Hasil untuk "Regional planning"

V. Olgyay

Xia Li, A. Yeh

M. Uysal, C. Jurowski

Giovana Goretti Feijó Almeida

Urban dynamics of the 21st century is distinguished by the prevalence and significance of cities and their constituent elements. The objective of this study is to analyze the role of the territorial brand as one of the strategic elements of public governance. The methodology employs a comparative approach between four Brazilian and Portuguese cities, utilizing seven variables pertinent to the concept of a “territorial brand as a cultural product of regional development.” The results underscore the significance of strategic planning in enhancing urban competitiveness, influencing urban public governance, and reflecting on urban, cultural, social, regional, and territorial changes. The study provides insights into the implementation of a territorial brand, particularly in cities with a cultural focus, offering a comprehensive understanding of how this governance strategy can shape urban development and reinforce local cultural identity.

Valerio Martinelli

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always remaining in the groove of rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of continuously updating emerging topics concerning relationships between urban planning, mobility and environment, through a collection of short scientific papers written by young researchers. The Review Notes are made of four parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal. This section, International Regulations and Legislation for the Energy Transition, explores the challenges and opportunities in the urban context to understand the evolving landscape of the global energy transition. The RED III Directive (2023/2413) introduces more ambitious targets for renewable energy than RED II and provides for Renewable Acceleration Areas (RAAs) to expedite plant authorizations. This gives a more prominent role to urban and regional planning, which must integrate energy, environmental, and infrastructure criteria into location decisions. The Italian case demonstrates how multilevel governance, along with operational tools such as strategic assessments and digital platforms, is crucial in defining RAAs. Cities thus assume a central role in decarbonization processes. This paper highlights opportunities and critical issues towards a faster, fairer, and more sustainable energy transition.

Lulama Mabe, Ilze Swarts, Mphoreng M. Mmako

Background: Nurse’s engagement is associated with positive outcomes, such as improved staff morale. This, in turn, motivates nursing staff to achieve organisational goals that elevate patient care. However, few studies have been conducted on this subject within the nursing staff of South Africa’s public sector. Aim: The study explores the correlation between staff morale and engagement, though correlation doesn’t imply causation. Further research is needed to determine any causal relationship. Settings: The study was conducted in the public health sectors in South Africa in six provinces; namely, Western Cape, Northern Cape, Eastern Cape, KwaZulu-Natal, Free State and North West as per the approval received from each province. Methods: A quantitative methodology, using self-administered questionnaire from validated instruments consisted of measuring scale developed by Gallup (2006) and the scale developed by Nolan, Brown, Naughton Nolan (1998). The data were analysed using the Statistical Package for the Social Sciences (SPSS) version 24. Pearson correlation analysis was used to test for relationship between employee engagement and staff morale. Results: Employee engagement, including compensation, career growth, and work environment, was discussed. For staff morale, rewards, recognition, and job satisfaction were explored. The study found a significant relationship between employee engagement and staff morale. Conclusion: This study suggests that nursing management foster a supportive work environment and provide sufficient resources to boost nurse engagement and morale. Contribution: The study will help management understand factors affecting nursing staff engagement and morale, and guide with improvements.

Mihir Parmar, Palash Goyal, Xin Liu et al.

Recently, decomposing complex problems into simple subtasks--a crucial part of human-like natural planning--to solve the given problem has significantly boosted the performance of large language models (LLMs). However, leveraging such planning structures during post-training to boost the performance of smaller open-source LLMs remains underexplored. Motivated by this, we introduce PLAN-TUNING, a unified post-training framework that (i) distills synthetic task decompositions (termed "planning trajectories") from large-scale LLMs and (ii) fine-tunes smaller models via supervised and reinforcement-learning objectives designed to mimic these planning processes to improve complex reasoning. On GSM8k and the MATH benchmarks, plan-tuned models outperform strong baselines by an average $\sim7\%$. Furthermore, plan-tuned models show better generalization capabilities on out-of-domain datasets, with average $\sim10\%$ and $\sim12\%$ performance improvements on OlympiadBench and AIME 2024, respectively. Our detailed analysis demonstrates how planning trajectories improves complex reasoning capabilities, showing that PLAN-TUNING is an effective strategy for improving task-specific performance of smaller LLMs.

Nicola Gigante, Francesco Leofante, Andrea Micheli

Counterfactual Explanations (CEs) are a powerful technique used to explain Machine Learning models by showing how the input to a model should be minimally changed for the model to produce a different output. Similar proposals have been made in the context of Automated Planning, where CEs have been characterised in terms of minimal modifications to an existing plan that would result in the satisfaction of a different goal. While such explanations may help diagnose faults and reason about the characteristics of a plan, they fail to capture higher-level properties of the problem being solved. To address this limitation, we propose a novel explanation paradigm that is based on counterfactual scenarios. In particular, given a planning problem $P$ and an \ltlf formula $ψ$ defining desired properties of a plan, counterfactual scenarios identify minimal modifications to $P$ such that it admits plans that comply with $ψ$. In this paper, we present two qualitative instantiations of counterfactual scenarios based on an explicit quantification over plans that must satisfy $ψ$. We then characterise the computational complexity of generating such counterfactual scenarios when different types of changes are allowed on $P$. We show that producing counterfactual scenarios is often only as expensive as computing a plan for $P$, thus demonstrating the practical viability of our proposal and ultimately providing a framework to construct practical algorithms in this area.

Michal Ciebielski, Victor Dhédin, Majid Khadiv

This work presents an optimization-based task and motion planning (TAMP) framework that unifies planning for locomotion and manipulation through a shared representation of contact modes. We define symbolic actions as contact mode changes, grounding high-level planning in low-level motion. This enables a unified search that spans task, contact, and motion planning while incorporating whole-body dynamics, as well as all constraints between the robot, the manipulated object, and the environment. Results on a humanoid platform show that our method can generate a broad range of physically consistent loco-manipulation behaviors over long action sequences requiring complex reasoning. To the best of our knowledge, this is the first work that enables the resolution of an integrated TAMP formulation with fully acyclic planning and whole body dynamics with actuation constraints for the humanoid loco-manipulation problem.

Abdulaziz I. Almulhim, Ismaila Rimi Abubakar

Urban water consumption has significant implications for both human welfare and the environment, especially in arid regions. In Saudi Arabia, where 60% of the urban water supply relies on energy-intensive desalination processes, the average per capita water consumption rate of 300 L/day is one of the highest worldwide. This study investigates potential behavioral patterns in residential water consumption among households in Saudi Arabia. A segmentation approach is employed to classify residents into clusters and evaluate their potential water consumption behavioral patterns. Data from 618 households were collected via a structured questionnaire and analyzed using descriptive statistics, principal component analysis (PCA), and cluster analysis (CA). Participants were categorized into six segments based on the relationship between their behaviors and water consumption derived from the factor analysis. The outcomes of PCA and CA analyses indicate a relationship between respondents’ socio-demographic factors and their potential behavioral patterns in residential water consumption. The findings highlight that education, household size, income, housing type, age, and nationality, in increasing order of significance, are the key factors influencing household water consumption and conservation tendencies. The paper concludes that comprehending residential water consumption patterns is crucial for effective interventions, social marketing strategies, and communication campaigns for behavioral changes. The study can inform water conservation policies and programs that promote sustainable water consumption practices, benefiting agencies, policymakers, and scholars in the water sector.

Li Zhang, Peter Jansen, Tianyi Zhang et al.

Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. However, existing methods rely on a fully-observed environment where all entity states are initially known, so a one-off representation can be constructed, leading to a complete plan. In contrast, we tackle partially-observed environments where there is initially no sufficient information to plan for the end-goal. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. By accomplishing the sub-goal, more information is acquired to augment the representation, eventually achieving the end-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation, with strong performance (98%) on the more complex Cooking World simulation where end-to-end LLMs fail to generate coherent plans (4%).

Anastassia Vybornova, Ane Rahbek Vierø, Kirsten Krogh Hansen et al.

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: a fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning, and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

Tobias John, Patrick Koopmann

We introduce ontology-mediated planning, in which planning problems are combined with an ontology. Our formalism differs from existing ones in that we focus on a strong separation of the formalisms for describing planning problems and ontologies, which are only losely coupled by an interface. Moreover, we present a black-box algorithm that supports the full expressive power of OWL DL. This goes beyond what existing approaches combining automated planning with ontologies can do, which only support limited description logics such as DL-Lite and description logics that are Horn. Our main algorithm relies on rewritings of the ontology-mediated planning specifications into PDDL, so that existing planning systems can be used to solve them. The algorithm relies on justifications, which allows for a generic approach that is independent of the expressivity of the ontology language. However, dedicated optimizations for computing justifications need to be implemented to enable an efficient rewriting procedure. We evaluated our implementation on benchmark sets from several domains. The evaluation shows that our procedure works in practice and that tailoring the reasoning procedure has significant impact on the performance.

Stephan Hasler, Daniel Tanneberg, Michael Gienger

Robotic planning systems model spatial relations in detail as these are needed for manipulation tasks. In contrast to this, other physical attributes of objects and the effect of devices are usually oversimplified and expressed by abstract compound attributes. This limits the ability of planners to find alternative solutions. We propose to break these compound attributes down into a shared set of elementary attributes. This strongly facilitates generalization between different tasks and environments and thus helps to find innovative solutions. On the down-side, this generalization comes with an increased complexity of the solution space. Therefore, as the main contribution of the paper, we propose a method that splits the planning problem into a sequence of views, where in each view only an increasing subset of attributes is considered. We show that this view-based strategy offers a good compromise between planning speed and quality of the found plan, and discuss its general applicability and limitations.

I. Baker, A. Peterson, Greg Brown et al.

Diana Reckien, Johannes Flacke, Richard Dawson et al.

Andrea Peirano, Mattia Barsanti, Ivana Delbono et al.

The loss of coral habitats and associated biodiversity have direct effects both on the physical dynamics of the coast and on natural resources, threatening the survival of local populations. Conservative actions, such as the creation of new Marine Protected Areas, are urgent measures needed to face climate change. Managers need fast and simple methods to evaluate marine habitats for planning conservation areas. Here, we present the application of an Ecological Quality Index (EQI), developed for regional-scale habitat maps of the Atlas of the Marine Coastal Habitats of the Kingdom of Tonga, by processing Copernicus Sentinel-2 imagery. Both the habitat mapping classification and the EQI application were focused on the importance of coral reef, seagrass and mangrove habitats, both as natural defense and sustenance for the local populations. Twelve main Pacific reef habitats were evaluated through a three-level EQI score assigned to six parameters: nursery ground, connectivity, species reservoir, fish attraction, biodiversity and primary production. The EQI was integrated into a developed georeferenced database associated to the QGIS software providing the ability to identify on the maps the area of interest and the associated habitats, and to quantify their ecological relevance. The EQI is proposed as a tool that can offer to stakeholders and environmental managers a simple and direct indicator of the value of the marine coastal environment. The index may be handled for management purposes of vast areas with remote and uninhabited islands.

Fátima Castro Moreira

Portugal’s ratification of the United Nations Convention on the Law of the Sea in 1997 brought with it the need to create an appropriate strategy to assist policy makers. This was done by the Strategic Commission for the Oceans, an entity created in 2003 with the aim of promoting a strategic plan based on the sustainable use of the ocean and its resources. More than a place allowing different uses and activities, the ocean itself should be seen as the most valuable natural resource and should be protected, preserved and valued. The political model proposed by the report suggested the creation of a specialised Council of Ministers dedicated to the formulation of policies and planning guidelines, and to the coordination of the integrated management of the sector, which together with an entity of a predominantly technical nature, reach a definition of a global policy for the sea composed of a national strategy, the regular evaluation of sea affairs and the coordination of sectoral policies. This paper begins with the analysis of this strategic reference as a governance model, articulating the intersections between the various actors. A current approach requires this governance model to be multi-level: global, continental/regional, and domestic/local.

Hazhir Aliahmadi, Maeve Beckett, Sam Connolly et al.

Land-use decision-making processes have a long history of producing globally pervasive systemic equity and sustainability concerns. Quantitative, optimization-based planning approaches, e.g. Multi-Objective Land Allocation (MOLA), seemingly open the possibility to improve objectivity and transparency by explicitly evaluating planning priorities by the type, amount, and location of land uses. Here, we show that optimization-based planning approaches with generic planning criteria generate a series of unstable "flashpoints" whereby tiny changes in planning priorities produce large-scale changes in the amount of land use by type. We give quantitative arguments that the flashpoints we uncover in MOLA models are examples of a more general family of instabilities that occur whenever planning accounts for factors that coordinate use on- and between-sites, regardless of whether these planning factors are formulated explicitly or implicitly. We show that instabilities lead to regions of ambiguity in land-use type that we term "gray areas". By directly mapping gray areas between flashpoints, we show that quantitative methods retain utility by reducing combinatorially large spaces of possible land-use patterns to a small, characteristic set that can engage stakeholders to arrive at more efficient and just outcomes.

Yuliang Li, Nitin Kamra, Ruta Desai et al.

LLMs have recently made impressive inroads on tasks whose output is structured, such as coding, robotic planning and querying databases. The vision of creating AI-powered personal assistants also involves creating structured outputs, such as a plan for one's day, or for an overseas trip. Here, since the plan is executed by a human, the output doesn't have to satisfy strict syntactic constraints. A useful assistant should also be able to incorporate vague constraints specified by the user in natural language. This makes LLMs an attractive option for planning. We consider the problem of planning one's day. We develop an LLM-based planner (LLMPlan) extended with the ability to self-reflect on its output and a symbolic planner (SymPlan) with the ability to translate text constraints into a symbolic representation. Despite no formal specification of constraints, we find that LLMPlan performs explicit constraint satisfaction akin to the traditional symbolic planners on average (2% performance difference), while retaining the reasoning of implicit requirements. Consequently, LLM-based planners outperform their symbolic counterparts in user satisfaction (70.5% vs. 40.4%) during interactive evaluation with 40 users.