Operation Of Parks Research Articles

Optimal scheduling of zero-carbon park considering variational characteristics of hydrogen energy storage systems

Zero-carbon parks have broad prospects in carbon neutralization. As an energy hub, hydrogen energy storage plays an important role in zero-carbon parks. However, the nonlinear characteristics of hydrogen energy storage systems (HESSs) have a significant impact on the system economy. Therefore, considering the variable working condition characteristics of HESSs, a hybrid operation method is proposed for HESS, to support the efficient and economic operation of zero-carbon parks, by analyzing the operating principle of a zero-carbon park with HESS, the system structure framework and variable condition linearization model of the equipment in HESS are established. Moreover, considering the energy output characteristics of hydrogen energy storage equipment under variable working conditions, a multimodule hybrid operation strategy is proposed for electrolytic and fuel cells, effectively meeting the thermoelectric load demand of zero- carbon parks in different scenarios. Finally, the economy of the proposed hybrid operation strategy was verified in typical scenarios, using a zero-carbon park embedded with a HESS.

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees’ parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees’ trip scheduling, employer’s EP investment decision, and commercial parking operator’s pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees’ departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer’s optimal EP investment decision. A competitive game between employer’s investment decision and commercial parking operator’s parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Predicting and explaining parking space sharing behaviors using LightGBM and SHAP with individual heterogeneity considered

ABSTRACT Shared parking plays a crucial role in alleviating parking pressure, but the heterogeneity of potential suppliers’ intentions was often ignored. This study addresses this gap by adopting an interpretable Machine Learning (ML) framework to investigate parking space sharing intentions, considering individual differences. A survey with 383 respondents from mainland China was conducted, and a Latent Class Model (LCM) identified three distinct groups of potential suppliers. The Light Gradient Boosting Machine (LightGBM), outperforming other ML models, was used to quantify factors influencing sharing behaviors. The SHapley Additive exPlanation (SHAP) approach revealed that influential factors vary across different latent classes. These findings provide insights for shared parking operators to encourage potential suppliers’ participation in shared parking.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master-Slave Game.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master-slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

The nexus between visitor satisfaction and expenditure behaviour in national parks: The case of Dragon Palace National Park, Guizhou, China

Several studies have explored the relationship between visitor expenditure, experience, satisfaction, intention to recommend, and revisit intention in national parks, but there is a gap in exploring the direct relationship between satisfaction and expenditure of national park visitors. This research identifies relationships between expenditure and behaviour patterns in Dragon Palace National Park, a 5A visitor attraction in Guizhou province, China. A structured survey questionnaire was utilised to collect 412 responses from national park visitors, employing convenience sampling techniques from August to December 2022. A partial least squares-structure equation model 4.0 was used to analyse the data. Findings support the positive relationship between experience, satisfaction, intention to recommend, and revisit intention in the national park. Also, results confirm that rational and emotional dimensions effectively measure the experience level. Further, this research confirms that experience is not a predictor of expenditure and that satisfaction has a negative relationship with expenditure by providing a measurement model to examine the experience in the national park. It frames a new theoretical lens on the relationship between expenditure and satisfaction and contributes significance to national park operations, providing a reference for future research. Management implicationsThe negative relationship between satisfaction and expenditure is interesting. The continuance of fostering national park visitor satisfaction for tourists cannot make sense to improve expenditure. The Dragon Palace National Park should pay more attention to overnight visitors' satisfaction and expenditure since they spend more but are less satisfied. It is vital to enhance the consuming willingness of visitors, improving the expenditure of satisfied visitors. Customising the experience, improving the cost performance of various goods and services, and enriching the experience content may work. The dimensions of the experience have been tested as effective, and the measurement model can be applied in other national parks.

“We struggle to survive”: Exploring the whole systems energy injustices of solar photovoltaics in India

Solar photovoltaics (PV) are among the cheapest forms of energy globally and one of the most effective options for mitigating the climate crisis in the electricity sector. In 2023, solar PV accounted for roughly 75 % of renewable power capacity additions globally, and capacity is expected to grow 20-fold by 2050. However, the political ecologies of solar power are uneven. Throughout the value chain, there are numerous social and environmental injustices experienced by laborers and in communities that are converted to sacrifice zones for sustainable development. Despite being the world’s third largest emitter of greenhouse gases, India is developing solar infrastructures at a rapid pace to mitigate the climate crisis. Drawn from a rich collection of original data—household surveys, semi-structured interviews, focus group discussions and naturalistic observation across various sites—in this study, we investigate perceptions by laborers of the hidden injustices of solar energy at multiple nodes of the solar PV lifecycle or value chain: silica mining (Uttar Pradesh), solar panel manufacturing (Karnataka), solar park development (Rajasthan), solar park operation (Rajasthan), e-waste (Delhi) and recycling (Tamil Nadu). We conclude with novel findings and urgent recommendations for future policy and research on India’s solar PV value chain.

High-tech industry development: selection of technology parks operation model elements in the field of high technology

The article presents the justification of model elements choice for technology parks work in the sphere of high technologies in Russia. The methodology for creating the model, which was built on the position of borrowing the best practices of existing models of innovation infrastructure facilities operation and functioning in the country, has been given. It has been proposed to form a rating among operating technology parks, within the framework of which a comprehensive assessment of technology park management companies’ performance has been carried out according to four groups of indicators (sub-indexes) such as subjects investment attractiveness, information openness, technology parks internal ecosystem, and management company efficiency. Based on the rating results, the leaders among technology parks have been identified, and their behavioral models adopted as reference metrics for the model to be developed. Based on the leaders’ position, it has been proposed to choose the best technology parks model in the field of high technologies in terms of their interaction with residents. Recommendations have been presented for the technology parks infrastructural model as the most promising for private companies, while investment projects should be developed considering federal requirements and standards to ensure their successful implementation and achievement of the set goals. Technology parks in the sphere of high technologies play a key role in Russian economy development. They contribute to the development of innovations, new technologies, high-tech industries, and knowledge-intensive business. It is important that such technology parks development is optimally organized with due regard to modern requirements and regulatory measures.

Waiting for the next new ride at Disney world? Understanding the determinants of capital expenditures among theme parks

PurposeThe purpose of this paper is to investigate the determinants of capital expenditures (CAPEX) in the theme park industry, particularly following the financial stresses induced by the COVID-19 pandemic. CAPEX drivers are poorly understood due to the idiosyncratic nature of this industry, which is dominated by a few large players. It also aims to identify the variables influencing both the growth and maintenance components of CAPEX among U.S. theme park operators.Design/methodology/approachThe paper uses contingency theory to analyze both financial and nonfinancial data from U.S. theme park operators between 2009 and 2021. The paper also uses partial least squares structural equation modeling to manage issues of multicollinearity and to ensure robustness in the findings.FindingsThe analysis identifies several key determinants of CAPEX. Resources and the presence of competing theme parks in proximity to an operator’s parks positively affect CAPEX. Conversely, higher leverage, dividend payouts, intellectual property (IP) dominance and population density in areas with their active parks correlate with reduced CAPEX. The paper also notes distinct trends in maintenance versus growth CAPEX post-COVID-19, with maintenance CAPEX increasing as operators invest in existing assets while growth CAPEX trending downwards.Research limitations/implicationsThe study’s scope is confined to U.S.-based theme park operators, limiting the generalizability of the findings internationally. Moreover, data limitations restrict the sample size due to the consolidation of the industry players, potentially affecting the statistical power of the analysis.Practical implicationsThis research offers significant insights for theme park operators, industry analysts and policymakers. Understanding the factors influencing CAPEX can aid operators in strategic planning and investment decisions, especially in a post-pandemic economic environment where efficient capital allocation will be crucial for recovery and growth. A major contribution of this research is the development of a new measure for IP dominance, which allows theme park operators to quantify the impact of IP on their investment strategies.Originality/valueThis study contributes uniquely by incorporating both financial and nonfinancial determinants in analyzing CAPEX within the theme park industry, a sector significantly impacted by the pandemic. It introduces novel metrics for assessing the impact of IP on CAPEX and differentiates between the factors driving maintenance and growth expenditures. The findings enrich the existing literature on hospitality management and provide actionable insights that could guide the strategic financial decisions of theme park operators.

Two-Carrier Cooperative Parking Robot: Design and Implementation

Abstract Parking robots have been designed to alleviate parking stress in crowded areas. Existing models occupy large spaces and have limited adaptability to uneven ground. This paper aims to enhance the performance of parking robots by proposing a leader-follower-control based two-carrier Cooperative Parking Robot (CPR). In this system, two omnidirectional carriers operate on a tight cooperative transporting algorithm to achieve steady motion in their collaborative handling and transportation of the target car into the designated parking space. The novel CPR was designed, modeled, and implemented. The results indicate that the proposed CPR approached the targeted car and maintained a consistent position and heading angle in its cooperative parking operation at the speed of 0.6 m/s. The parking robot exhibited significant improvement in its adaptability to cars and uneven ground, and its compact configuration reduced its space occupation. Therefore, the proposed CPR has been proven robust for autonomous cooperative parking operations.

A smart parking system combining IoT and AI to address improper parking

The world is entering a new era of intelligent parking, with researchers exploring innovative solutions to enhance the parking experience. These solutions aim to simplify parking challenges for drivers, reducing stress caused by traffic congestion and the search for available spaces. This study presents an intelligent parking system driven by artificial intelligence (AI). It addresses inefficient and improper parking, providing drivers with detailed information about available spaces and improving their management. This approach empowers drivers with insights into parking availability, reducing time and fuel consumption. The system streamlines parking operations, ensures accurate spot availability information, and enhances overall efficiency and user satisfaction. The effectiveness of this proposed system is evidenced by a range of outcomes, including significant reductions in the time spent searching for parking and decreased fuel consumption along comparable travel routes.

The Parking Management System

This Parking Management System (PMS) is a comprehensive solution designed to streamline and enhance the efficiency of parking operations. Leveraging advanced technology, the system optimizes the utilization of parking spaces, reduces congestion, and improves overall user experience. Key features include real-time occupancy monitoring, automated payment processing, and smart allocation of available spaces. PMS not only ensures seamless entry and exit for vehicles but also integrates with mobile applications for convenient reservation and navigation. With a focus on sustainability, the system promotes eco-friendly practices by encouraging the use of electric vehicle charging stations and implementing intelligent traffic flow management.In summary, the Parking Management System represents a modern, user-friendly approach to address the challenges associated with urban parking, promoting a more organized and environmentally conscious urban environment. Keywords—Real-time Occupancy Monitoring, Automated Payment Processing

US National Park Service and concession staff perceptions regarding waste management in Yosemite, Grand Teton, and Denali National Parks

Each year, over 45,000 metric tons of waste are generated in US national parks through a variety of means, including park operations, visitation, and other sources. In an effort to address these impacts, the National Park Service (NPS) has partnered with commercial and non-profit organizations to implement the Zero Landfill Initiative (ZLI). The goal of the ZLI is to realize a steady decrease in waste generated in parks, and an increase in materials being sent for recycling. Through this initiative and aligning research, efforts to mitigate waste and recycling issues with visitors is underway; however, to date there have been no attempts to understand the perspectives of those individuals who manage these parks on a daily basis. This study explored Theory of Planned Behavior-based constructs regarding disposal of waste and recycling using surveys with NPS employees and park concession staff in Yosemite, Grand Teton, and Denali National Parks. Results indicate that perceived difficulty and moral norms related to disposal of waste and recycling are significant drivers of self-reported behavior and intent with NPS and concession staff. Generally, concession staff perceptions align with the goals of ZLI more than those of NPS staff. This research adds to the limited understanding of land mana­ger perceptions, and results provide justification for future messaging and trainings that could improve sustainable management of these and other NPS units in the future. Keywords: Park Management; Visitor Use; Recycling; Waste Management; Communication; Human Behavior; Leave No Trace

Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision

Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety.

Smart Vehicle Parking Facility and Monitoring System Using ML

In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System designed to address the challenges of parking management without the need for sensors or IoT technology. This system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.In response to the exponential growth of vehicles in the last two decades, we propose a Machine Learning-based Smart Parking System implemented as a web application using HTML, CSS, and JavaScript, without the need for sensors or IoT technology. This web-based system leverages cloud computing and a cyber-physical framework to streamline parking operations, providing real-time information to users about parking slot availability, efficient management of reserved and unreserved slots, detection of anomalies, and intelligent traffic management. With a user-friendly interface, the system minimises human intervention, resulting in time, cost, and energy savings, offering an enhanced and efficient solution for urban parking management.

Quantifying and evaluating strategies to decrease carbon dioxide emissions generated from tourism to Yellowstone National Park

The tourism industry needs strategies to reduce emissions and hasten the achievement of global carbon dioxide (CO2) emission reduction targets. Using a case study approach, we estimated CO2 emissions related to park tourism in Yellowstone National Park (USA) generated from transit to and from the park, transit within the park, accommodations, and park operations. Results indicate tourism to Yellowstone National Park produces an estimated 1.03 megaton (1.03 billion kg) of CO2-equivalent emissions annually, with an average of 479 kg CO2 per visitor. Almost 90% of these emissions were attributable to transit to and from the destination, while 5% were from transit within the park, 4% from overnight accommodations, and about 1% from other park operations (e.g., visitor centers, museums, shops, restaurants, etc.). Visitors who fly only made up about 35% of all visitors, but produced 72% of the emissions related to transit to and from the park. Future scenarios that alter transit to and from the park can reduce emissions the most; this includes a greater proportion of local or regional visitors, fewer visitors flying, and increased fuel efficiency of vehicles. The method developed in this work, and applied specifically to Yellowstone National Park, can be adopted elsewhere and used to help decision makers evaluate the effectiveness of potential emission reduction strategies.

High-Precision Map Construction in Degraded Long Tunnel Environments of Urban Subways

In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision.

Low-carbon Urban Parks as a Novel Awareness-raising Medium for the “Dual Carbon” Goals: A Case Study of Beijing Wenyuhe “Future iValley” Park

In the process of promotion of carbon peaking and carbon neutrality strategies in China, it is of great significance to improve the public's awareness and understanding of the content of carbon peaking and carbon neutrality. Against this background, urban low-carbon parks are developing rapidly across the country. This article introduces and analyzes the concepts concerning construction and operation of urban low-carbon parks, and lists the key elements contained in urban low-carbon park. This article also conducts a case study of Beijing Wenyuhe “Future iValley” Park demonstrating the implementation of those concepts and elements in concrete projects as well as their actual effects. By means of the introduction and elaboration above, the article comes to the conclusion that urban low-carbon parks are a new medium that can effectively enhance public awareness of carbon peaking and carbon neutrality.

Enhancing China’s National Park Fund Guarantees: A Social-Ecological Game Model Approach

This study presents a novel tripartite evolutionary game model within a social-ecological system framework to address the challenge of securing sustainable operational funding for national parks while balancing social and ecological interests. The model engages 3 key stakeholders: government, public welfare organizations, and national park operators, highlighting their complex interdependencies. Findings indicate that cooperative strategies among stakeholders are more likely when appropriate incentives and penalties are applied and decrease with higher associated costs. The study recommends that policymakers implement diversified funding mechanisms, clarify fiscal responsibilities, and promote coordinated efforts between central and local governments. Additionally, reducing operational costs and barriers for public welfare organizations and park operators can enhance cooperative behavior. This research provides valuable insights for optimizing funding mechanisms for national parks and offers an innovative tool for the study of social-ecological systems.

Low Carbon Operation Control Technology for Smart Parks Combined with Intelligent Optimization Algorithms

Abstract This paper first calculates carbon emissions based on the carbon emission factor method and other methods and constructs a linear programming model for low-carbon operation of smart parks with economy, independence, and carbon emissions as objective functions, and constrains them from four aspects: power grid, natural gas pipeline network, equipment output, and energy storage battery limit. The problem can be solved using the sparrow optimization algorithm. Finally, the carbon emissions, power dispatching, and power consumption of the sample parks under the low-carbon operation control mode were analyzed. The results show that the carbon emissions of the park are reduced by 2035.93kg, and the total operating cost is reduced by 1680.11 yuan during the typical daily operation in summer after the intelligent optimization algorithm is used. The park’s carbon emissions decreased by 1686.53kg, and the total operating cost decreased by 1582.42 yuan during the typical daily operation in winter. The importance of this study lies in the low-carbon and modernization of smart parks.