Station Location Research Articles

Optimal Location and Sizing of Hybrid Photovoltaic Public Charging Stations in Reconfigurable Feeders Using Levy Flight Honey Badger Algorithm

Optimal Location and Sizing of Hybrid Photovoltaic Public Charging Stations in Reconfigurable Feeders Using Levy Flight Honey Badger Algorithm

Research on Chinese Fire Station Optimal Location Model Based on Fire Risk Statistics: Case Study in Shanghai

With the rapid development of urbanization in China, the gap between urban and rural areas is decreasing. The traditional approach of constructing fire stations based on urban built-up areas is no longer suitable for the needs of modern fire rescue. Therefore, a comprehensive fire station location model is proposed based on fire risk assessment. This method divides the protected area units based on the urban road network. By evaluating different regions based on spatial position, land attributes, population density, floor area ratio, and fire incident indicators, the fire rescue risk levels and categorize regions into four risk levels are assessed. Corresponding response times were determined, and an objective model was developed to maximize the coverage area for fire response. The Baidu API was utilized to accurately calculate driving distances and times, and Gurobi optimization software was used to solve the model. Taking Shanghai as an example, the fire station location and layout from two perspectives—re-planning based on overall station placement and re-planning based on existing stations—were analyzed. The results suggest that constructing around 150 fire stations in Shanghai would effectively meet the fire rescue needs, which aligns with the actual situation in Shanghai and demonstrates the strong applicability of this model. This approach enables the meeting of new demand for fire station construction due to the significant increase in the coverage area while effectively utilizing firefighting resources.

Optimizing location-routing and demand allocation in the household waste collection system using a branch-and-price algorithm

In catering to the rapid development of urbanization and the growing urban population, it is essential to design an efficient and effective household waste collection system for guaranteeing a favorable ecological environment in cities. This paper focuses on a location-routing and demand allocation problem (LRDAP) in the household waste collection system where the household waste is initially allocated and transported to a set of intermediate transfer stations using a category of small garbage vehicles, and subsequently a fleet of large garbage vehicles visits each transfer station and transports the collected household waste to the waste disposal center for harmless treatment. The purpose is to simultaneously determine the location of transfer stations, the demand allocation of household waste collection and the routing plan of garbage vehicles while maximizing the efficiency of household waste collection within the whole system. Then, we formulate the proposed LRDAP into a mixed integer program. To solve our LRDAP, an exact branch-and-price (B&P) algorithm is developed along with several acceleration strategies involving column generation (CG) stabilization and heuristic dynamic programming (DP). Finally, we illustrate the proposed model and solution methodology by dealing with real-world problem instances with respect to household waste collection in China. Computational results are presented that explore the performance of our proposed B&P algorithm, analyze the optimized location-routing and demand allocation plan, discuss the impact of selected parameters, and gain some managerial insights in designing an efficient and effective household waste collection system under different decision-making conditions.

Location selection for offshore wind power station using interval-valued intuitionistic fuzzy distance measure-RANCOM-WISP method

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment.

Estimation of pollutant baseline concentration in Delhi city: An opportunity from coupled effect of rainfall scavenging and lockdown restriction

The accurate measurement of pollutant baseline concentration is a crucial step for robust assessment, prediction and forecasting of air pollution in any region, especially in metro cities that witness extreme high level of pollution. The present study is an attempt to fill this gap and derive the baseline concentrations of two critical air pollutants, i.e., PM2.5 and NOx in Delhi city. The study critically analyzes the data of winter period having consistent rainfall followed by complete weekend lockdown due to COVID-19. The findings indicate that the estimated baseline concentration is in the range of 20–36 μg/m3 and 12–63 μg/m3 for PM2.5 and NOx (NO + NO2), respectively during winter period of year 2022. The estimated baseline levels can be reasonably well compared with other reported values, both in the past studies as well as estimated using other referred methods. The baseline values are found higher in central areas of the city compared to outskirt located stations. The estimated baseline concentrations can be used in models for predicting the actual urban increment (actual increase in the source contribution with respect to the baseline), which will further help in decision making of target-oriented control actions.

Re-envisioning the Park-and-Ride concept for the automated vehicle (AV) era with Private-to-Shared AV transfer stations

Cities implemented park-and-ride (PNR) systems to decrease congestion in dense urban areas while providing transit options to travelers who live in a city's low- to medium-density regions. The success of PNR systems is mixed, as they suffer from several disadvantages, namely, the uncertainty of parking locations and infrequent and/or unreliable transit services, and the fact that travelers still need to walk to their destination. Motivated by the premise of PNR systems and the potential of automated vehicles (AVs), to address each of the shortcomings of PNR systems, this study proposes a future system with near-ubiquitous AVs where travelers transfer from privately owned AVs (PAVs) to shared-use, shared-ride AVs (SAVs), called a PAV-SAV transfer system. This study proposes a modeling framework to assess the potential market share of the PAV-SAV transfer system and the network impacts (e.g., congestion, vehicle miles traveled) of the proposed system. Finally, the study identifies good designs for the PAV-SAV transfer system using scenario analysis. The critical design variables are the location of transfer stations, the capacity of SAVs, and the transfer station connector links. For the Greater Los Angeles area, the computational results show a market share for PAV-SAV of almost 18% for person trips terminating in downtown Los Angeles. In all scenarios, the proposed PAV-SAV system decreases vehicle hours traveled (VHT) across the whole network with significant decreases in the urban core. For all designs, the PAV-SAV system decreases vehicle miles traveled (VMT) compared to a network without PAV-SAV transfer stations, albeit only slightly. Locating transfer stations closer to the urban core, increasing vehicle capacities, and connecting transfer stations to both arterial links and highway links improves network performance (i.e., VMT and VHT) and increases the market share of the PAV-SAV system.

Obstacles to Electric Vehicle Adoption in India and Proposed Remedies

The inductive approach focuses on analyzing the factors influencing Electric Vehicle (EV) adoption in India, while the deductive approach centers on understanding how these factors play out in developed nations and their applicability to developing countries. By iteratively applying the inductive-deductive approach, a taxonomy is developed categorizing these factors into micro-, macro-, and meso-level antecedents. This taxonomy serves as a framework for systematically organizing cohesive initiatives to promote EV adoption in developing nations, stressing the importance of tailoring these factors to the unique infrastructural, economic, and market conditions of such countries. One significant challenge hindering the adoption of EVs is the concern over their limited driving range. Recent proposals suggest the implementation of dynamic wireless charging, enabling power exchange between vehicles and the grid while in motion. This paper highlights the importance of optimizing EV routes requiring charging to maximize the utilization of Mobile Energy Disseminators (MEDs) functioning as mobile charging stations. The growing popularity of EVs has brought about a corresponding increase in challenges. Lengthy waiting times at charging stations present a major hurdle to widespread EV adoption. To address this, battery swapping stations (BSSs) offer an efficient solution, reducing wait times and promoting healthy recharging cycles for batteries. Additionally, these swapping stations create opportunities not only for EVs but also for power systems, providing regulation services to the grid, particularly beneficial for smaller networks like microgrid (MG) systems. This study delves into determining the optimal location and size of swap stations to maximize revenue within an MG system.

Optimizing locations of emergency medical stations for rural areas: A case study in Iran

Given the dearth of emergency medical services in rural areas, offering these services is among the most important things that should be done right to save the lives of people who have suffered severe injuries. Rapid access to EMSs, which includes a shorter response time and the availability of all intensive care services, including the ICU, CCU, NICU, and PICU, is crucial in this regard. In this regard, a GIS-based ACO-QAP model is proposed for rural areas to optimize locations for EMS stations. This model takes into account the standards of EMS in rural areas, and the standards of out-of-city roads, reducing the response times from EMS stations to the scene of damage and from the damage scene to the closest hospital that offers the required intensive care services. Additionally, to increase the availability of intensive care services and to optimal use of available resources, the model determines which types of intensive care services should be provided in the EMS stations. As a result of the integration for setting up the out-of-city EMS, 614 optimized locations were identified. The identified EMS stations should provide single or multiple intensive care services. There were identified as 544 (88.6%) multiservice stations and 70 (11.4%) single service stations. Among the multiservice stations, 113 (20.8%) stations were identified as two-service, 150 (27.5%) as three-service, and 281 (51.7%), as four-service stations. This model generates a chain of EMS stations that improves spatial accessibility and emergency medical availability while reducing resource usage.

Effects of some weather variables on the signal strength of Maloney FM radio, Nasarawa State, Nigeria

This study aims to assess the effects of atmospheric temperature, relative humidity, and atmospheric pressure on the signal strength of Maloney FM Radio, 95.9 MHz, Keffi, Nasarawa State. It is essential to measure the signal strength of the new radio station in relation with some parameters of weather to provide effective service delivery to the critical sectors and the entire populace. The research was conducted using the data of atmospheric temperature, atmospheric pressure, relative humidity, and the strength of the Maloney radio signal acquired from 6.00 a.m. to 6.00 p.m. daily at a 30 min interval, from January 2023 to June 2023, in Karshi, Abuja, a neighbouring town to Nasarawa state where the radio station is located. The data analysis involving cointegration and descriptive statistics of the effects of atmospheric variables on the radio signal strength was implemented using Eviews and Microsoft Excel Software. The results showed daily and monthly variations of the radio signal strength as well as the atmospheric temperature, atmospheric pressure and relative humidity. The variation is such that the lowest monthly signal strength was in June, probably due to higher rainfall in the area in June being the last month in this work (Jan.–Jun.) compared to other months which are dry season (January–March) or when the rain was just starting (April–May), in the study area. It was observed that atmospheric temperature and pressure negatively correlate with signal strength while relative humidity positively correlates with received signal strength in all the six months under study. The cointegration analysis also showed a long-term relationship between the radio signal strength and the atmospheric components, as the probability on the first row on the tables of both Trace Test and Maximum Eigenvalue methods were 0.0016 and 0.0008, which are less than the 5% (0.05) set critical value. In the same way, the critical value results on the same first row in both methods are smaller than the Trace statistics values. This implies that there is a long-term relationship between the atmospheric variables and the Maloney FM radio signal strength. These results will help mitigate the attenuation effect, and attendant signal loss of the radio station's signal strength. By proxy, the results will support the operations and regulations of spectrum management in the design of satellite communications and satellite microwave band specifications in Nigeria. In addition, the pre-determination of the radio station location will depend on this study's result analyses, considering that the effects of atmospheric temperature, pressure, and humidity conditions are inevitable. Governments, policymakers, stakeholders, and other relevant authorities should ensure the domestication of the study by providing the necessary research materials and facilities for wider studies. As regards the Maloney FM radio station, Keffi, studied in this work, there is no similar or related work done on it before now, hence, providing the detailed relationship between the signal strength of the radio station and some weather variables will go a long way in the improvement of the quality of signal transmission from Maloney radio station. The results when provided to the station, will enable the management to also evaluate if their set target of the station such as the radio horizon, radio signal coverage, and the link budget have been met.

Smart electric vehicle charging for reducing photovoltaic energy curtailment

The increased diffusion of Photovoltaic (PV) generation could be limited by the grid capacity to host the power input from PV systems, especially in mid-day hours on sunny days. This paper proposes a framework of analysis to determine the mitigation of the PV energy curtailment in the grid, based on a novel smart charging strategy for electric vehicles (EVs) that operate in the grid-to-vehicle mode. This framework adopts a combination of methods and techniques, with a clustering procedure to group the EV charging stations with some PV systems based on the electrical distance, a novel smart charging strategy for the EVs based on the definition of fuzzy weights, and the exploitation of the blockchain technology with a Proof-of-Authority consensus protocol to reinforce network security and achieve decentralization. The blockchain provides full traceability of EV charging operations and grid status monitoring during time. The proposed framework contains novel indicators of avoided PV power reduction and energy curtailment. To account for the EV-related uncertainty, these indicators are determined from a Monte Carlo-based assessment of the three-phase power flow results. The results are presented on a three-phase unbalanced 123-node system with given locations of PV systems and EV charging stations.

Optimal Location of EV Charging Stations in the Distribution System Considering GWO Algorithm

Objectives: The transition from conventional vehicles to electric vehicles (EVs) has been significantly influenced by the current scarcity of fossil fuels and the environmental concerns surrounding greenhouse gas emissions. The exploration of the optimal location of electric vehicle charging stations has been prompted by the electrification of the transportation system and the increasing demand for EVs. Methods: One of the main challenges is the detrimental impact of improper positioning of charging stations on the power distribution network. The Grey Wolf Optimization (GWO) approach is used to determine the effective Charging Stations (CS) locations to minimize the losses, which is the major objective of the proposed technique. An IEEE 33 bus system is used to implement the suggested technique. Findings : The proposed method finds the suitable deployments for EVCS within the distribution system, which is of utmost importance due to the constraints imposed by limited battery capacity and prolonged charging periods. This article proposes the optimal location for EVCS to minimize losses inside the distribution network. Novelty: The effectiveness of the proposed GWO is evaluated by comparing it with the PSO optimal algorithm. Finally, a comparison of system voltage and nominal voltage is presented and the probability comparison of EV location with the base case and the proposed algorithms is presented. Keywords: Electric Vehicle Charging Station, Distribution System, GWO, System and Nominal Voltage

The Flow Termini Coverage Model for Locating Bike-Sharing Stations

As of 2022, more than 1,900 cities around the world had implemented bike-sharing systems (BSSs) with docking stations. Because there is a limit on how far most people are willing to walk to and from BSS stations, maximal coverage models have been a popular approach to optimizing dock station locations. Existing maximal coverage models, however, mainly evaluate demand coverage based on proximity to individual stations without considering the fact that BSS users must have dock stations convenient to both their pickup and drop-off sites. Given this, traditional single-node maximal coverage models might not locate stations to maximize the number of complete-able trips and will also tend to overestimate the coverage of demand. This study proposes a flow termini coverage model (FTCM) to locate BSS stations by considering the flow-based BSS usage demands and network connectivity. The model is tested on the Youbike system in Taipei, one of the most heavily used dock-station BSSs worldwide. This study compares the FTCM with both the traditional single-station maximal coverage model and Taipei’s official planning strategy. Results suggest that the FTCM consistently covers more origin–destination trips, especially in areas with unbalanced BSS usage. We also provide discussion on the cost–benefit trade-offs for transportation planners to choose the most appropriate modeling approaches. The FTCM could also be adapted to optimizing micromobility hubs for parking dockless shared bikes and e-scooters.

A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location

This work focuses on a hybrid preference-based electric vehicle charging station location problem, which considers multiple optimization preferences of distribution network operators, charge station owners, and electric vehicle users. The problem is formulated by an uncertain mixed-integer programming model. Due to the multi-fold uncertainty of the charging process, the uncertain model parameters are expressed as Type-2 fuzzy variables (T2-FVs). The critical value-based type reduction method is adopted to handle the high computational complexity. The proposed uncertain model is converted to its equivalent deterministic chance-constrained programming model. The deterministic counterpart is solved by General Algebraic Modeling System (GAMS). At last, numerical simulations are performed to demonstrate the proposed location strategy as well as some sensitivity analyses. The results indicate that for any given parameters, the equivalent deterministic model follows the general form of mixed-integer programming one that can be easily solved by GAMS. The proposed methodology can effectively handle the multi-fold uncertainty of the charging process. Compared with crisp models, the proposed location strategy can provide more robust location decisions for electric vehicle charging stations (EVCSs). In addition, we also found that different interest groups have conflicting preferences for the locations of EVCSs, so considering multiple hybrid optimization preferences is necessary.

Estimation Algorithm to Improve Cellular System Performance by Moving Base Stations locations

Cellular system adopts the idea of frequency reuse, where the frequency bandwidth of each cell is repeated in non-adjacent cell, in order to use the available frequency band efficiently, however, the interference occurs even between the distant cells. The aim of the paper is to reduce the interference between these co-cells.The user receives the signal from its own cell, as well as the signal from the far cell which has the same frequency, the parameter Signal to Interference Ration, SIR, is the main factor to evaluate the performance of the cellular system.In this paper, we propose algorithm to move the locations of base stations of the cells, in order to increase SIR value, decrease Probability of blocking, and number of users out of coverage.

Robust Charging Network Planning for Metropolitan Taxi Fleets

We study the robust charging station location problem for a large-scale commercial taxi fleet. Vehicles within the fleet coordinate on charging operations but not on customer acquisition. We decide on a set of charging stations to open to ensure operational feasibility. To make this decision, we propose a novel solution method situated between the location routing problems with intraroute facilities and flow refueling location problems. Additionally, we introduce a problem variant that makes a station sizing decision. Using our exact approach, charging stations for a robust operation of citywide taxi fleets can be planned. We develop a deterministic core problem employing a cutting plane method for the strategic problem and a branch-and-price decomposition for the operational problem. We embed this problem into a robust solution framework based on adversarial sampling, which allows for planner-selectable risk tolerance. We solve instances derived from real-world data of the metropolitan area of Munich containing 1,000 vehicles and 60 potential charging station locations. Our investigation of the sensitivity of technological developments shows that increasing battery capacities shows a more favorable impact on vehicle feasibility of up to 10 percentage points compared with increasing charging speeds. Allowing for depot charging dominates both of these options. Finally, we show that allowing just 1% of operational infeasibility risk lowers infrastructure costs by 20%. Funding: This work was partially funded by the Deutsche Forschungsgemeinschaft [Project 277991500]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0207 .

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony–genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.

Optimizing electric vehicles charging for enhancing environmental sustainability and reducing carbon emissions of freight transport: case of Czech Republic

The limited infrastructure of charging stations, which is crucial in route planning and total journey time and creates uncertainty in efficiency and operating costs, calls for new economic and statistical methods in sustainability development and environmental economics. This paper aims to examine the challenges of integrating electric vehicles into freight transport to improve distribution logistics’ environmental sustainability, which represents one of the pathways for reducing environmental risk. The analysis results underscore the inadequacy of the truck charging station network in the Czech Republic. This insufficiency presents an opportunity to enhance environmental sustainability and reduce carbon emissions through strategic analysis and optimizing charging station locations. The difficulty of identifying optimal locations for these stations, given truck availability, requires using multi-criteria decision-making techniques such as the Analytical Network Process (ANP). Municipalities with limited access to existing logistics facilities were considered during the simulation. This way, 15 new locations were identified for municipalities with insufficient distance to a charging station.By implementing the ANP method, the study contributes to a more environmentally sustainable transportation infrastructure, highlighting the potential for significant reductions in carbon emissions through improved charging station networks. These results apply to other countries and can provide novel insights on optimizing charging station locations for sustainable economic development and reducing freight transport’s carbon emissions and environmental risks.

The influence of feeding station location on the space use and behavior of reintroduced 'alalā: Causes and consequences

AbstractSupplemental feeding is a common soft‐release strategy for increasing settlement, survival, and breeding in animals after translocation. However, supplemental feeding can also hinder natural patterns of dispersal or influence social interactions. Some drawbacks of feeding can be mitigated if feeding stations can function as management tools for directing animals' movement, for example, by guiding them toward wild food resources or prospective territories while directing them away from potential threats, for example, predator hotspots or urban/agricultural centers. However, these effects may not work equally across individuals or habitat types. For species such as 'alalā (Corvus hawaiiensis), Hawaii's last living corvid, being able to manipulate space use via supplemental feeders could improve reintroduction outcomes. We determined if and how feeding stations influence a released population of 'alalā by strategically moving feeders across varying distances and habitat types, while measuring how quickly birds discovered new feeders and how their space use changed. We found that 'alalā discovered feeders more rapidly in closed as opposed to open canopy habitat, and feeder movement influenced how far 'alalā ranged, especially in afternoon periods. Sex, social network position, and individual home range size did not predict feeder discovery. These insights offer lessons for using supplemental feeding when managing reintroductions.

Agent-based simulation of vehicle-sharing systems

ABSTRACT Agent-based simulation is a powerful tool that allows emulating the complex behaviour of individuals within a system in order to assess its global performance. In this context, this paper develops a modular agent-based simulation framework for being applied to vehicle-sharing systems. The modular structure of the simulator implies that each sub-system is treated separately, so that each module can be replaced or modified without affecting the others. In this way, the simulator can be used to analyse specific problems such as the optimal location of stations, the design of repositioning strategies, or different vehicle usage policies, by focusing only on the relevant module. The simulation framework proposed has been implemented and tested in the analysis of two different systems: a station-based bike-sharing system and a mixed (i.e., station-based and free-floating) car-sharing initiative. The paper shows some of the results obtained from such implementations as an example of its possible applications.

The integrated on-demand bus routing problem: Combining on-demand buses with a high-frequency fixed line public transport network

In this work we analyze the performance of integrating a large-scale on-demand bus system with a high-frequency fixed line public transport network in an urban context. Given are a high-speed metro network, a set of real-time requests, a set of bus station locations and a fleet of fixed capacity minibuses. Requests have a set of possible departure/arrival bus stations within walking distance of the actual departure/arrival location and have to be served within a certain time window. The aim is to simultaneously (1) decide on the trip type for each passenger (only bus, only metro or mixed), (2) route the on-demand buses, (3) in the case of a bus-leg in the trip, assign each passenger to a departure and arrival bus station (bus station assignment), and (4) in the case of a metro-leg in the trip, decide the assigned transfer station(s) and used metro lines (transfer station assignment). We call this problem the integrated on-demand bus routing problem. After presenting a mathematical model, we propose a quick and scalable insertion-based heuristic to solve the problem.The results found by the heuristic are further used to compare the performance of an integrated system, to a system that only uses on-demand buses. It is concluded that the integrated system always performs better regarding the service rate or number of served requests. Depending on the speed and layout of the metro network, also the average user ride time per passenger improves by the integration.