Station Placement Research Articles

Electric Vehicle Charging Station Placement Method for Urban Areas

For accommodating more electric vehicles (EVs) to battle against fossil fuel emission, the problem of charging station placement is inevitable and could be costly if done improperly. Some researches consider a general setup, using conditions such as driving ranges for planning. However, most of the EV growths in the next decades will happen in the urban area, where driving ranges is not the biggest concern. For such a need, we consider several practical aspects of urban systems, such as voltage regulation cost and protection device upgrade resulting from the large integration of EVs. Notably, our diversified objective can reveal the trade-off between different factors in different cities worldwide. To understand the global optimum of large-scale analysis, we add constraint one-by-one to see how to preserve the problem convexity. Our sensitivity analysis before and after convexification shows that our approach is not only universally applicable but also has a small approximation error for prioritizing the most urgent constraint in a specific setup. Finally, numerical results demonstrate the trade-off, the relationship between different factors and the global objective, and the small approximation error. A unique observation in this study shows the importance of incorporating the protection device upgrade in urban system planning on charging stations.

Two-Dimensional Drone Base Station Placement in Cellular Networks Using MINLP Model

Utilization of drones is going to become predominated in cellular networks as aerial base stations in order to temporary cover areas where stationary base stations cannot serve the users. Detecting optimal location and efficient number of drone-Base Stations (DBSs) are the targets we tackle in this paper. Toward this goal, we first model the problem using mixed integer non-linear programming. The output of the proposed method is the number and the optimal location of DBSs in a two-dimension area, and the object is to maximize the number of covered users. In the second step, since the proposed method is not solvable using conventional methods, we use a proposed method to solve the optimization problem. Simulation results illustrate that the proposed method has achieved its goals.

Fast charging stations placement methodology for electric taxis in urban zones

In recent years, as a policy to increase penetration of electric vehicles and reduce air pollution, the number of electric taxis has increased in the transportation networks. In contrast to private electric vehicles, electric taxis need to recharge in a short time due to their constant operation and different driving patterns. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. To improve the sustainable planning of urban infrastructures, this paper presents a methodology to help in decision making for installing fast charging stations, considering the criteria of all urban planners involved in the installation process. The result of the proposal is a spatial database that identifies locations for charging stations that meet the predefined criteria. Such database can be visualized in geographic information systems of the urban planning department or the electrical service concessionaire. The proposed methodology is tested in a Brazilian medium-sized city showing the importance of this information in decision making. The proposal is compared with another spatial methodology, showing that the proposed method creates a better spatial distribution of charging stations giving better options for the main agents interested in the city.

Sensor-Based Optimization of Terrestrial Laser Scanning Measurement Setup on GPU

A novel formulation of the set cover problem is presented to find the optimal placement of the scan stations in a terrestrial laser scanning survey. The problem is formulated in 2-D by including sensor-based constraints such as coverage and overlap. The coverage constraint ensures a minimum density of horizontal scan lines on the ground. The overlap constraint enables automatic scan alignment and registration. The optimization problem takes into account both environment occlusions and a maximum allowed incidence angle of the laser beams. The adopted laser model includes fixed parameters such as laser height, angular resolution, field of view, and minimum and maximum sensor range. The sensor placement problem is solved using a numerical approach implemented on graphics processing unit (GPU). Thanks to the GPU acceleration, experiments have been performed in large-scale environments with internal structures.

Approximate Algorithms for 3-D Placement of IBFD Enabled Drone-Mounted Base Stations

The future 5G network is expected to provide throughput 1000 times and spectrum efficiency 10 times those of the current 4G network. In-band full-duplex (IBFD) is a promising technique for 5G that can conceivably improve spectrum efficiency. Drone-mounted base stations (DBSs) provide high mobility and high flexibility, and leveraging DBSs to provision services to UEs can facilitate the quality of service of the wireless network and enlarge the coverage area. Therefore, the throughput and the spectrum efficiency can be improved by leveraging IBFD-enabled DBSs. We formulate the drone-mounted base station placement with IBFD communications ( DBSP-IBFD ) problem, which consists of two sub-problems: the joint bandwidth, power allocation and UE association ( joint-BPU ) problem and the DBS placement problem. We first propose an approximate algorithm to solve the joint-BPU problem that provides guaranteed performance. Then, an exhaustive search method is employed to obtain the optimal locations for DBSs. After that, we propose a $\frac{1}{2}(1-\frac{1}{2^{l}})$ -approximation algorithm to solve the DBSP-IBFD problem, where $l$ is the number of simulation runs. Simulation results have demonstrated that the throughput of the proposed approximate algorithm is superior to benchmark algorithms in solving the DBSP-IBFD problem.

GIS-analysis of ground transport accessibility of fire stations at regional scale

Abstract. Geospatial approaches are widely used to organize access and to manage the extinguishing of forest fires globally. Term “transport accessibility” is used in a variety of geographical and economic researches. Assessment of transport accessibility is directly related to the feasibility study to locate the fire stations in a particular region. Location analysis of objects relative to other objects, while taking into account various quantitative and qualitative parameters, is a classical problem solved by geoinformation systems.Present research work is aimed to be used to improve the situation with forest fires in Russia where one of the main asset of operational regional firefighting in the forests is a fire-and-chemical (fire) station. Traditionally station placement is under the responsibility of Russian region to which stations are administratively subordinate. The location of fire station is determined taking into account the species structure of forests, natural fire danger, road infrastructure and some other factors. Irkutsk region, one of the territories with the constant perennial fire danger in the forests, was chosen as a test area.Using this area as a typical example of regional extent, an analysis of fire stations placement then planning the ground movement of fire brigades from a station to the forest fire locations has been carried out. Previously obtained results to create the shortest routes within three-hour accessibility for the fire hazardous seasons 2002–2017 are used. Russian regulatory documents of the forest industry are applied. Thus, topic of GIS analysis serves as a continuation of the study (http://cepl.rssi.ru/wp-content/uploads/2018/11/Podolskaya-E.S.-et.-al..pdf, in Russian) and various aspects of transportation problem are considered on the example of Irkutsk region.We used the following input data: point layer of fire stations, road network (including roads of different classes and forest glades), and archive of forest fires detected using the spectroradiometer MODIS from the Aqua and Terra spacecraft. Additional data were collected from the open regional Internet sources. GIS analysis used ArcGIS ArcMap Desktop extensions such as Network Analyst, Spatial Analyst, and ArcGIS tools like ET GeoWizards (https://www.ian-ko.com/).We have developed and have used a forest fire transport model of ground access by trucks for the Irkutsk region with the spatial arrangement of fire stations, two protection zones and road network. Speed of the forest fire trucks is classified into 5 groups, taking into account the official permissions and road class. Also every segment of road has its attributive data of speed “adjusted” by the elevation value of the ETopo2, an open-access model (https://www.ngdc.noaa.gov/mgg/global/etopo2.html). Taking into consideration these relief data allows to decrease the vehicle’s speed in the mountainous conditions.Based on the regional specifics and available data for a fire hazardous season, the following set of evaluation parameters was proposed, namely:Road network: roads existence, length, density, and configuration;Spatial distribution of detected forest fires;Territory of fire stations servicing.All the listed parameters are interconnected to each other and, in combination, jointly impact the stations ground transport accessibility assessment at the regional level. We have used GIS-analysis methods such as buffering, allocation, and density, as well as geographic and directional distribution. Time frame of analysis is the full fire hazardous season. Undertaken analysis for the forest fires detected within the ground protection zone (archive of 2002–2017) has shown that the fire stations’ distribution was appropriate. The analysis was based on the assumption that stations had the same weight, geographical and transport location of the stations was reviewed in conjunction to the thematical forestry recommendations. To go further in the GIS analysis some characteristics (work force and technical resources) of the weighted stations could be added.Additional factors of influence can be the location of protected areas with their specific access regime, seasonality of road use, forest fire zoning, forestry boundaries, economic criteria, placement of fire stations in the populated area, etc. It is advisable to conduct a fire stations placement analysis as a preparation event before and after the end of the fire-hazardous season to summarize the effectiveness of actions to extinguish forest fires in the region. Practical results of the study can be used as well to prepare the regional forestry development programs and plans.

The effect of new metro stations on local land use and housing prices: The case of Wuhan, China

The metro system is an important means of transportation for urban residents in mega-cities in China, where not all families can afford an automobile and there are policy restrictions on private cars. Numerous empirical studies have investigated the implicit prices of metro stations in China. However, few have systematically examined the influence of the proximity of a metro station on housing prices. More importantly, the potential endogeneity of metro station placement is always neglected, which means that the validity of the reported causal effects of the distance to the metro station on housing prices must be questioned. To investigate whether households value being close to a metro station because closeness generates fundamentally positive externalities, we analyze the main changes in land use around metro stations before and after new metro lines are built, based on high-resolution images of Wuhan in China provided by Google Earth. The results suggest that the opening of new metro stations revitalizes land around suburban stations more than it does around central stations. However, the area around central stations exhibits a greater population increase than does the area surrounding suburban stations before a new line opens. The results for suburban and central stations seem to be paradoxical, suggesting that the cross-sectional regression using the full sample may be misleading. An additional before-and-after evaluation based on second-hand housing sales data in Wuhan indicates that households do value metro station proximity. Using the case of line 6, we show that opening new metro stations significantly increases housing prices in nearby areas. This influence extends to 1600 m from new metro stations. Moreover, second-hand housing within 1600 m of metro stations is 7–14% more valuable than its more remotely located counterparts. These findings suggest that local metro planners in China should not only recognize the importance of the metro for raising land-sector revenue but also focus on the location choice and spatial distribution of metro stations from the perspective of residents' interests.

Electricity rates for electric vehicle direct current fast charging in the United States

While several efforts are promoting a widespread and convenient network of direct current fast charging (DCFC) stations to support electric vehicles, there is limited understanding of the magnitude and variability of the cost of electricity for these applications. This information gap may hinder optimal investing and planning for charging station placement and in turn affect electric vehicle adoption and usage. Here, we assess the electricity cost for different scenarios of DCFC station size and use based on over 7500 commercial and industrial electricity rates available for 2017 across the United States. Results show that the cost of electricity for DCFC varies dramatically, ranging from less than $0.10 to over $2 per kilowatt-hour, depending on station design and high uncertainty in use. The main driver of cost is low utilization, which results from a combination of few charging events and limited energy recharged during each event. Low utilization leads to significantly higher electricity cost, particularly for rates with demand charges; however, cost decreases rapidly as utilization increases. For high-utilization stations, selecting rates with demand charges can actually reduce electricity costs compared to non-demand-charge rates. Moreover, significant opportunities for cost savings based on existing rates include preferential charging during off-peak hours and limiting multi-plug station power so that not all plugs can be used simultaneously at maximum power.

An Efficient Energy Saving Sink Selection Scheme with the Best Base Station Placement Strategy Using Tree Based Self Organizing Protocol for IoT

Wireless sensor networks using sink node increases the effectiveness in collecting data from various detecting and sensing fields. Sink node acts as an interface between the sensing field and the user agents. The data collected by sensor nodes is send to the sink nodes. The sink is base station, which collect and process data in the centralized mode. So base station is a special node which has unlimited energy and is active all the time. Placement of the base station will have great impact on the energy consumption and lifetime of WSNs. This paper proposes an efficient sink selection scheme (ES3) with proper ase station placement, for sensor network of IoT. In ES3 two kinds of nodes exist (1) network node, (2) non-network node. Network nodes involve in transmission of packets from sensing area to the base station. Non-network nodes will be waiting to join the sensing field and to select proper sink node after receiving broadcast packets. based on the placement of the base station, residual energy of network nodes, distance between the sensor node and the sink node, selection of sink node by each sensor node, routing structure is created. By considering 100 sensor nodes in NS2 a simulation is carried out to evaluate ES3. Simulation results proved that the ES3 will be more energy saving when the base station is placed in the middle of the sensing field. By using performance metrics like, average energy consumption of network, delay in processing packet, dropping ratio of the packet, jitter, packet delivery ratio, residual energy of network nodes, throughput an analysis is done using two scenarios, when the base station is placed in middle and in outskirts of sensing field.

A Comparison of Different Multi-Criteria Analyses for Electric Vehicle Charging Station Deployment

Scarcity in near future, and the price fluctuation of fossil fuels lead to many industries change their products that use these resources into more eco-friendly versions. One of the most recent and noticeable example of this situation is the automobile industry. As a result of this, electric vehicles have started to become more popular and their sales increase substantially. Since these electric vehicles have a limited range for transportation before they require charging, the number and the positions of charging units become very important. In this study, we consider a electric vehicle (EV) charging station (CS) placement problem which aims to maximize users’ utility. To deal with these problem, we survey to find the weights of criterion, which are selected as accessibility, traffic convenience and waiting time calculated by AHP method and VIKOR and TOPSIS methods are used to evaluate each alternative. As a main tool for charging station deployment, an integer programming model, which maximizes electric vehicle driver’s utility in term of evaluation results, is developed and the effects of different evaluation techniques on deployment procedure is shown and compared.

Optimal Relay Station Placement with Non-uniform User Distribution

An relay station (RS) is a low-cost alternative to upgrading a conventional communication infrastructure. It can cope with ever-increasing wireless traffic demand and eliminate shadow areas efficiently. Accordingly, relay-based networking is a key technology for the next generation wireless communication infrastructure. In a relay-based network, the signal to interference plus noise ratio (SINR) and the throughput of the mobile stations (MSs), especially those located at cell edges, heavily depend on the location of the RSs. Hence, it is important to determine the optimal RS locations. In this paper, we address an anomaly which causes a serious fairness problem among MSs when the RS locations are determined only by throughput maximization. Moreover, RS locations were considered for a non-uniform geographical user distribution environment, because in the real world users are not uniformly distributed. We propose a utility optimal RS placement scheme based on a genetic algorithm (GA), which considers both the fairness problem and the non-uniform user distribution. An extensive simulation study verified that the proposed scheme extends cell coverage and maximizes utility while guaranteeing fairness among MSs.

Mobile network synthesis strategy

Introduction:Effective synthesis of а mobile communication network includes joint optimisation of two processes: placement of base stations and frequency assignment. In real environments, the well-known cellular concept fails due to some reasons, such as not homogeneous traffic and non-isotropic wave propagation in the service area.Purpose:Looking for the universal method of finding a network structure close to the optimal.Results:The proposed approach is based on the idea of adaptive vector quantization of the network service area. As a result, it is reduced to a 2D discrete map split into zones with approximately equal number of service requests. In each zone, the algorithm finds such coordinates of its base station that provide the shortest average distance to all subscribers. This method takes into account the shortage of the a priory information about the current traffic, ensures maximum coverage of the service area, and what is not less important, significantly simplifies the process of frequency assignment.

Surrogate Modeling for Capacity Planning of Charging Station Equipped With Photovoltaic Panel and Hydropneumatic Energy Storage

Abstract Due to the promising potential for environmental sustainability, there has been a significant increase of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) in the market. To support this increasing demand for EVs and PHEVs, challenges related to capacity planning and investment costs of public charging infrastructure must be addressed. Hence, in this paper, a capacity planning problem for charging stations is developed and aims to balance the current capital investment costs and future operational revenue. The charging station is assumed to be equipped with the solar photovoltaic (PV) panel and an energy storage system, which could be electric battery or recently invented hydropneumatic energy storage (ground-level integrated diverse energy storage (GLIDES)) system. A co-optimization model that minimizes investment and operation cost is established to determine optimal solution while considering capacity planning and following operations. EV mobility is modeled as an Erlang-loss system. Meanwhile, stochastic programming is adopted to capture uncertainties from solar radiation and charging demand of EV fleet. To provide a more general and computationally efficient model, main configuration parameters are sampled in design space and then fixed in solving the co-optimization model. Sampled parameters include EV charging slots number, PV area, capacity of energy storage system, and daily mean EV arrival number. Based on the sampled parameter combinations and its responses, black-box mappings are then constructed using surrogate models, which could provide insights for charging station placement in different practical situations. The effectiveness of the proposed surrogate modeling approach is demonstrated in numerical experiments. The results indicate better profit advantage of GLIDES over battery system with the increased power capacity

SRC

In this work, we propose a novel system, called Route Planning Maker (RPM) to help the government or transportation companies to design new route services in the city. The RPM system has a flexible user interface that allows users design the nearby areas of a new route and further deploying new stations. Moreover, based on user-designed arbitrary transportation routes and the expected locations of stations, the RPM system provides an intelligent function to infer passenger flows in certain time intervals so that the user can estimate the effectiveness of designed routes. To capture the spatial-temporal factors correlated with passenger flows, we propose to combine dynamic features such as human mobility, passenger volume of existing routes, and static features, including road network structure, point-of-interests (POI), station placement of existing routes and local population structure. Finally, to combine these features, we modified Deep Neural Network (DNN) for regression to derive the passenger flow for each given designated route. The experiments on the Tainan's bus-ticket data outperform baseline methods for 75%.

Power Level Assignment and Base Station Placement Using Simulated Annealing for 4G-LTE Femtocell Networks in Multi-floor Buildings

Power Level Assignment and Base Station Placement Using Simulated Annealing for 4G-LTE Femtocell Networks in Multi-floor Buildings

Do new bike share stations increase member use: A quasi-experimental study

As the number of bike share programs across the world has grown, studies of bike programs and operations have proliferated. Most empirical studies of bike share demand have included analyses of station use while a limited number of studies have investigated member behavior. Moreover, a limitation of this research is that the most research designs have been cross-sectional and therefore unable to establish causality. To address this limitation, we employ a quasi-experimental, difference-in-difference modeling approach using a six-year panel data set of members’ bike share trips from 2010 to 2015 in Minneapolis-St. Paul, Minnesota. This research design takes advantage of changes in the bike share network over time to establish treatment and control groups and test the significance of effects of changes in accessibility on the frequency of individual member’s use of bike share. Improvements in accessibility are measured as a reduction in distance to stations resulting from placement of new stations or relocation of old stations. We find a significant negative impact of distance on the frequency of use and that the effects of increasing bike share accessibility are larger in areas with denser bike share services. Specifically, members for whom access improved (i.e., distance decreased) were significantly more likely to increase the frequency of use than members for whom access remained the same. Moreover, by developing different models, we show the effects of distance are heterogeneous and vary with different built environment contexts. Members who live in areas with higher population density and a higher percentage of retail land use tended to increase their bike share use more. Our results indicate that improvements in physical accessibility may not result in practically meaningful changes in the frequency of use in all cases and imply that multi-faceted strategies for increasing use may be needed.

Future UAV-Based ITS: A Comprehensive Scheduling Framework

Multi-rotor unmanned aerial vehicles (UAVs) have been recently recognized as one of the top emerging technologies to be utilized in various smart city domains such as intelligent transportation systems (ITS). They represent an innovative mean to complement existing technologies to surveil transportation network, control traffic, and monitor incidents. The UAVs usually operate for time-limited missions due to their limited battery capacity. Hence, they need to frequently return to their docking stations to recharge their batteries, which handicaps their mission coverage and performance. When designing a UAV-based ITS infrastructure, it is crucial to leverage the UAV fleet effectively. In this paper, a generic management framework of UAVs for ITS applications is developed. The problem of docking/charging station placement is first investigated to find optimized locations for a given number of stations to be installed by the ITS operator. To this end, two fundamental criteria are taken into account: i) the flying time required by the UAV to reach the mission/incident location and ii) the risk of battery failure during the UAV operation. The two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are designed for this purpose. Once the docking stations are placed, a UAV scheduling program is formulated to optimally cover the pre-known missions while minimizing the total energy consumption of the fleet and respecting a coverage efficiency targeted by the ITS operator. The proposed proactive scheduling approach employs multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in the geographical area of interest over a long period.

Base station placement optimisation using genetic algorithms approach

The base station (BS) placement, or planning cell problem, involves choosing the position and infrastructure configuration for cellular networks. This problem is considered to be a mathematical optimisation problem and will be optimised in our study using genetic algorithms. The various parameters such as site coordinates (x, y), transmitting power, height and tilt are taken as design parameters for BS placement. This paper takes signal coverage, interference and cost as objective functions and handover, traffic demand and overlap as a very important constraint. Receiving field strength testing services for all items is calculated using simulations and path loss is calculated using Hata model. Assuming that a flat area is considered, the performance of the proposed algorithm was evaluated with 97% of the users in the network being covered with a good quality signal.

Charging Station Placement for Electric Vehicles: A Case Study of Guwahati City, India

The ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This paper formulates and solves the charging infrastructure-planning problem for Guwahati, India, that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics, such as voltage stability, reliability, power loss, as well as EV user’s convenience, and random road traffic. The placement problem was solved by using a Pareto dominance-based hybrid algorithm amalgamating chicken swarm optimization (CSO) and the teaching learning-based optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.

Autonomous Base Station Placement for Localization of the GNSS Interference Source

This paper presents the control strategy of autonomous base station placement for localization of the GNSS interference source. The proposed algorithm deals with the optimization of the base station trajectory for target motion analysis based on bearing only tracking problem. The control strategy of the proposed algorithm is designed to maximize a cost function which is generally a functional of the Fisher information matrix. Compared to the optimal control methods, the proposed algorithm is easy to be designed and implemented, and constraints of multiple base stations’ trajectories can be effectively included. In addition, the proposed algorithm also considered target’s dynamics that is both uncertain and random, and there are multiple base stations for observing the target. In order to verify the performance of the proposed algorithm, simulation was performed with dynamic target case in the 2D scenario. It is assumed that the base stations’ networks have non-fully connected topology. According to the simulation results, it was confirmed that the proposed algorithm presents a flexible control strategy of autonomous multiple base stations’ placement for bearing only target tracking system.