Hasil untuk "City planning"

Taylor Paul, William Regli

This work pursues automated planning and scheduling of distributed data pipelines, or workflows. We develop a general workflow and resource graph representation that includes both data processing and sharing components with corresponding network interfaces for scheduling. Leveraging these graphs, we introduce WORKSWORLD, a new domain for numeric domain-independent planners designed for permanently scheduled workflows, like ingest pipelines. Our framework permits users to define data sources, available workflow components, and desired data destinations and formats without explicitly declaring the entire workflow graph as a goal. The planner solves a joint planning and scheduling problem, producing a plan that both builds the workflow graph and schedules its components on the resource graph. We empirically show that a state-of-the-art numeric planner running on commodity hardware with one hour of CPU time and 30GB of memory can solve linear-chain workflows of up to 14 components across eight sites.

Sijia Chen, Di Niu

Large language models (LLMs), when guided by explicit textual plans, can perform reliable step-by-step reasoning during problem-solving. However, generating accurate and effective textual plans remains challenging due to LLM hallucinations and the high diversity of task-specific questions. To address this, we draw inspiration from human Implicit Cognition (IC), the subconscious process by which decisions are guided by compact, generalized patterns learned from past experiences without requiring explicit verbalization. We propose iCLP, a novel framework that enables LLMs to adaptively generate latent plans (LPs), which are compact encodings of effective reasoning instructions. iCLP first distills explicit plans from existing step-by-step reasoning trajectories. It then learns discrete representations of these plans via a vector-quantized autoencoder coupled with a codebook. Finally, by fine-tuning LLMs on paired latent plans and corresponding reasoning steps, the models learn to perform implicit planning during reasoning. Experimental results on mathematical reasoning and code generation tasks demonstrate that, with iCLP, LLMs can plan in latent space while reasoning in language space. This approach yields significant improvements in both accuracy and efficiency and, crucially, demonstrates strong cross-domain generalization while preserving the interpretability of chain-of-thought reasoning.

Ghulam Mudassir, Antinisca Di Marco, Giordano d'Aloisio

Natural disasters always have several effects on human lives. It is challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastructures and facilities with the available resources (mainly budget and time). Governments always define plans and policies according to the law and political strategies that should maximise social benefits. The severity of damage and the vast resources needed to bring life back to normality make such reconstruction a challenge. This article is the extension of our previously published work by conducting comprehensive comparative analysis by integrating additional deep learning models plus random agent which is used as a baseline. Our prior research introduced a decision support system by using the Deep Reinforcement Learning technique for the planning of post-disaster city reconstruction, maximizing the social benefit of the reconstruction process, considering available resources, meeting the needs of the broad community stakeholders (like citizens' social benefits and politicians' priorities) and keeping in consideration city's structural constraints (like dependencies among roads and buildings). The proposed approach, named post disaster REbuilding plAn ProvIdeR (REPAIR) is generic. It can determine a set of alternative plans for local administrators who select the ideal one to implement, and it can be applied to areas of any extension. We show the application of REPAIR in a real use case, i.e., to the L'Aquila reconstruction process, damaged in 2009 by a major earthquake.

Qian Cao, Jielin Chen, Junchao Zhao et al.

The spatial layout of urban sites shapes land-use efficiency and spatial organization. Traditional site planning often relies on experiential judgment and single-source data, limiting systematic quantification of multifunctional layouts. We propose a Site Planning Layout Indicator (SPLI) system, a data-driven framework integrating empirical knowledge with heterogeneous multi-source data to produce structured urban spatial information. The SPLI supports multimodal spatial data systems for analytics, inference, and retrieval by combining OpenStreetMap (OSM), Points of Interest (POI), building morphology, land use, and satellite imagery. It extends conventional metrics through five dimensions: (1) Hierarchical Building Function Classification, refining empirical systems into clear hierarchies; (2) Spatial Organization, quantifying seven layout patterns (e.g., symmetrical, concentric, axial-oriented); (3) Functional Diversity, transforming qualitative assessments into measurable indicators using Functional Ratio (FR) and Simpson Index (SI); (4) Accessibility to Essential Services, integrating facility distribution and transport networks for comprehensive accessibility metrics; and (5) Land Use Intensity, using Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) to assess utilization efficiency. Data gaps are addressed through deep learning, including Relational Graph Neural Networks (RGNN) and Graph Neural Networks (GNN). Experiments show the SPLI improves functional classification accuracy and provides a standardized basis for automated, data-driven urban spatial analytics.

Sijie Yang, Binyu Lei, Filip Biljecki

Ensuring liveability and comfort is one of the fundamental objectives of urban planning. Numerous studies have employed computational methods to assess and quantify factors related to urban comfort such as greenery coverage, thermal comfort, and walkability. However, a clear definition of urban comfort and its comprehensive evaluation framework remain elusive. Our research explores the theoretical interpretations and methodologies for assessing urban comfort within digital planning, emphasising three key dimensions: multidimensional analysis, data support, and AI assistance.

Yi-Hsuan Lin, Lalitphat Khongsomchit, Sakdirat Kaewunruen et al.

IntroductionBuilding Information Modelling (BIM) has emerged as a multidisciplinary methodology that integrates information-rich data with virtual representations to support the management of built assets throughout their lifecycle. While BIM is increasingly adopted in architecture, engineering, and construction (AEC) industries and demonstrates significant value in infrastructure projects; however, its application in the railway sector remains limited. The complexity of railway networks, combined with the growing demand for transit projects, presents unique challenges that hinder effective implementation.MethodsThis study investigates the barriers of BIM adoption within the railway industry through a structured questionnaire distributed to professionals and a subsequent detailed analysis of responses.ResultsThis study identifies critical gaps in current BIM practices and highlights several severe obstacles that require urgent attention. Feedback reveals key challenges across four main areas: (1) Technology, (2) Market, (3) Socio-cultural factors, and (4) Policy.DiscussionBy outlining these barriers and suggesting potential solutions, the study provides valuable insights for stakeholders and identifies future research directions to advance BIM integration in railway projects.

Arda Sarp Yenicesu, Sepehr Nourmohammadi, Berk Cicek et al.

This article introduces a novel heuristic for Task and Motion Planning (TAMP) named Interpretable Responsibility Sharing (IRS), which enhances planning efficiency in domestic robots by leveraging human-constructed environments and inherent biases. Utilizing auxiliary objects (e.g., trays and pitchers), which are commonly found in household settings, IRS systematically incorporates these elements to simplify and optimize task execution. The heuristic is rooted in the novel concept of Responsibility Sharing (RS), where auxiliary objects share the task's responsibility with the embodied agent, dividing complex tasks into manageable sub-problems. This division not only reflects human usage patterns but also aids robots in navigating and manipulating within human spaces more effectively. By integrating Optimized Rule Synthesis (ORS) for decision-making, IRS ensures that the use of auxiliary objects is both strategic and context-aware, thereby improving the interpretability and effectiveness of robotic planning. Experiments conducted across various household tasks demonstrate that IRS significantly outperforms traditional methods by reducing the effort required in task execution and enhancing the overall decision-making process. This approach not only aligns with human intuitive methods but also offers a scalable solution adaptable to diverse domestic environments. Code is available at https://github.com/asyncs/IRS.

Oliver Niggemann, Gautam Biswas, Alexander Diedrich et al.

The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver, Canada, brought together researchers to discuss recent advances in AI planning methods for Cyber-Physical Systems (CPS). CPS pose a major challenge due to their complexity and data-intensive nature, which often exceeds the capabilities of traditional planning algorithms. The workshop highlighted new approaches such as neuro-symbolic architectures, large language models (LLMs), deep reinforcement learning and advances in symbolic planning. These techniques are promising when it comes to managing the complexity of CPS and have potential for real-world applications.

Lia Nurcahyani, Dyah Widiyastuti, Wiwit Estuti et al.

Background: Stunting leads to increased morbidity and mortality among children. To accelerate stunting reduction, family assistance teams support at-risk families, requiring engaging and accessible educational resources. However, existing educational media materials are fragmented and lack a comprehensive approach, resulting in gaps during family assistance sessions. To improve accessibility and efficacy, a comprehensive, technology-based educational tool is necessary. Objectives: To develop a Stunting Prevention Education Media Package (PaSti PenTing) based on technology and local wisdom. Methods: This study used a Research and Development approach conducted in Cirebon City. The stages included the formulation of basic concepts, and in-depth interviews with experts, namely the Chairman of the Central Board of the Indonesian Midwives Association, the Head of the Cirebon City Health Office, the Head of the Cirebon City Women's Empowerment, Child Protection, Population Control and Family Planning Office and lecturers with S3 backgrounds. These interviews provided input related to the materials used for designing the PaSti PenTing. The research instrument uses in-depth interview guidance and data analysis was carried out using content analysis. Results: Based on expert input, the PaSti PenTing design was developed. The main menu consists of an introduction and a menu for target groups (teenagers, brides-to-be, pregnant women, postpartum mothers, and toddlers). Each menu contains educational materials. Conclusions: PasTi PenTing is a comprehensive media that can be used by the assistance team and families at risk of stunting to improve knowledge, attitudes, and behaviors in stunting prevention.

Hend A. Elhawy, Laila Mohamed Khodeir, Ahmed Khaled

The challenges related to sanitation and water management in new urban communities have contributed to the emergence of visions of Integrated Urban Water Management (IUWM) and Water Sensitive Urban Design in cities worldwide to keep pace with these subtle changes in the shape of the urban environment. This research paper aims to monitor the main obstacles and challenges facing residents of new cities in Egypt and the environmental impacts resulting from those challenges in the urban context. A questionnaire was conducted among a group of the population. This study took the economic housing model for low-income people in the 6th of October City in Egypt as a case study. As a result, the study found that the main challenges facing the residents of these areas and their impacts are:1) Lack of regular maintenance. 2) Health risks to the population. 3) Damaging of public parks and gardens. 4) Pollution of the surrounding environment, including soil and water. 5) Lack of state funding for maintenance and Limited financial resources available for projects in this type of housing. 6) Lack of government engagement in preparing programs to raise population awareness and clarity in environmental goals and quality of life policies. 7) Lack of community initiatives to participate in decision-making. 8) Lack of political guidance on planning processes for sustainable infrastructure.

Yewon Lee, Andrew Z. Li, Philip Huang et al.

Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. This can be inefficient because, typically, candidate task plans resulting from the tree search ignore geometric information. This often leads to motion planning failures that require expensive backtracking steps to find alternative task plans. We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes the hybrid optimization problem into a continuous domain. This allows us to leverage gradients from differentiable physics simulation to fully optimize discrete and continuous plan parameters for TAMP. In particular, we solve the optimization problem using a gradient-based variational inference algorithm called Stein Variational Gradient Descent. This allows us to find a distribution of solutions within a single optimization run. Furthermore, we use an off-the-shelf differentiable physics simulator that is parallelized on the GPU to run parallelized inference over diverse plan parameters. We demonstrate our method on a variety of problems and show that it can find multiple diverse plans in a single optimization run while also being significantly faster than existing approaches.

Mohamed Harmanani

The coronavirus disease 2019 (COVID-19) pandemic has been ongoing for around 3 years, and has infected over 750 million people and caused over 6 million deaths worldwide at the time of writing. Throughout the pandemic, several strategies for controlling the spread of the disease have been debated by healthcare professionals, government authorities, and international bodies. To anticipate the potential impact of the disease, and to simulate the effectiveness of different mitigation strategies, a robust model of disease spread is needed. In this work, we explore a novel approach based on probabilistic planning and dynamic graph analysis to model the spread of COVID-19 in indoor spaces. We endow the planner with means to control the spread of the disease through non-pharmaceutical interventions (NPIs) such as mandating masks and vaccines, and we compare the impact of crowds and capacity limits on the spread of COVID-19 in these settings. We demonstrate that the use of probabilistic planning is effective in predicting the amount of infections that are likely to occur in shared spaces, and that automated planners have the potential to design competent interventions to limit the spread of the disease. Our code is fully open-source and is available at: https://github.com/mharmanani/prob-planning-covid19 .

Changmei Liang, Changmei Liang, Changmei Liang et al.

The cost-benefit is a key factor when selecting an appropriate sponge city construction scheme. The research of applying intelligent technology to find cost-benefit efficient planning and construction of sponge city is urgently required. This paper established a multi-objective simulation optimization framework of sponge city construction which considered minimization of runoff control rate, pollutant control rate and life-cycle cost Non-dominated sorting genetic algorithm (NSGA-II) was successfully coupled to Storm water management model to complete the simulation-optimization process. A case study in Xining, China, was conducted to demonstrate the proposed framework. The results of this research suggested that 1) different sponge city construction schemes lead to different runoff control rates and pollutant control rates although under the same investment; 2) the runoff control rate and pollutant control rate total suspended solids decreased with the increase of the rainfall return period, while the cost of sponge city construction increased with the increase of rainfall return period. Furthermore, for T = 2-year, the sponge facility exhibited the most stable control effect on runoff and pollutants among the three different return periods (T = 2-year, 5-year, 10-year); 3) sponge city construction exhibited a “cost-benefit” efficient interval. For T = 2-year, the cost-benefit high efficiency interval of sponge city construction is calculated between 1.2 billion and 1.8 billion; for T = 5-year, the interval is between 1.2 billion and 1.8 billion, while for T = 10-year, the interval is between 1.3 billion and 2.1 billion. The above observations provide reference for reasonable and effective sponge city construction in Xining, China.

Reham N. Nagar, Ahmed M.M. Hany, Mohammed A. Mohammed

Background: The lactational amenorrhea method (LAM) is 98% successful in preventing future pregnancy within the first six months after delivery. Correct application and adherence to LAM's three criteria have an impact on its efficiency and efficacy. Objectives: Assess the knowledge about lactational amenorrhea method (LAM) of women attending primary health care in QENA city, identify attitude toward LAM and determine the proportion of women use LAM was also investigated the factors determining practice of LAM. Patients and methods: This cross-sectional study on 400 breastfeeding women in reproductive ages 15:49 years was undertaken at family planning clinics of five primary health care units in Qena city to determine the prevalence of LAM usage. A structured questionnaire was utilised there. Results: Prevalence of usage lactational amenorrhea method (LAM) among the women attend primary health care units in QENA city was 65.3%. The failure of LAM and occurrence pregnancy was 29.9% among women use LAM.There is a significant positive correlation between duration of breastfeeding and duration of exclusive breastfeeding and duration of amenorrhea. According to logistic regression analysis mother age, mother education, family size, socioeconomic level significant related to LAM practice. Practicing of LAM was higher in women from rural residence, not working women and educated women level with lower socioeconomic level. Conclusion: According to the findings rural women are more likely to practice LAM. According to the findings, health care practitioners should encourage the mothers to breastfeed their children and provide women with good lactational amenorrhea advice.

Sayed A. Sayed, Yasser Abdel-Hamid, Hesham Ahmed Hefny

Abstract The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve many traffic issues, such as traffic congestion issues. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of artificial intelligence approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

Villegas Jhonnel P., Bauyot Mary Fil M., Sacro Jeralyn H. et al.

Many countries across the globe, including the Philippines, have implemented Gender and Development (GAD) policies to reduce gender biases and promote equality. However, mainstreaming efforts have been challenging due to the scarce availability of learning resources in the local context. This study is an initiative to provide the GAD Focal Point System (GFPS) in the Department of Education – City of Mati with a primary reference in training their GAD Focal Persons on Gender Sensitivity, GAD Planning, and Budgeting (GPB). The coverage is based on their learning needs and gaps determined through participatory strategies involving the school administrators, teachers, students, and parents. Also, the results of the gender audit using the Gender Mainstreaming Evaluation Framework (GMEF) are used as a baseline in module development. The framework presents strategic directions that agencies need to follow to advance across stages. It was found that Mati’s primary and secondary schools are in the initial stages of gender mainstreaming. As such, GAD’s basic concepts and definitions are introduced, along with various forms of gender-based violence and the appropriate mechanisms to address them aptly. The essential steps in planning and budgeting are also detailed, providing an active experience among the participants. It is imperative to sustain GAD capacity-building initiatives to catalyze a more directed and engaged policy framework.

Serafina Amoroso

La aspiración principal de este artículo es que las breves notas que contiene puedan inspirar un debate político sobre la posibilidad de establecer una renovada relación entre la ciudad y el contexto territorial de Florencia y el río que los atraviesa (el Arno) para poder replantearla sobre la base de una mirada renovada y desde una perspectiva de género, no sólo en términos espaciales y físicos sino también en términos temporales y socioculturales. La metodología de trabajo adoptada aúna la investigación bibliográfica de tipo documental con la experiencia y observación directa de los lugares analizados. El texto se estructura en una primera parte en la que se contextualiza su contenido en el marco teórico del realismo agencial de Karen Barad, en una segunda parte en la que se explora brevemente en qué términos la relación e interacción entre ciudad, territorio y río ha favorecido el desarrollo de ciertas actividades a expensas de otras, y finalmente en una parte conclusiva en la que, ofreciendo una lectura crítica de la situación actual desde una perspectiva de género, se puedan vislumbrar potenciales pistas para delinear estrategias futuras para renovarla.

Geraud Nangue Tasse, Benjamin Rosman, Steven James

We propose world value functions (WVFs), a type of goal-oriented general value function that represents how to solve not just a given task, but any other goal-reaching task in an agent's environment. This is achieved by equipping an agent with an internal goal space defined as all the world states where it experiences a terminal transition. The agent can then modify the standard task rewards to define its own reward function, which provably drives it to learn how to achieve all reachable internal goals, and the value of doing so in the current task. We demonstrate two key benefits of WVFs in the context of learning and planning. In particular, given a learned WVF, an agent can compute the optimal policy in a new task by simply estimating the task's reward function. Furthermore, we show that WVFs also implicitly encode the transition dynamics of the environment, and so can be used to perform planning. Experimental results show that WVFs can be learned faster than regular value functions, while their ability to infer the environment's dynamics can be used to integrate learning and planning methods to further improve sample efficiency.

Srishti Mishra, Srinjoy Das

Leveraging civic data, divided into 3 categories spending, infrastructure and citizen feedback, can present a clear picture of the priorities, performance, and pain-points of a city. Data driven insights highlight the current issues faced by citizens as well as disparity between government spending and quality of work, and can aid in providing effective solutions. City infrastructure; footpaths, lighting, and parks, describe the living quality of citizens and can be compared to the annual spending in these sectors to track effectiveness. Analyzing complaints ensures citizen feedback is taken into account during both long-term planning and in short-term solutions to pinpoint critical areas of improvement. Integrating an analysis loop and data driven dashboards can help in improving performance of municipal corporations, while adding transparency between citizens and the city officials. In the paper, constituency rankings across the city infrastructure indicated a low importance towards greenery in terms of Parks, where each constituency has less than 2% of their area as a park. As populations in these areas are already high and increasing, this is likely to worsen in the coming years. Comparing the results with complaints, surprisingly the rankings of footpaths in constituencies were contrary to the number of complaints in these constituencies, with high ranking constituencies receiving the highest number of complaints, which would require further analysis. In terms of street lights, the areas with low quality lighting were associated with a large number of complaints from citizens, indicating that action needs to be taken immediately. Overall, a text analysis of complaints across constituencies reflected the everyday struggles of the city with the top keywords 'roads' and 'vehicles', followed by 'footpaths' and 'garbage', which are both critical problems in Bangalore City today.

Yiyu Li, Qingxu Huang, Ling Zhang et al.

As a proxy for human activity, per capita urban land has great significance for urban planning. We still lack a comprehensive understanding of per capita urban land from the perspective of urban–rural gradients. Thus, based on the concentric buffering method and the dynamic-time-warp clustering method, this research analyzes the urban–rural gradient of the per capita urban land of 345 cities in China in 2000, 2010, and 2016. We find that the per capita urban land in China grew from 110.2 m²/person in 2000 to 118.9 m²/person, increasing by 7.9%. The urban–rural gradient of the per capita urban land can be classified into six types: (1) large city with a mono peak; (2) large city with a fluctuating increase; (3) medium city with a mono peak; (4) medium city with a declining trend; (5) small city with a mono peak, and (6) small city with a declining trend. In addition, most cities shifted from a mono-peak type to a declining type, which suggested that the low-density, sprawling development was intensifying. The dynamic-time-warp clustering method used in this research can effectively compare trends of the urban–rural gradient of per capita urban land across cities, which can be applied to the analysis of the urban–rural gradient of air pollution, urban green space, and urban heat islands.