Station Site Selection Research Articles

A Study on Bicycle-Sharing Dispatching Station Site Selection and Planning Based on Multivariate Data

Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages of low cost and sustainability. Bicycle-sharing operators use electronic fence parking points to manage bicycle-sharing, but it is too time-consuming and impractical to manage them individually. Therefore, it is necessary to cluster the electronic fence parking points and implement regionalized management. This study uses a Mean-shift clustering algorithm to cluster the electronic fence parking points on Xiamen Island, construct a bicycle-sharing dispatching station, and divide the management area. Singular value decomposition is employed to analyze the travel demand patterns of bicycle-sharing and travel characteristics. In addition, we constructed regression models to explore the relationship between the urban built environment and bicycle-sharing trips during the morning and evening peak hours. The study results show that: (1) The 73 dispatching stations constructed cover 86.72% of the bicycle-sharing data, with a good clustering effect. (2) The travel demand for bicycle-sharing shows multiple patterns and different characteristics in different spatial and temporal distributions, which are influenced by land use. (3) There are spatial and temporal differences in the impact of the urban built environment on bicycle-sharing trips, especially residential and enterprise poi densities have opposite effects on shared bicycle-sharing during morning and evening peak hours. The research results of this paper can serve in the planning of bicycle-sharing dispatch stations and the differentiated management and dispatch of bicycle-sharing, which can reduce operating costs and promote the development of sustainable urban transport.

Research on key influencing factors for site selection of DC microgrid-based hydrogen-electric hybrid refueling stations

In the context of the dual-carbon economy, HRSs play a critical role as an essential intermediary hub for upstream hydrogen production, midstream hydrogen storage and transportation, and downstream vehicle fueling. The use of renewable energy sources for hydrogen production and refueling has become a topic of great interest, and direct current (DC) microgrid-based HRSs are a promising model. However, there is a lack of research on the key influencing factors for the site selection of electric-hydrogen hybrid refueling stations. To address this research gap, this study reviewed the scientific literature on refueling station site selection and identified 34 site selection factors based on the characteristics of DC microgrid-based electric-hydrogen hybrid refueling stations. The importance of these factors was evaluated through a combination of fuzzy Delphi and LBWA models, and an optimized site selection evaluation index system was constructed. The results showed that hydrogen storage technology, wind speed, hydrogen production technology, lighting conditions, spatial safety, profit risk, and leakage risk are the key factors that influence site selection decisions. After eliminating nine non-important indicators, a site selection index system with 1-4-25 layers factors was established. The proposed key factors and index system have important guiding and reference significance for governments and investors in making site selection decisions and managing electric-hydrogen hybrid refueling station projects.

Location of Electric Vehicle Charging Stations Based on Game Theory

In order to solve the design problem of electric vehicle charging station distribution, based on the consideration of user and investor costs, this paper establishes a mixed integer model for charging station site selection based on game theory ideas. Among them, the user cost is determined by two indicators, namely, the cost of time for users to reach the charging station and the cost of time for users to wait in line, while the cost of the charging station is determined by the construction cost and the daily operation and maintenance cost. In the established model, the hierarchical analysis is used to minimize the combined cost of users and charging stations as the objective. In addition, an improved artificial bee colony algorithm is designed to solve the model. The improved algorithm adds a neighborhood search method and a feasible decoding scheme to the honey bee harvesting and tracking process, thus solving the problems of low search accuracy, poor convergence, and inability to directly calculate the mixed integer model of the original algorithm. Simulation results show that the improved artificial bee colony algorithm can effectively solve the mixed integer model and has higher search accuracy and convergence speed compared with the traditional method. By applying the algorithm to solve the siting model, the location and number of charging stations can be clearly planned, thus improving charging efficiency and reliability.

Battery-Swapping Station Site Selection and Distribution in Material Vehicles Scheduling Based on NSGA-II

The location planning of battery-swapping stations needs to consider many factors. This paper is based on the location planning of battery-swapping stations and considers limits on the number of electric material vehicles and battery packs. Firstly, a rapid vehicle exchange model for battery-swapping stations is established. On this basis, a steady-state model of the vehicle transporting materials is established, which describes the regular and dynamic operation of the whole vehicle dispatching system based on maximizing the number of vehicles always transporting materials on the road. Finally, a material transportation target planning model is established. NSGA-II is used to solve the maximum material transportation target planning function for the best selection of battery-swapping station sites and the best number of vehicles and battery packs for material transportation.

The charging station and swapping station site selection with many-objective evolutionary algorithm

The charging station and swapping station site selection with many-objective evolutionary algorithm

Adaptive multi-objective particle swarm optimization based on virtual Pareto front

In a multi-objective particle swarm optimization (MOPSO), the selection strategies of the personal best solution (pBest) for a single particle and the global best solution (gBest) for the whole swarm are two key challenges to balance the convergence and diversity of an algorithm during its iterative process. Many selection strategies in the existing literatures were emphasized on the individual characteristics of the separate particles rather than the collective features of the whole swarm. In this paper, a novel gBest selection strategy based on a new defined virtual generational distance indicator, which is calculated from a virtual Pareto front fabricated according to the geometry of a given elite archive, is proposed for selecting the most appropriate Pareto optimal solution as the gBest with respect to the comprehensive convergence and diversity contribution to improve the search effectiveness and efficiency of a MOPSO. Besides, an adaptive pBest selection strategy based on the evolutionary state in different iterations is designed for identifying the more suitable pBest from its personal archive for each particle to strengthen the exploitation or exploration ability adaptively. The experimental results show that a MOPSO with the new gBest and pBest selection strategies outperforms ten state-of-the-art competitive algorithms on DTLZ, F, WFG and ZDT series of benchmark problems. In addition, a case study on the site selection for mobile earthquake monitoring stations is also illustrated the effectiveness of the proposed algorithm in the real-world application.

Clustering Optimized Genetic Algorithm-Based 5G Communication base Station Site Selection

To minimize the negative impact of the base station construction, and at the same time make the base station cover as many users as possible, the Genetic Algorithm optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is proposed. Initially, the number of points is fixed and encoded into a gene sequence with 4 different features at each point. After that, the process of the DBSCAN is stated and then it constructs the cluster adaptation function. Then, with the combination of the cluster adaptation function and the coverage function, it can select the individual, cross the gene sequence, and change the slight value of the sequence. Ultimately, by iteration, it can output the optimal point location and the result shows that the optimal coverage is 95%.

Power partitioned neutral aggregation operators for T-spherical fuzzy sets: An application to [formula omitted] refuelling site selection

T-spherical fuzzy set (T-SFS) is emerged as one of the effective tools for dealing uncertainty in decision-making process. Whereas, power aggregation operators help us in normalizing the impact of extreme values and capture the interconnectedness of the arguments. Meantime, one of the most prominent factors in multi-attribute decision-making (MADM) problems is the lack of awareness of biasness. Neutral operations highlight fair and unbiased character of decision makers. Thus, aiming these advantages and heterogeneity of arguments, a hybrid form of operators, weighted power partitioned neutral average operator and weighted power partitioned neutral geometric operator are developed under T-SFS environment for the first time. Beside these, power weighted neutral average, power ordered weighted neutral average, power hybrid neutral average operators, and their dual forms are initiated too. A new modified score function for T-SFS is formulated. Based on the developed operators and score function, an MADM algorithm is constituted and utilized in solving a hypothetical case study problem on hydrogen (H2) refuelling station site selection. Finally, comparative study of the developed operators with other operators is carried out to explore the applicability and supremacy of the designed MADM algorithm.

An objective assignment method for siting new energy plants

This paper proposes a multi-criteria decision-making method for new energy power station site selection based on a fuzzy comprehensive analysis method. It is applied to make multi-criteria analysis decisions on the target location when selecting the address for building a new energy power station, to ensure the reasonableness of the corresponding weights of each index, and to give reasonable and scientific suggestions for decision-makers to obtain the expected results. We analyze the mechanism of the sequential relationship analysis method and fuzzy inverse entropy weighting method from the perspective of the index assignment method, and combine the two assignment methods by linear weighting, so as to lay the foundation for further construction of a multi-attribute hybrid index system with the coexistence of qualitative and quantitative indexes.

A graph-theory-based dynamic programming planning method for distributed energy system planning: Campus area as a case study

Distributed energy systems are widely used in current regional energy planning because of their flexibility in terms of energy supply. However, the differentiated demand for various building loads increases the uncertainty of the energy supply. To reduce load fluctuation and hydraulic imbalance, a dynamic programming method based on graph theory was proposed in this study for energy station site selection and pipeline network layout deployment. The kernel density method was applied to distribute the regional building load for the site selection of energy stations. With the minimum load fluctuation rate as the goal, a 0–1 dynamic programming method was proposed to optimize the energy supply range of the energy station. Based on graph theory, an improved Prim algorithm was developed to determine the pipeline network layout. Taking a campus area as a case study, the proposed planning method was shown to reduce the initial investment, annual operating cost, and equivalent annual cost by 1.23%, 6.52%, and 5.04%, respectively. The optimized planning scheme not only balanced the load fluctuation in each energy station but also reduced the total pressure loss of the pipeline network by 19.86%.

A hybrid probabilistic linguistic term set decision-making evaluation method and its application in the site selection of offshore wind power station

In the context of carbon neutrality, China vigorously develops clean energy and renewable energy. As a result, offshore wind power projects have grown rapidly. The success of offshore wind power projects depends on the location of offshore wind power station(OWPS), and this paper aims to develop a hybrid decision-making framework to address the location of offshore wind power stations. Firstly, a comprehensive evaluation attribute system of offshore wind power station site selection is innovatively constructed, including wind resources, construction conditions, economy, environment and risks. Secondly, probabilistic linguistic term set(PLTS) is used to describe uncertain information, which improves the reliability of decision information. Thirdly, The Best-worst method (BWM) based on probabilistic language term sets determines the weights of attributes, which ensures the accuracy of the attribute weight. Finally, an extended TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) method is proposed to rank the alternatives. The proposed method is applied to the location selection of offshore wind power station in Jiangsu Province, China. Through sensitivity analysis and comparative analysis, it is verified that the proposed model has good stability and applicability.

A Spatial Decision Support System Based on a Hybrid AHP and TOPSIS Method for Fire Station Site Selection

A Spatial Decision Support System Based on a Hybrid AHP and TOPSIS Method for Fire Station Site Selection

Analysis of the Factors Affecting the Construction of Subway Stations in Residential Areas

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.

Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations

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.

Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province

The applicability of a combined fuzzy best-worst method (FBWM) and geographic information system (GIS) was investigated to find the optimal location of a solar power plant site in Guilan province, which has a temperate and humid climate. Fifteen criteria were determined based on the guidelines and performance of photovoltaic (PV) systems and divided into four main categories: environmental, economic, technical, and social. The data were achieved from satellite images and organisations. The FBWM was applied to weight the criteria based on the opinion of the decision-maker, and then spatial analysis was done by GIS to find the potential sites for the solar power plant. The result of this study indicates that among the total area of Guilan province, about 0.74% is “the most suitable” region for solar power plants and 0.46, 0.54, 0.53, and 97.73% are in the class of “suitable”, “relatively suitable”, “least suitable” and “limited”. Four alternatives of A1, A2, A3 and A4 with an area of larger than 60,638 m2 which is the required area for a 10 MW solar power plant and a distance from power lines of less than 2 km, were selected. The techno-economic aspects were conducted among the four alternatives, which show that about a 35% increase of the feed-in tariff is necessary to decrease the payback period of the most economical site (A3) from 6.7 to 5 y, which encourages the investors of a PV power plant. Also, the greenhouse gas (GHG) emission analysis indicates that the construction of a 10 MW power plant on A3 could prevent the release of 10,113 t of GHG into the atmosphere annually.

Mobile Communication Network Site Planning Research Based on Simulated Annealing Algorithm Optimization Model

With the development of 5G network, it becomes a hot topic to reasonably plan the siting of communication base stations in the weak coverage area of 5G network. In this paper, we take improving the coverage of service volume and reducing the cost of building base stations in the weak coverage area as the target of siting, and select base station sites among 2500×2500 points in a given area under ideal conditions and actual conditions, respectively, and use the less time-complex clustering method to cluster all weak coverage points. In order to improve the feasibility of the model in practical applications, we try to develop the base station site selection scheme that benefits the most for the 5G construction company by setting up a multi-objective planning model based on multiple constraints. On this basis, the conditions of the multi-objective planning model are changed and optimized to obtain a new multi-objective planning model based on the requirement that the communication coverage of base stations is fan-shaped in real life, and the simulated annealing optimization algorithm based on greedy thinking is used to optimize and solve the listed multi-objective planning model to obtain a new station site plan.

Multiple attribute group decision making based on quasirung orthopair fuzzy sets: Application to electric vehicle charging station site selection problem

This paper aims to greatly improve decision makers’ capability of capturing their judgment in a broader space. In order to achieve this, we introduce the notion of a p,q-quasirung orthopair fuzzy set (p,q-QOFS), which is an extension of the q-rung orthopair fuzzy set. In p,q-QOFS, the sum of the pth power of membership degree and qth power of nonmembership degree is less than or equal to 1, where p and q are natural numbers. Thus, due to the additional parameter p, the p,q-QOFS can express incomplete information more flexibly and elaborately. This paper first develops the concept of p,q-QOFS and establishes that it is an extension of several existing fuzzy sets. Then, we introduce the score and accuracy functions of p,q-QOFS and analyze a few mathematical properties. Next, we define the Hamming distance measure between two p,q-QOFSs and some important properties. After that, we investigate the basic operations of p,q-QOFSs and extend these operational laws to aggregation operators. Further, we introduce the weighted averaging and geometric aggregation operators to aggregate p,q-quasirung orthopair fuzzy data. Moreover, we establish a p,q-quasirung orthopair fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for solving multi-attribute group decision-making problems with unknown attribute weights. The present study also discusses a case study illustrating the applicability of the method through the selection of the most appropriate site for an electric vehicle charging station in an Indian city. In this case study, we consider seven alternative sites, including Raniganj, Jamuria, Kulti, and Burnpur. As a result of this study, Jamuria appears to be the best location to build an electric vehicle charging station. Finally, we illustrate the validity and practicability of our developed method through a comparative analysis with existing methods.

Interblock Flow Prediction With Relation Graph Network for Cold Start on Bike-Sharing System

As the IoT technology becomes well established and sharing economy expands worldwide, the bike-sharing system (BSS) has spread fast in the last decade. When introducing the BSS in a new city, the operator often faces many challenges: e.g., optimizing station siting (physical or electric), constructing bike lanes, and making strategies for the initial distribution and rebalancing of bikes. These challenges require an accurate interblock flow prediction before deploying the BSS. In this article, we derive blocks from the urban road network and extract blocks’ features based on the distribution and types of POI. Then, the interblock flow can be predicted based on the features of both start/end block and neighbor blocks. We propose a unified architecture with a generalized attention mechanism named the relation graph network (RGN) to extract block features and make predictions across cities. The evaluations on real-world data sets show that RGN outperforms other popular graph neural networks in multiple metrics. We further simulate the applications of station site selection and bike lane planning based on the prediction from RGN, and the result provides a noticeable increase in the BSS’s deployment/operation efficiency.

Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience

To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis of the urban population. The goal of this model is maximum electric vehicle user satisfaction and the highest charging convenience. Then, according to model characteristics, the immune algorithm is designed and optimized to solve the model. The optimization of the immune algorithm includes two aspects. On the one aspect, judging that the stop condition is added in the mutation link. On the other aspect, two mutation operators are designed in the optimized immune algorithm. Finally, the simulation example is determined by a three-step method in Jinan City. The results show that the electric vehicle charging station site-selection model in this paper can better meet user needs compared with traditional models. Compared with the traditional immune algorithm, the convergence speed of the optimized immune algorithm is improved, and the proposed algorithm is superior to the traditional immune algorithm in terms of stability and accuracy.

Geographic information system and system dynamics combination technique for municipal solid waste treatment station site selection.

The rapid growth of municipal solid waste put pressure on the end treatment facilities, and the site selection of MSW treatment facilities needs to be carefully conducted. This study aims to develop a Standard Operational Procedures (SOP) for municipal solid waste (MSW) treatment station site selection based on GIS and system dynamics combination. System dynamics was used to establish a forecasting model for MSW simulation followed by the spatial analysis method to generate a geographic information system. The MSW simulation in Xiamen city using the system dynamics model proved that the model was with high prediction accuracy. An SOP for GIS-based MSW treatment station site selection was developed. The site selection for MSW comprehensive treatment in Xiamen using GIS and system dynamics screened the locations of the comprehensive solid waste treatment stations in Xiamen and eliminated unsuitable lands using the mapping function of the geographic information system, thus validating the SOP.