Search Results (58)

With the global trend towards “carbon neutrality,” the use of electric vehicles is becoming increasingly widespread, leading to new impacts on urban spaces. In the process of allocating resources for urban charging stations, there are widespread issues such as a singular planning approach and inadequate adaptation to actual travel demands. Therefore, this study adopts a method of integrating multi-source data to optimize the planning and layout of public electric vehicle charging facilities in Macau, striving to achieve breakthroughs in theoretical methods and key technologies. The study obtained a determination coefficient of R² = 0.43 through quantitative analysis, which is within a reasonable range of fitting spatial syntax and charging facility layout. This indicates that there is a moderate positive correlation between the distribution of charging facilities and core indicators such as road network integration and accessibility—about 43% of layout differences can be explained by spatial syntax indicators, and the remaining 57% of differences reserve space for optimizing multiple factors such as population density and parking lot distribution. On this basis, this study compares the layout experience of medium to high-density cities such as Hong Kong and Singapore, and combines the common characteristics of old parishes on Macau Island and new urban areas on outlying islands to explore innovative sustainable development technology paths that are suitable for Macau. This study not only summarizes the key factors and optimization breakthroughs that affect the spatial distribution of charging facilities in Macau, providing basic data and methodological strategies for charging facility planning, but also helps Macau save energy and reduce emissions, build a green city through layout optimization, provide practical reference for the development of land reclamation areas, and provide reference for carbon neutrality and smart city construction in the Guangdong Hong Kong Macau Greater Bay Area. Full article

Mass market adoption of EVs is critical for decreasing greenhouse gas emissions and dependence on fossil fuels. However, this transition faces significant challenges, particularly the limited availability of public charging infrastructure. Expanding charging stations and renewable integrated EV parking lots can accelerate the adoption of EVs by enhancing charging accessibility and sustainability. This paper introduces an integrated optimization framework to determine the optimal siting of a Residential Parking Lot (RPL), a Commercial Parking Lot (CPL), and an Industrial Fast Charging Station (IFCS) within the IEEE 33-bus distribution system. In addition, the optimal sizing of rooftop solar photovoltaic (SPV) systems on the RPL and CPL is addressed to enhance energy sustainability and reduce grid dependency. The framework aims to minimize overall power losses while considering long-term technical, economic, and environmental impacts. To solve the formulated multi-dimensional optimization problem, Horse Herd Optimization (HHO) is used. Comparative analyses on IEEE-33 bus demonstrate that HHO outperforms well-known optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) in achieving lower energy losses. Case studies show that installing a 400-kW rooftop PV system can reduce daily energy expenditures by up to 51.60%, while coordinated vehicle scheduling further decreases energy purchasing costs by 4.68%. The results underscore the significant technical, economic, and environmental benefits of optimally integrating EV charging infrastructure with renewable energy systems, contributing to more sustainable and resilient urban energy networks. Full article

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. Full article

The Boronia Ridge palusmont, Walpole, in southern Western Australia, is situated in the most humid part of the State. It was a unique hilltop wetland complex and the only one of its type in the State. On its margins, the area also supports the ancient sedge Reedia spathacea, a Gondwanan relict endemic to humid southern Western Australia and the Walpole region and a plant that was ultimately recognised as being of national significance and protected under Australia’s strongest conservation law, the Environment Protection and Biodiversity Act (1999). However, prior to the geoheritage and biodiversity values of the area being known, in the late 1980s, a pristine scenic area west of Walpole, adjacent to the Walpole River and Walpole Inlet, classified as a Class A national park, was earmarked for urban development, in spite of there being “very little demonstrated requirement for land in Walpole”. This appeared to be as a result of poor land-use planning, since the urbanisation proposed was to be located on the Walpole River delta and wetlands. Urban infrastructures would also impact on adjoining wetlands and the Walpole Inlet System. With new information available in relation to the soils, wetlands, and environmental values of the area, in 1993, community groups and scientists combined, at a public Local Government meeting, to demonstrate that the proposed urban development, referred to as Lot 650, and later Boronia Ridge, with its above-land surface wastewater treatment, was inappropriate, both from an engineering perspective and due to the high conservation values of the area. With the support of the local government of the day and expert scientists who confirmed local concerns, the community engaged in a 7-year conflict with the development proponent, government agencies involved in decision making, and politicians of the day. Ultimately, the use of state-of-the-art science and traditional geomorphic, stratigraphic, hydrological, and geoheritage principles failed to prevent the urbanisation of the area in favour of preserving the whole area as a wetland complex. The following three reasons for this failure are identified: 1. political, rather than science-based decision making, 2. government agencies staffed without the necessary training in biological or earth sciences to make informed decisions, and 3. little attention to environmental concerns due to a bias towards development. Walpole, with its population of 400, moved from a low priority on the wastewater treatment priority list in Western Australia to a high priority on the deep sewerage priority list to accommodate a proposed residential development. Full article

There are numerous idle electric vehicle (EV) resources in urban distribution networks, which hold significant potential for emergency power supply support following network failures. Based on this, a proactive power supply support strategy is proposed, integrating urban idle resources and emergency resources under extreme scenarios. First, an emergency dispatch model is established for EVs in public parking lots and electric power supply vehicles (EPSVs), considering the impact of road congestion. Next, the costs of various emergency resources are analyzed, and a multi-source collaborative power restoration strategy is proposed. This strategy includes EPSVs, EVs, photovoltaics, line repair teams, and other resources, with load shedding loss costs incorporated into the optimization framework. Finally, the proposed strategy is validated through simulations using an IEEE 33-node distribution network and a 32-node transportation network. The results demonstrate that the line topology of the faulty distribution network is restored to normal after the repair team’s intervention. Moreover, the strategy enables efficient utilization and economic dispatch of urban idle and emergency resources while improving system reliability. Full article

Urban green spaces (UGSs) are vital in enhancing environmental quality, social well-being, and climate resilience, yet their distribution and accessibility remain uneven in many rapidly urbanizing cities. The 3–30–300 rule offers a structured guideline with which to assess urban greenness, emphasizing tree visibility, canopy cover, and green space proximity. However, its applicability in dense and resource-constrained urban environments has not been sufficiently examined. This study evaluates the feasibility of the 3–30–300 rule in Nagpur, India, using survey-based visibility assessments, NDVI-derived vegetation cover analysis, and QGIS-based accessibility evaluation. The study also introduces the Urban Greenness Exposure Index (UGEI), a composite metric that refines greenness assessment by capturing intra-zone variations beyond broad classifications. The findings reveal significant variations in urban greenness exposure across Nagpur’s ten municipal zones. Low-greenness zones report the highest tree visibility deprivation (below two trees), limited canopy cover (~7%), and restricted green space access (over 80% of residents lacking access within 300 m). The correlation analysis shows that higher canopy cover does not necessarily correspond to better visibility or accessibility, highlighting the need for integrated planning strategies. The study concludes that applying the 3–30–300 rule in high-density Indian cities requires localized adaptations, such as incentivizing street tree planting, integrating vertical greenery, and repurposing vacant lots for public parks. The UGEI framework offers a practical tool for identifying priority zones and guiding equitable greening interventions, based on insights drawn from the Nagpur case study. 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

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

This article reports the results of an investigation into the activity concentration of natural radionuclides in raw building materials for underground parking lots, together with the assessment of the radiation hazard for the public related to exposure to ionizing radiations. To this purpose, high-purity germanium (HPGe) γ-ray spectrometry was employed in order to quantify the average specific activity of ²²⁶Ra, ²³²Th, and ⁴⁰K natural radioisotopes. With the aim to assess any possible radiological health risk for the population, the absorbed γ-dose rate (D), the annual effective dose equivalent outdoor (AEDE_out) and indoor (AEDE_in), the activity concentration index (I), and the alpha index (I_α) were also estimated, resulting in values that were lower than the maximum recommended ones for humans. Finally, the extent of the correlations existing between the observed radioactivity and radiological parameters and of these parameters with the analyzed samples was quantified through statistical analyses, including Pearson’s correlation, a principal component analysis (PCA), and a hierarchical cluster analysis (HCA). As a result, three clusters of the investigated samples were recognized based on their chemical composition and mineralogical nature. Noteworthily, this paper covers a certain gap in science since its topic does not appear in literature in this form. Thus, the authors underline the importance of this work to global knowledge in the environmental research and public health fields. Full article

In the post-pandemic era, there has been a noticeable increase in tourism demand in China, and the comprehensive driving role of tourism in the national economy and social development has become more pronounced. Tourism public service providers, which are led by the government, urgently need to consider methods for enhancing the efficacy of public services in tourism to meet tourist demands, methods for further attracting more visitors, and methods for achieving sustainable and high-quality development in the tourism industry. However, despite the continuous enrichment and enhancement of the content and quality of tourism public services by Chinese government departments, the current research on government-provided tourism public services in underdeveloped areas is still relatively scarce in terms of tourists’ cognitive evaluations. Therefore, this study focuses on five 5A-rated scenic areas in Xinjiang, where 1122 valid questionnaires were distributed. In using exploratory factor analysis and confirmatory factor analysis, we established an evaluation system for Xinjiang’s tourism public services. Paired sample t-tests and importance–performance analyses (IPA) were employed to assess the importance and satisfaction of the aforementioned indicators. The results showed the following: (1) The tourism public service quality scale comprised 47 measurement items across four dimensions and exhibited high reliability, convergent validity, and discriminant validity. (2) The average satisfaction score across the 47 indicators was 3.90, thus indicating a favorable assessment of Xinjiang’s tourism public services by visitors. In addition, the highest satisfaction noted was in well-established safety assurance mechanisms (4.46), and the lowest was recorded in facial recognition entry systems (3.35). (3) The IPA results suggest that aspects such as comprehensive traffic guidance signage, affordability of transportation, and convenience of access are factors that require maintenance. Clear safety guidelines and warning systems, truthful promotion, and emphasis on protecting tourist rights are in the potential advantage area. The promotion of paid leave policies requires moderate attention, while intelligent parking lots, electronic all-in-one cards for scenic areas, and one-click rescue indicators necessitate improvement. These research findings have significant practical implications for the construction of public services in Xinjiang’s tourism. Full article

As penetration of EVs in the transportation sector is increasing, the demand for the mandatory installation of charging infrastructure also is increasing. In addition, renewable energy and energy storage systems (ESSs) are being reviewed for use in electric vehicle charging stations (EVCSs). In this paper, we present an optimal electricity trading volume and an optimal installation capacity of ESSs to maximize the daily profit of the EVCSs equipped with solar power generation when the EVCSs are licensed to sell energy to the power supplier during a specific time period. By formulating and solving the optimization problem of the EVCSs, this paper analyzes validation results for the different useful lives of ESSs, the peak power of a PV generator, and weather conditions at the Yangjae Solar Station and the Suseo Station public parking lot, Seoul, Republic of Korea. Furthermore, this paper validates that the daily expected profit of EVCSs with the proposed method outperforms the profit of conventional EVCSs which do not utilize ESSs. Full article

Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas. Reducing wild parking habits can be pursued by setting reserved parking spaces. In this work, we consider the problem faced by a municipality that hosts e-scooter sharing services and must choose which locations in its territory may be rented as reserved parking lots to sharing companies, with the aim of maximizing a return on renting and while taking into account spatial consideration and parking needs of local residents. Since this problem may result difficult to solve even for a state-of-the-art optimization software, we propose a hybrid metaheuristic solution algorithm combining a quantum-inspired ant colony optimization algorithm with an exact large neighborhood search. Results of computational tests considering realistic instances referring to the Italian capital city of Rome show the superior performance of the proposed hybrid metaheuristic. Full article

Latin America is as heterogeneous as its cities. To understand Latin American cities, it is necessary to have a clear vision of how they are organized, not only physically but according to their social, cultural, and economic contexts (which are associated). Historically, it has suffered a lot in terms of politics and the security of its cities. Insecurity reflects a structural problem; economic and social inequality are the main actors of spatial segregation, motivating violence and, consequently, the insecurity of urban space. Fortaleza is one of the largest Brazilian cities, and it is possible to fit it into this reality. Many public actions may benefit only one sector of society, showing biased investments and, again, confirming the tremendous economic and social differences in Latin American cities. In this study, questionnaires related to attendance, feelings, maintenance, and safety were made to some of Fortaleza’s residents regarding an urban park called Parque do Cocó, one of the biggest in Latin America. Due to its large area, it is located in different city neighborhoods, allowing for us to see the differences in treatments throughout its extension. This article aims to understand how the public opinions and mentality of a portion of the population are characterized concerning safety in green public spaces in the city. In addition, the insecurity of public green spaces can also be inserted into a problem of environmental injustice in the urban context. This study of Fortaleza’s Cocó Park highlights significant disparities in safety perceptions and maintenance across socioeconomic regions. Findings indicate that areas with higher human development index (HDI) scores experience better park conditions. The research underscores the necessity for comprehensive urban policies that address socioeconomic inequalities, as evidenced by the correlation between crime rates and HDI. Cocó Park emerges as a key factor in sustainable urban development, aligning with Fortaleza’s urban planning goals. The study emphasizes the critical role of urban green spaces in enhancing the quality of life and fostering social cohesion in urban landscapes. Moreover, with the data collected, it will be possible to stress further how urban adequacy relates to social situations in Latin American cities. Full article

In developing cities, newly emerging cities have started facing the problem of insufficient public parking facilities and ineffective regulations. To support the planning, design and management of the public parking system towards a smart and sustainable city vision, it is necessary to study deeply parking behaviors. This paper presents an empirical study on parking-choice behaviors of motorcycle users and car users in the emerging cities of developing countries through a case study of three cities in Binh Duong, Vietnam. To explore the behavioral mechanisms and influential factors, the multinomial logit parking choice models are developed using revealed preference and stated preference data. The users’ overall satisfaction and perceived importance of parking lot design and service aspects are analyzed using order logistic regression. The revealed choices show no trade-off between parking fee and walking distance, as the users are not fully aware of parking locations and service features. However, the stated choice experiments prove a potential existence of the trade-off mechanism and differentiate significant factors in the decision of choices for the two user groups. The results bring insightful implications for the development of a smart public parking system. Full article

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. Full article