makers (DMs) to express the indeterminate information. Furthermore, a fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (Fuzzy-VIKOR) is approach is utilized to rank the alternative EVCSRC sites. Finally, a case of Beijing is studied to demonstrate the validity of the proposed site selection framework. The result shows the EVCSRC site located at Sijiqing community in Haidian district should be selected as the optimal site. This paper presents a feasible and easy-to-use decision-making framework for investors.

The seismic hazard department and department of the Carpathian region seismicity of the Subbotin Institute of Geophysics of the National Academy of Science of Ukraine, with the support and collaboration of the U.S. Department of Energy, Lawrence Livermore National Laboratory, Michigan State University, and the EarthScope Consortium had done first steps to expand the Ukrainian National Seismic Network (UT) through the installation of permanent broadband seismic stations in the territory of Ukraine.The main goal of the Seismic Network Expansion and Modernization in Ukraine is to improve regional network coverage by making high-quality data from new stations openly available to the global scientific community in real time. It will strengthen national seismic monitoring and earthquake response capabilities in Ukraine by upgrading and expanding the national networks with high-quality broadband seismometers and strong motion sensors. It will emphasize data quality, real-time data exchange, and network sustainability through training in best practices on station site selection, installation, data management, and network operation.To maximize the effectiveness of investigating existing and new seismic sites, multiple factors must be considered during the initial selection, preparation, and installation of new seismic stations. One critical component during the site selection process of any seismic network is an assessment of the seismic noise level at potential sites. The capacity of any seismic station to detect earthquakes and record high quality waveforms will be determined by the signal and noise characteristics of the site. Proper site selection is strongly related to the network's region and can be a critical issue. Besides the earth’s natural background noise, there are other noise sources to consider like those related to infrastructure close by (roads, traffic, mining activity, tenants, etc.). The goal of this work is to conduct noise surveys that can be quickly deployed in order to efficiently evaluate potential sites for the installation permanent seismic stations.An initial noise survey was conducted at two sites: one at an existing site LUBU (near Liubeshka village, Lviv district) and one at a new site SUGL (near Mala Uhol`ka village, Zakarpattia district). We analyzed, and report the data in the form of both time-history examples and standardized Probability Density Function noise plots. Seismic spectral analysis based on the calculation of Power Spectral Density distribution using a Probability Density Function by McNamara approach.

The traditional station site selection work is mainly based on manual exploration by engineers whose shortcomings are timeconsuming, laborious, and uncertain. This paper studies the correlation between multi-source heterogeneous data, such as land spatial planning data, geographic data and power grid thematic data and power grid project station site selection. First of all, an adaptive particle swarm optimization (PSO) algorithm for processing the geographic information (GI_PSO) is proposed. The algorithm can not only adaptively adjust the particle's motion strategy and speed based on the particle diversity, but also adaptively adjust the number of particles based on the distance between extreme points. Second, the performance of the algorithm is evaluated in the extremum search of a PSO common test function. Then the multi-source heterogeneous data is processed to meet the needs of station selection, and the details of the algorithm used to find multiple sites is presented. Finally, an example of searching the optimal three station sites with the lowest cost in an area of about 60 km×45 km is used to verify the correctness of the method in the paper.

تحلیل فضایی- مکانی جایگاههای پمپ بنزین و تعیین مکان بهینۀ احداث جایگاههای جدید با استفاده از GIS و MCDM (مطالعۀ موردی: منطقۀ 6 شهرداری تهران)

Fuzzy-based GIS approach with new MCDM method for bike-sharing station site selection according to land-use types

Urban economic development, congestion relief, and traffic efficiency are all greatly impacted by the thoughtful planning of urban metro station layout. with the urban area of Lanzhou as an example, the suitability of the station locations of the built metro stations of the rail transit lines 1 and 2 in the study area have been evaluated using multi-source heterogeneous spatial data through data collection, feature matrix construction, the use of random forest and K-fold cross-validation, among other methods. The average Gini reduction value was used to examine the contribution rate of each feature indicator based on the examination of model truthfulness. According to the study's findings: (1) K-fold cross-validation was applied to test the random forest model that was built using the built metro stations and particular factors. The average accuracy of the tests and out-of-bag data (OOB) of tenfold cross-validation were 89.62% and 91.285%, respectively. Additionally, the AUC area under the ROC curve was 0.9823, indicating that this time, from the perspective of the natural environment, traffic location, and social factors The 19 elements selected from the views of the urban function structure, social economics, and natural environment are closely associated to the locations of the metro station in the research region, and the prediction the findings are more reliable; (2) It becomes apparent that more than half of the built station sites display excellent agreement with the predicted sites in terms of geographical location by superimposing the built metro station sites with the prediction results and tally up their cumulative prediction probability values within the 300 m buffering zone; (3) Based on the contribution rate of each indicator to the model, transport facilities, companies, population density, night lighting, science, education and culture, residential communities, and road network density are identified as the primary influential factors, each accounting for over 6.6%. Subsequently, land use, elevation, and slope are found to have relatively lower contributions. The results of the research provided important information for the local metro's best location selection and planning.

Study of site selection of electric vehicle charging station based on extended GRP method under picture fuzzy environment

To design a more suitable scheme under different conditions so that subway stations can play their role better, this study investigated the construction elements of subway stations in residential areas. Metro stations in residential areas are generally located at the intersection of urban roads. As the “handover space” of the city, the construction principle should be based on people’s experience. As the core basis for traffic planning and future operation management, construction of subways in residential areas should take into consideration factors such as trip volume, distribution, and mode selection of residents, and be determined based on such mathematical models as the unit factor method, gravity model, and disaggregated analysis method. Station site selection is based on the point, line, and surface elements, and the importance of a station in the line network is judged by degree and betweenness centrality; the accessibility of the line network is determined by connectivity. In this study, the influencing factors of residential area subway station construction are divided into construction elements and site selection. Internal construction elements of stations include station entrances and exits, escalators, ticket machines, and transfer routes, and the conclusion of the mathematical model is used to select or give opinions about the internal construction elements of the subway. The point, line, and surface elements and the connection relationship between the subway and buses are used to determine the site selection of the subway. Furthermore, this paper discusses the three elements that affect the construction of the subway, comprehensively considers the functional requirements of the subway, and makes reasonable adjustments to each element. Finally, the requirements for the elements of the subway construction are determined.

In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of ensuring sustainable economic growth, offshore wind power development not only provides a clean and sustainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind energy projects have been promptly suggested in Vietnam due to policy advancement, with the country's excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision-making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete decision information use and information loss during the decision-making process, and it is easy to overlook the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank locations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable development has been recognized by reviewing the literature review and interviewing experts to practice the two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed method. The best location schemes have been determined by using the decision framework. The results of the sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. The proposed methodology can be used to attain a decision-making process at the regional level for offshore wind farm planning and coastal development, and the study results encourage the establishment of renewable energy development policies.

This paper presents an overview of the Radiofrequency (RF) coverage analysis methodology used to design and build the radio site layout in support of the Federal Aviation Administration (FAA)'s Automatic Dependent Surveillance - Broadcast (ADS-B) Air Traffic Modernization Program. This layout is optimized to meet system performance, safety, and interference requirements while minimizing the number of radio station sites required for providing RF coverage in the entire airspace of the Unites States. In addition, it includes the process of designing and applying the radio station siting template for site selection process. This template provided the basis for development of computer algorithms that facilitated the selection of radio station sites during the proposal phase of ADS-B program. Because this algorithm did not take into account detailed terrain, the reverse viewshed analysis method was developed to account for terrain and optimize radio station siting, in particular, when designing for Terminal service volumes. This paper describes the utilization of reverse viewshed in radio station site selection, a method that allows for an exhaustive evaluation of all tower resources and greatly facilitates the radio site selection process. In addition, this paper provides an overview of the tool selected for the development of a model-based Service Volume engineering environment. The tool will be used to capture the service volume design and configuration parameters.

In view of the imbalance of vehicle refueling needs belonging to different routes in time and space, this paper presents a multi-objective optimization model to design the site layout of bus gas stations, in which a fuzzy vehicle fueling demand for each line is considered. The proposed model features the process of vehicle refueling in bus gas stations abstracted as multi-server queuing systems to analyze the influence of the location of fueling stations on bus scheduling. Based on credibility theory, the expected value model of the site selection for bus gas stations is established, where some practical factors such as vehicle travel, queuing and refueling are comprehensively considered. The primary objective is to minimize the construction cost of filling stations, while the secondary objective is to minimize the cost of refueling all buses. According to the characteristics of the problem, it is presented as a deterministic, single objective, linear programming model. Genetic algorithm is designed to solve this problem by defining the coding scheme of solutions, fitness function, and the heuristic algorithm of generating the initial population. Finally, the optimal site selection scheme for bus gas stations is calculated and validated through a numerical example. The influence of fueling station’s ability on its layout is analyzed, thereby the validity of the model and algorithm is verified.

The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper for EVCS site selection. Firstly, the use of the PROMETHEE method can bolster the confidence and visibility for decision makers. Secondly, the cloud model is recommended to describe the fuzziness and randomness of linguistic terms integrally and accurately. Finally, the Analytical Network Process (ANP) method is adopted to measure the correlation of the indicators with a greatly simplified calculation of the parameters and the steps required.

The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations.