Planning Issues Research Articles

For Spatial Planning and Land Consolidation Issue

For Spatial Planning and Land Consolidation Issue

Multi-strategy cooperative enhancement dung beetle optimizer and its application in obstacle avoidance navigation.

Path planning is a crucial component of unmanned driving systems designed to address the challenge of autonomous navigation. This article introduces a novel approach called the multi-strategy cooperative enhanced dung beetle optimization algorithm (RCDBO) and applies it to dispose of path planning issues. Its primary aim is to mitigate the shortcomings of the fundamental dung beetle optimization algorithm (DBO), namely, its tendency to prematurely converge to local optima and its limited global planning capability. Initially, population initialization leverages Bernoulli-based chaotic mapping to enhance diversity and ensure uniform randomness, thereby improving both the quality of initial solutions and optimization efficiency. Subsequently, a random walk strategy is employed to perturb the rolling behavior of the dung beetle population during the initial stage, thus mitigating potential algorithmic local stagnation. Finally, adopting a vertical and horizontal crossover strategy introduces perturbations to the current best value of the dung beetle population, thereby enhancing the DBO method's global optimization capability during the later stages of evolution. The RCDBO method was evaluated against several well-established swarm intelligence algorithms using twelve benchmark test functions, the CEC2021 test suite, the Wilcoxon rank-sum test, and the Friedman test, to rigorously assess its feasibility. Additionally, the effectiveness of each strategy within the RCDBO framework was validated, and the algorithm's capability to achieve an optimal balance between global exploration and local exploitation was systematically analyzed through the CEC2021 test suite. Furthermore, in path planning simulation experiments targeting maps of varying sizes and complexities, the RCDBO algorithm, compared to the basic DBO algorithm, results in shorter planned path lengths. In simpler map environments, it exhibits fewer turning points and smoother paths, indicating that the RCDBO algorithm also possesses certain advantages in addressing path-planning challenges.

Decision-Making Approach for Land Use in Urban Industrial Area Redevelopment Projects

The process of industrial transformation, becoming the most important for building up sustainable cities, is in urgent need for studying alternative options for land use to ensure sustainable city development. Rapid urbanization requires new built-up design in strong correlation with urban spatial planning issues. The objective of this paper is to contribute for the studies of sustainable land use solutions through investigating the problems of the reorganization of depressed industrial areas. The research methodology is based on the multicriteria decision-making method to provide primarily social functions of depressed industrial areas, depending on environmental and economic aspects of their historical location in urban structure. Since the article highlights the concept of the 15-min city to build a polycentric urban spatial structure, the assessment of supply and demand matching of social facilities is based on the analysis of 15 min accessibility to the serviced within either industrial zone and the city on the whole. The GIS spatial analysis method supports the developed criteria set to evaluate main city functions. The research provides the methodology for defining the priority land use solution for a specific industrial area in a specific location. The proposed methodology suggests a procedure for evaluating the importance of the land use composition for the city community, considering environmental requirements and cost-effectiveness of the project. Three different industrial zones located in the same city are a case study to asses the reliability of the proposed methodology. The research conclusion provides implementing sustainable land-use solutions for improving urban environment quality as well as the quality of life for the population.

A Fermatean Fuzzy SWARA-TOPSIS Methodology based on SCOR model for Autonomous Vehicle Parking Lot Selection

Population growth in crowded cities and the resulting increase in vehicle use have led to the problem of insufficient parking. When public parking lots and urban growth are not in coordination, vehicles park on the street and close the crosswalks. In the coming years, this problem will become more complicated with the addition of autonomous vehicles (AVs) to urban traffic. This study addresses the research question of how to effectively select AV parking lots in urban areas experiencing population growth and increased vehicle usage. For this aim, a hybrid Multi-Criteria Decision Making (MCDM) methodology, combining SWARA (Step-wise Weight Assessment Ratio Analysis) and TOPSIS (Technique for Order Preference by Similarity) approaches in a Fermatean Fuzzy (FF) environment is proposed. The decision hierarchy based on the SCOR model has been developed to determine and construct the evaluation criteria. Then, a case study analysis has been applied to selected districts in Istanbul, which is Turkiye's most populous and developing city. Operating expenses, safety and security, and land costs are determined as the most important factors. As a result of the detailed fuzzy analysis, which districts should primarily be chosen for AV parking lots in Istanbul is determined and finally, the robustness and validity of the results obtained by the sensitivity analysis being questioned. The study contributes by providing insights into AV parking lot selection, demonstrating the efficacy of the proposed methodology, and highlighting the importance of addressing this issue in urban planning.

Bridging the gap: challenges of urban planning and policies for favelas in Salvador, Brazil

Salvador, the capital of Bahia State in Brazil, has long struggled with pronounced economic and social inequality. This stratification has resulted in spatial segregation, leading to the expansion of favelas, typically near the city center, where segregation in various dimensions of social engagement intensifies tensions of inequities. The municipal government has made several attempts to address favela-related issues through urban planning policies. Notwithstanding the progress made in lowering violent crime and extreme poverty, these issues still exist. This paper explores the interaction between government-led practices and corresponding self-organized actions toward planning strategies, invoking a social justice framework and critical urban theories. It identifies mismatches between institutional objectives and residents’ expectations, highlighting that structural contradictions in the region’s social development remain unresolved. The analysis suggests that planning policies should consider key factors, such as resource redistribution, recognition of sociocultural identity, and political representation. On the basis of these findings, this paper offers insights into urban planning for favelas as examples of complex governance challenges. It also aims to contribute to the global discourse on similar urban planning issues.

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China

Understanding and recognizing urban morphology evolution is a crucial issue in urban planning, with extensive research dedicated to detecting the extent of urban expansion. However, as urban development patterns shift from incremental expansion to stock optimization, related studies on meso- and microscale urban morphology evolution face limitations such as insufficient spatiotemporal data granularity, poor generalizability, and inability to extract internal evolution patterns. This study employs deep learning and meso-/microscopic urban form indicators to develop a generic framework for extracting and describing the evolution of meso-/microscale urban morphology. The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. We applied this framework to conduct a combined quantitative and qualitative analysis of the internal urban morphology evolution of Binhai New Area from 2009 to 2022, with detailed visualizations of morphology evolution at each time point. The study identified that different locations in the area exhibited seven distinct evolution patterns: edge areal expansion, preservation of developmental potential, industrial land development pattern, rapid comprehensive demolition and construction pattern, linear development pattern, mixed evolution, and stable evolution. The results indicate that in the stock development phase, high-density urban areas exhibit multidimensional development characteristics by region, period, and function. Our work demonstrates the potential of using deep learning and grid classification indicators to study meso-/microscale urban morphology evolution, providing a scalable, cost-effective, quantitative, and portable approach for historical urban morphology understanding.

An Improved RRT Path-Planning Algorithm Based on Vehicle Lane-Change Trajectory Data

The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal path quality. To overcome these issues, this paper proposes an improved RRT planning algorithm based on vehicle lane change trajectory data. This algorithm dynamically adjusts the sampling area based on road environment and trajectory change laws, allowing the random tree to sample within an effective area, thereby improving the algorithm’s sampling efficiency. After determining the sampling area, a sampling point optimization strategy is used to enhance sampling quality, resulting in a smoother and more executable path. Finally, the vehicle is processed in a standardized manner to further improve path safety. Simulation results indicate that, compared to the original RRT algorithm, the improved version reduces nodes, planning time, and path length by 12.77%, 64.79%, and 12.87%, respectively. It also improves path smoothness and more closely aligns with actual lane-change trajectories, demonstrating the effectiveness and executability of the improved algorithm.

An effective sizing study on PV-wind-battery hybrid renewable energy systems

Hybrid renewable energy system is the effective and efficient way of energy harvesting in remote or isolated areas having the advantage of harnessing of multiple renewable sources at a time. Solar, wind and battery-based hybrid system is one of the most promising alternatives in this field. Sizing of the system components depend on various technical and economical specifications. The research studied the effect of solar panel tilt angle and wind turbine hub height on sizing optimization of PV wind battery-based hybrid renewable energy system using HOMER software. Through empirical method, an optimal monthly, seasonal, yearly optimal tilt angle and wind turbine hub height have been computed. These values are used in addition with various other technical and economical specifications in order to determine the optimal sizing of hybrid system. At first, three different scenarios have been considered for the study of yearly, seasonal, and monthly optimal tilt angle with an optimized hub height for the wind turbine. Then, a comparative analysis of the sizing of hybrid system at unoptimized and optimized tilt angle and hub height have also been done. Furthermore, a detailed comparative and sensitivity analysis have also been performed across different scenarios and approaches. A detailed financial study for policy planning and implementation issues with other, traditional state of the art approaches significantly demonstrates the effectiveness of the proposed method for optimal sizing of the hybrid system.

Expansion of UK medical student numbers: what are the challenges and opportunities for undergraduate dermatology education?

This correspondence piece addresses the topical issue of the UK government's plan to expand medical school numbers and its implications for undergraduate medical education, affecting both learners and educators. The article summarises the results of a survey of undergraduate dermatology educators, highlighting their concerns about the challenges and impact of increasing student numbers. The article highlights the risk of a reduction in the volume and quality of clinical learning opportunities in undergraduate dermatology as a consequence of student numbers exceeding clinical departmental capacity and resources.

Towards A City of Good Dwelling

One of the main issues in contemporary urban planning is the fragmentation of urban spaces (Batty 2009) due to the relentless processes of land transformation. This phenomenon can lead to the creation of isolated urban islands and a lack of high-quality public areas. Such weaknesses particularly affect the socio-economic conditions of marginal areas within the existing city, especially those on the outskirts near rural areas. Public spaces (Low et al 2006) are essential for social interaction, community building, and improving the overall quality of urban life. They are spaces where cultural, economic, and social activities converge, contributing to the city's vibrancy and inclusiveness. Are considered the key of the new processes of urban regeneration and it contributes significantly to the making of the city of "good dwelling" (Beauregard 2020). To obtain the conditions for a good dwelling, it is proposed to apply the seven principles contained in the "Manifesto della Città del Buon Abitare"(Colarossi et al 2023): the city of good dwelling must assume the general principles of hospitality, urbanity and beauty of its public spaces. The paper proposes as innovative approach, the use of an urban planning tool with which it is possible to apply the principles of the Manifesto of the city of good living and which is called "Plan-program of public space planning and public and private services" (P.A.S.S.i.). The effort of this research is to identify a general planning tool for the redevelopment to the existing city. The multi-scalar approach of the research tool simultaneously allows an overall vision of the program and a qualitative control of each individual project.

Aplikasi Model Pertumbuhan Logistik untuk Estimasi Jumlah Penduduk Kabupaten Ponorogo

Population growth is a critical issue for government planning, necessitating accurate projections for future years to inform long-term policy decisions. This study explores estimating the population of Ponorogo Regency through a logistic growth model. The model’s differential equation, structured as a separable differential equation, was solved and applied to real population data to derive a specific solution. The results indicate that, among five tested models, four logistic growth models are viable for projecting the population of Ponorogo Regency.

The Role of Contrast-Enhanced Imaging Methods in Breast Cancer Early Diagnostics and Treatment Planning

Оn March 27, 2023, а multidisciplinary Advisory Board meeting was held in Moscow with the participation of Е.V. Artamonova, Е.А. Busko, V.V. Didenko, А.D. Zikiryakhodzhaev, М.S. Karpova, G.Е. Kvetenadze, P.V. Krivorot’ko, М.L. Mazo, А.V. Petrovsky, О.S. Puchkova, S.V. Serebryakova under the chairmanship of I.Е. Tyurin and N.I. Rozhkova. The participants discussed different issues of early diagnostics and treatment planning of breast cancer. We present the conclusion made by the Advisory Board.

Data science basis and influencing factors for the evaluation of environmental safety perception in Macau parishes

In the context of rapid urbanization, accurately identifying the visual factors that influence environmental safety perception is crucial for improving urban transportation environments and enhancing pedestrian safety. With the increase in urban population density and traffic flow, optimizing urban environmental design to elevate residents’ sense of safety has become a key issue in urban planning and management. However, the existing studies face numerous challenges in conducting large-scale quantitative analysis of environmental safety perception in complex scenarios, such as difficulties in data acquisition and limitations in analytical methods. This study addresses these challenges by applying image semantic segmentation and object detection techniques to extract key visual elements from street view images, combined with manual scoring and deep learning methods, to construct a road safety perception dataset. Using a LightGBM model and the SHAP interpretation framework, in this study, we identify the critical visual factors influencing environmental safety perception. An empirical study was conducted in Macau, a modern city where Eastern and Western cultures intersect, and tourism thrives. The findings reveal that: ① The overall environmental safety perception in the eight parishes and surrounding roads of Macau is relatively high, with significant regional differences in safety perception scores around Macau’s parish roads; ② The proportions of buildings, sidewalks, roads, and trees in images are the four primary factors influencing environmental safety perception; ③ The proportions and quantities of visual elements interact with each other, and their reasonable distribution helps form clear spatial visibility and creates conducive activity spaces, thereby enhancing the perception of environmental safety. Through empirical analysis, this study uncovers the mechanisms by which visual elements in urban street scenes affect environmental safety perception, providing scientific evidence for urban planning and transportation environment improvement. The research holds theoretical significance and offers practical references for urban design and management, demonstrating broad application value.

Optimization of industrial layout in airport economic zone through government-enterprise interaction

To avoid the issue of industrial layout plans in Airport Economic Zones (AEZs) being hard to implement due to mismatched the land supply and demand, it is necessary to clarify industrial layout factors that the government (land supplier) and locating enterprises (land demanders) focus on. This study constructs a AEZ's industrial layout optimization model from the government's perspective and enterprises’ locating utility. Based on the Gale-Shapley algorithm, aiming at eliminating blocking pairs, we establish government-enterprise interacting rules and coordinating mechanisms, corresponding to two types of government-enterprise interactions, namely public consultation and investment promotion joint meeting. According to government transparency and enterprise participation, the optimization model and Agent-Based Model are adopted in the combination to simulate the government-enterprise interactions in the decision-making of industrial layout in AEZ, under two interaction rules of “enterprises provide feedback in one go - the government makes revisions in one go” and “enterprises provide feedback one by one - the government makes revisions one by one”, respectively. By analyzing the changing curves of their objective functions, the equilibrium point where both parties can reach an agreement is identified, and the industrial layout scheme in AEZ is determined that will be mutually accepted. It is found that positive interaction with high government transparency and high enterprise participation will help improve the rationality of the industrial layout in AEZ, maximizing the efficiency of spatial allocation of elements and resources.

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

Effect of Capital Deductions on the Financial Performance of Listed Companies at the Nairobi Securities Exchange in Kenya

Purpose: Shareholders’ wealth maximization is a priority objective of every corporate entity. The issue of corporate tax planning and firm value is gaining relevance following the recent financial crises which resulted in the increasing collapse of global and local companies. The purpose of this study was to investigate the effect of capital deductions on the financial performance of companies listed at the Nairobi Securities Exchange (NSE) in Kenya. The study was anchored on signaling theory. Methodology: The study adopted both explanatory and longitudinal research. Secondary data for the study for the period between 2013-2022 was extracted from annual financial statements of 57 listed firms targeted for analysis. The data for the study was analyzed through descriptive and inferential statistics using statistical techniques including Pearson correlation coefficient and regression analysis. All the analyses were done using the STATA version 13.0 Pearson correlation results revealed a significant association between study variables and financial performance. Multiple Linear regression model was used to analyze relationships and the effect of the study variables. Results: The study findings established that capital deductions (β = 0.844, p-value = 0.000, <0.05) have a positive and significant effect on financial performance. The study contributes to knowledge by revealing new insights that tax planning has a significant effect on financial performance of listed firms in Kenya. Specifically, it was established that capital deductions have a significant effect on financial performance of listed firms. Conclusion: The study concludes that a holistic approach to tax planning and an optimal mix of tax planning strategies are important determinants and have a positive contributory effect on firm performance. The study recommends that companies’ management should strive to have an in-depth understanding of tax laws to take advantage of every opportunity that will reduce their tax liability thereby increasing their returns and value. The future researchers should explore the effect of Indirect tax incentives on financial performance.

Языковое планирование в Японии с середины XIX до начала XXI века

The article examines language planning in Japan from the second half of the XIX century to the beginning of the XXI century. It analyzes changes in the linguistic situation, identifies the key actors of language planning, as well as legislative acts of the Japanese government that turned various elements of language planning into an organized system of state policy. Special attention is focused on the Meiji era and language reforms after World War II, when changes to the Japanese language were stimulated by powerful popular movements. At other times, as well as at present, language planning has been under strict control of the Japanese government and has been primarily limited to its language policy. Additionally, the article provides a brief overview of language planning and policy issues in Japan on occupied territories during World War II. It also highlights the key features of the contemporary linguistic situation and the current tasks of language policy and planning in Japan in the XXI century.

Participation of Citizen as Social Capital in LAPOR! Application in Indonesia

Citizen engagement is crucial for the success of smart cities. In urban areas, communities as social capital has an interpersonal bond that unites people. Due to the complexity of urban issues like pollution, waste, and population density, citizens can monitor the government's work priorities to solve the problem. This article focuses on smart cities from a human-centered perspective, emphasizing the role of information technology. In Indonesia, citizens' rights awareness is facilitated by the National Public Service Complaint Management System (SP4N) or People's Aspiration and Online Complaints Service (LAPOR!), which acts as social capital. This article explores five key aspects of smart cities and smart citizenship: governance, citizen participation, infrastructure, public spaces, and urban livability. While ICT-driven approaches can swiftly transform cities, a community-based approach fosters long-term shifts. Meanwhile, limited citizen involvement contributes to ecosystem degradation in Indonesia and underscores the urgency and importance of citizen engagement in urban planning. In democratic societies, citizens hold power, and concepts of smart citizenship encompass civic intelligence and, at the same time, also value local knowledge. In Indonesia, LAPOR! reflects smart citizenship, enabling transparent reporting of aspirations and complaints. However, factors like limited access and education of citizens affect its use. This research used qualitative methods with content analysis methods. Qualitative content analysis is used to examine the textual content of websites. Using LAPOR! SP4N in Indonesia to see the implementation of the concept of smart society and smart city. Participatory processes should contribute substantially to urban planning and governance. Information technology serves as a temporary solution for long-term urban planning issues. Broader concerns like public policy and participatory democracy must also be considered. In conclusion, this article explores the dynamic relationship between smart cities and smart citizenship, highlighting the importance of active citizen engagement and the potential of technology to empower communities for positive urban transformations.

Research on the Impact and Strategy of the Accelerating Urbanization in China

As urbanization accelerates at an unprecedented pace, especially in rapidly developing nations like China, a range of significant challenges has emerged. This essay will explore four major issues arising from urbanization: polarization, urban planning difficulties, pollution, and global warming. These problems not only impact rural and urban areas but also pose serious threats to the global community. Polarization exacerbates income inequality, leading to a widening gap between rural and urban populations. Urban planning issues, such as inadequate infrastructure, strain city resources, causing congestion and inefficiency, pollution, fueled by industrial growth, deteriorates air quality, posing health risks to citizens; while global warming, driven by increased carbon emissions, accelerates climate change with severe consequences worldwide. To address these challenges, solutions such as boosting farmers incomes, constructing viaducts and taller buildings, promoting the use of masks, and reducing carbon emissions are proposed. However, the feasibility of these solutions depends on each countrys financial capacity and available resources. Therefore, this essay will assess the practicality of these measures and their potential to mitigate the risks associated with rapid urbanization.