Search Results (91)

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development. Full article

Stormwater runoff containing road deicing salts has led to the increasing salinization of surface waters in northern climates, and urban municipalities are increasingly being mandated to manage stormwater runoff to improve water quality. We assessed chloride concentrations in runoff from late-winter snowmelt and rainfall events flowing into an urban Minnesota, USA, lake during two different years, predicting that specific stormwater drainages with greater concentrations of roadways and parking lots would produce higher chloride loads during runoff than other drainages with fewer impervious surfaces. Chloride levels were measured in runoff draining into Lake Winona via 11 stormwater outfalls, a single channelized creek inlet, and two in-lake locations during each snowmelt or rainfall event from mid-February through early April in 2021 and 2023. In total, 33% of outfall runoff samples entering the lake collected over two years had chloride concentrations exceeding the 230 ppm chronic standard for aquatic life in USA surface waters, but no sample exceeded the 860 ppm acute standard. Chloride concentrations in outfall runoff (mean ± SD; 190 ± 191 ppm, n = 143) were significantly higher than in-lake concentrations (43 ± 14 ppm, n = 25), but chloride levels did not differ significantly between snowmelt and rainfall runoff events. Runoff from highway locations had higher chloride concentrations than runoff from residential areas. Site-specific chloride levels were highly variable both within and between years, with only a single monitored outfall displaying high chloride levels in both years. There are several possible avenues available within the city to reduce deicer use, capture and treat salt-laden runoff, and prevent or reduce the delivery of chlorides to the lake. Full article

This paper aims to create an innovative approach to improving IoT-based smart parking systems by integrating machine learning (ML) and Artificial Intelligence (AI) with mathematical approaches in order to increase the accuracy of the parking availability predictions. Three regression-based ML models, random forest, gradient boosting, and LightGBM, were developed and their predictive capability was compared using data collected from three parking locations in Skopje, North Macedonia from 2019 to 2021. The main novelty of this study is based on the use of autoregressive modeling strategies with lagged features and Z-score normalization to improve the accuracy of regression-based time series forecasts. Bayesian optimization was chosen for its ability to efficiently explore the hyperparameter space while minimizing RMSE. The lagged features were able to capture the temporal dependencies more effectively than the other models, resulting in lower RMSE values. The LightGBM model with lagged data produced an R² of 0.9742 and an RMSE of 0.1580, making it the best model for time series prediction. Furthermore, an IoT-based system architecture was also developed and deployed which included real-time data collection from sensors placed at the entry and exit of the parking lots and from individual slots. The integration of ML, AI, and IoT technologies improves the efficiency of the parking management system, reduces traffic congestion and, most importantly, offers a scalable approach to the development of urban mobility solutions. Full article

Urban greenspaces, such as parks and other public vegetated spaces, provide respite from the built environment for residents and visitors. Lesser-known urban greenspaces are community-managed open spaces (CMOSs), such as play lots, community gardens, and memorial gardens. This study investigated the effect of the distance to and size of parks and CMOSs on residential house prices in Baltimore, MD, in 2016–2017 using a hedonic price model. This is the first study of an urban city comparing parks and CMOSs. The study included 21,116 houses sold and revealed that park proximate price premiums ranged from 7.73% to 11.01% for distances of up to a 1/2 mile, and the CMOS proximate price premiums were 8.69% and 8.96% for distances of up to 1/8 and 1/4 miles, respectively. Moreover, both parks and CMOSs revealed a buyer preference of a 1/8 to 1/2 mile distance from these urban greenspaces. Small- to medium-sized parks, less than 9.65 acres, increased house prices by approximately 2.36%, and small CMOSs, less than 0.24 acres, increased house prices by 5.93%. These results confirm that parks and CMOSs provide economic benefits in addition to their social, health, and well-being benefits and suggest that CMOSs are a viable economic development strategy for communities. Full article

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or parking management systems in real time, which has a significant impact on improving urban parking efficiency, alleviating traffic congestion, optimizing driving experience, and promoting the development of intelligent transportation systems. This paper firstly describes the research significance of parking space detection technology and its research background, and then systematically reviews different types of parking spaces and detection technologies, covering a variety of technical means such as ultrasonic sensors, infrared sensors, magnetic sensors, other sensors, methods based on traditional computer vision, and methods based on deep learning. At the end of the paper, the article summarizes the current research progress in parking space detection technology, analyzes the existing challenges, and provides an outlook on future research directions. Full article

Electric vehicles (EVs) help reduce transportation emissions. A user-friendly charging infrastructure and efficient charging processes can promote their wider adoption. Low-power charging is effective for short-distance travel, especially when vehicles are parked for extended periods, like during daily commutes. These idle times present opportunities to improve coordination between EVs and service providers to meet charging needs. The present study examines strategies for coordinated charging in workplace parking lots to minimize the impact on the power grid while maximizing the satisfaction of charging demand. Our method utilizes a heuristic approach for EV charging, focusing on event logic that considers arrival and departure times and energy requirements. We compare various charging management methods in a workplace parking lot against a first-in-first-out (FIFO) strategy. Using real data on workplace parking lot usage, the study found that efficient electric vehicle charging in a parking lot can be achieved either through optimized scheduling with a single high-power charger, requiring user cooperation, or by installing multiple chargers with alternating sockets. Compared to FIFO charging, the implemented strategies allow for a reduction in the maximum charging power between 30 and 40%, a charging demand satisfaction rate of 99%, and a minimum SOC amount of 83%. Full article

Nowadays, enhancing campus environments through mitigations of air pollutions is an essential endeavor to support academic achievements, health, and safety of students and staffs in higher educational institutes. In laboratories, pollutants from welding, auto repairs, or chemical experiments can drastically degrade the air quality in the campus, endangering the respiratory and cognitive health of students and staffs. Besides, in universities in Indonesia, automobile emissions of harmful substances such as carbon monoxide (CO), nitrogen dioxide (NO₂), and hydrocarbon (HC) have been a serious problem for a long time. Almost everybody is using a motorbike or a car every day in daily life, while the number of students is continuously increasing. However, people in many campuses including managements do not be aware these problems, since air quality is not monitored. In this paper, we present a real-time air quality monitoring system utilizing Internet of Things (IoT) integrated sensors capable of detecting pollutants and measuring environmental conditions to visualize them. By transmitting data to the SEMAR IoT application server platform via an ESP32 microcontroller, this system provides instant alerts through a web application and Telegram notifications when pollutant levels exceed safe thresholds. For evaluations of the proposed system, we adopted three sensors to measure the levels of CO, NO₂, and HC and conducted experiments in three sites, namely, Mechatronics Laboratory, Power and Emission Laboratory, and Parking Lot, at the State Polytechnic of Malang, Indonesia. Then, the results reveal Good, Unhealthy, and Dangerous for them, respectively, among the five categories defined by the Indonesian government. The system highlighted its ability to monitor air quality fluctuations, trigger warnings of hazardous conditions, and inform the campus community. The correlation of the sensor levels can identify the relationship of each pollutant, which provides insight into the characteristics of pollutants in a particular scenario. Full article

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed. Full article

The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery’s SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system’s responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption. Full article

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots. Full article

Ferronickel slag (FNS) is a byproduct produced during ferronickel alloy manufacturing, primarily used in the manufacturing of stainless steel and iron alloys. This material is produced by cooling molten slag with water or air, posing significant disposal challenges, as improper storage in industrial yards can lead to environmental contamination. This study investigates the chemical and mineralogical characteristics of reduction ferronickel slag (RFNS) and its potential use as an alternative aggregate in hot mix asphalt (HMA). The research is based on the practical application of HMA containing RFNS in an experimental area, specifically the parking lot used by buses transporting employees of Anglo American, located at the Codemin Industrial Unit in Niquelândia, Goiás, Central Brazil. Chemical analysis revealed that RFNS primarily consists of MgO, Fe₂O₃, and SiO₂, which are elements with minimal environmental impact. The lack of significant calcium content minimizes concerns about expansion issues commonly associated with calcium-rich slags. The X-ray diffractogram indicates a predominantly crystalline structure with minerals like Laihunite and Magnetite, which enhances wear and abrasion resistance. HMA containing 40% RFNS was tested using the Marshall methodology, and a small experimental area was subsequently constructed. The HMA containing RFNS met regulatory specifications and technological controls, achieving an average resilient modulus value of 6323 MPa. Visual inspections conducted four years later confirmed that the pavement remained in excellent condition, validating RFNS as a durable and effective alternative aggregate for asphalt mixtures. The successful application of RFNS not only demonstrates its potential for local road paving near industrial areas but also underscores the importance of sustainable waste management solutions. This research highlights the value of academia–industry collaboration in advancing environmentally responsible practices and reinforces the contribution of RFNS to enhancing local infrastructure and promoting a more sustainable future. Full article

In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot. Full article

Nowadays, the internet of electric vehicles (IoEV) has opened many new opportunities for various applications such as charging station selection, charging/discharging management, as well as supporting various end-user services. In Chile, the current deployment of charging station networks is still at an early stage and such stations do not support the required local and global communication and monitoring capabilities that allow the integration of such services. The underlaying communication infrastructures will play an important role in supporting different applications, such as grid-to-vehicle, vehicle-to-grid, and vehicle-to-vehicle services. This work developed an IoEV architecture for real-time monitoring of charging station networks, which consists of three layers: the physical layer, the communication network layer, and the virtual layer. In order to support reliable IoEV communications, different requirements for data rate, reliability, latency, and security are needed. We developed a communication network model for charging stations based on the IEC 61850-90-8 standard. The performance of the developed architecture has been evaluated considering different real scenarios including a standalone charging station, a group of charging stations in a university campus parking lot, and charging stations in a city. The performance of the communication network has been evaluated with respect to end-to-end latency. Full article

Building information modeling (BIM) has been used by the architectural and engineering disciplines to streamline the building design, construction, and management process, but there has been much more limited experience in extending the application to landscape design and implementation. This study integrated BIM software (Autodesk InfraWorks 2024.1) with a dynamic, process-oriented, conceptual hydrologic/hydraulic model (PCSWMM 2023, version 7.6.3665) to enhance the analytical tools for sustainable landscape design. We illustrate the model integration through a case study that links an existing nature-based solution (NbS) development, the PTT Metro Forest Park, Bangkok, Thailand, with theoretical new-build NbS for an adjacent property. A BIM school building was virtually situated on an empty lot beside the Metro Forest Park and seven NbS scenarios were run with design storms having 2-year, 5-year, and 100-year return intervals. The combination of a rain garden, permeable pavement, a retention pond, and a green roof was effective in sustainably managing runoff from the theoretical new-build site discharging to the Metro Forest. NbS design characteristics such as rain garden substrate depth and green roof area were optimized using the hydrologic/hydraulic model. Model results showed that even with the 100-year rainfall event, the existing Metro Forest pond storage capacity was sufficient so that flooding on the property would not occur. The consideration of connectivity between NbS features is facilitated by the modeling approach, which is important for NbS planning and assessment at a regional scale. Full article

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model. Full article