Hasil untuk "Regional planning"

Jan Miller

How should two language models interact to produce better code than either can alone? The conventional approach -- a reasoning model plans, a code specialist implements -- seems natural but fails: on HumanEval+, plan-then-code degrades performance by 2.4 percentage points versus the code specialist alone. We show that reversing the interaction changes everything. When the code specialist generates freely and the reasoning model reviews instead of plans, the same two models on the same hardware achieve 90.2% pass@1 -- exceeding GPT-4o (87.2%) and O1 Preview (89.0%) -- on ~$2/hr of commodity GPU. Cross-benchmark validation across 542 problems (HumanEval+ and MBPP+) reveals a moderating variable: review effectiveness scales with specification richness, yielding 4x more improvement on richly-specified problems (+9.8pp) than on lean ones (+2.3pp), while remaining net-positive in both cases. The practical implication is twofold: compose models by their cognitive strengths (reviewers review, coders code), and invest in specification quality to amplify the returns.

Truong Xuan Khanh, Truong Quynh Hoa

Recent advances in artificial intelligence have produced systems capable of remarkable performance across a wide range of tasks. These gains, however, are increasingly accompanied by concerns regarding long-horizon developmental behavior, as many systems converge toward repetitive solution patterns rather than sustained growth. We argue that a central limitation of contemporary AI systems lies not in capability per se, but in the premature fixation of their performance frontier. To address this issue, we introduce the concept of a \emph{Dynamic Intelligence Ceiling} (DIC), defined as the highest level of effective intelligence attainable by a system at a given time under its current resources, internal intent, and structural configuration. To make this notion empirically tractable, we propose a trajectory-centric evaluation framework that measures intelligence as a moving frontier rather than a static snapshot. We operationalize DIC using two estimators: the \emph{Progressive Difficulty Ceiling} (PDC), which captures the maximal reliably solvable difficulty under constrained resources, and the \emph{Ceiling Drift Rate} (CDR), which quantifies the temporal evolution of this frontier. These estimators are instantiated through a procedurally generated benchmark that jointly evaluates long-horizon planning and structural creativity within a single controlled environment. Our results reveal a qualitative distinction between systems that deepen exploitation within a fixed solution manifold and those that sustain frontier expansion over time. Importantly, our framework does not posit unbounded intelligence, but reframes limits as dynamic and trajectory-dependent rather than static and prematurely fixed. \vspace{0.5em} \noindent\textbf{Keywords:} AI evaluation, planning and creativity, developmental intelligence, dynamic intelligence ceilings, complex adaptive systems

Ghina Amalia, Budi Heri Pirngadi, M. Zaenal Ramdhani A. Siddiq

Water needs in industrial areas can be met by using surface water and groundwater sources. However, this can cause its own problems for the region due to increased water demand. The area of the industrial area in Sumberjaya Sub-district which reaches 329,700 m² will require water for industry amounting to 8,534,678 m3/ year, with no direction in sustainable groundwater management it is feared that it will have an impact on the loss of groundwater resources and the occurrence of a crisis or scarcity of water resources. This research aims to find out directions in sustainable groundwater management that are appropriate, integrated, and can be applied to supporting industrial estates in Sumberjaya District, Majalengka Regency. This analysis was conducted using descriptive qualitative and descriptive quantitative approaches. We conducted this analysis using observation, interview, and secondary data collection methods. The analysis method in this research is the calculation method of rainwater harvesting (PAH) and the calculation of the recharge concept. From the results of the analysis of the potential and problems that exist in Sumberjaya Subdistrict, in helping to fulfil industrial water needs other than groundwater, can apply concept direction in groundwater conservation using the Rainwater Harvesting calculation method and recharge wells.

Jinsong Deng, Shaotang Cai, Weinong Wu et al.

Accurate load forecasting serves as the core foundation for grid planning and operations. Traditional load forecasting methods often rely solely on historical load data from a single region for training, making the models region-specific and leading to significant accuracy degradation when applied to other regions. This limits the generalization ability of these models to cross-regional load forecasting tasks. To address this issue, this study proposed a collaborative training strategy based on pseudo-distributed federated learning. Inspired by the pseudo-distributed concept, this strategy builds multiple sub-models by serially training load datasets from different regions on the same server. After a certain number of local epochs for each sub-model, parameter aggregation was performed. The aggregated parameters are then updated into each sub-model, and this process is repeated during each global epoch until the model converges, ultimately forming a global model capable of forecasting loads across multiple regions. Experiments demonstrated that this strategy exhibited exceptional generalization ability across various deep learning models, federated learning methods, and datasets.

Muhammad Abdul Mubdi Bindar, Muhammad Zainal Ibad, Goldie Melinda Wijayanti et al.

This paper studies the commuting patterns of students and staff at the Sumatera Institute of Technology (ITERA), a rapidly growing university located in a peri-urban area of Lampung Province, Indonesia. The research is grounded in the understanding that peri-urban commuters face unique mobility challenges shaped by transitional land use, limited infrastructure, and high motorcycle dependency. Using both statistical and spatial analyses, the article analyzed distinct travel behaviors and their socioeconomic determinants. Findings reveal that motorcycles dominate as the primary commuting mode for both groups, driven by cultural norms and constrained public transport access. Staff exhibit higher rates of vehicle ownership and longer, more dispersed commutes, while students tend to reside closer to campus and rely on borrowed motorcycles. Temporal analysis shows structured weekday travel among staff and more flexible, weekend-active patterns among students. The findings offer targeted insights for developing sustainable transportation strategies in rapidly expanding peri-urban institutions—such as promoting bicycle and pedestrian infrastructure, designing transport policies that account for widespread motorcycle borrowing among students, and differentiating mobility interventions based on the spatial dispersion and financial profiles of staff versus students.

Fangdi Chang, Hongyuan Zhang, Peiyi Zhao et al.

The growing global demand for grain drives a greater need for nitrogen (N) input. Yet, it contributes to nitrous oxide (N2O) emissions, aggravating global climate change. To tackle this dual challenge of fulfilling crop demands while maintaining or reducing N2O emissions, a field study was performed in wheat-green manure cropping system to assess the effects of varying fertilizer application (N100, N90 and N80: N fertilizer reduced by 0%, 10% and 20%) combined with green manure return strategy (GMR: green manure roots return, GMRS: green manure roots and shoots return), and wheat fallow after harvest (CK) on wheat yield and yield stability from 2020 to 2024, N2O emissions, as well as N2O emission intensity from 2022 to 2024. Results showed that, although N fertilizer combined with green manure return strategy increased spring wheat yield by 8%–22% by increasing soil mineral N contents, it decreased yield stability compared with CK. Soil N2O emissions were mainly negatively and positively regulated by pH and NO3−-N content in saline-alkali soil, respectively. N80 decreased cumulative soil N2O emission and N2O intensity by 20% and 10% compared with N100, respectively. Irrespective of the variations in N fertilizer levels, GMR decreased cumulative N2O emission and N2O intensity by 20%–34% and 22%–38% compared with GMRS, respectively. Overall, the findings highlighted N fertilizer reduced by 20% (160 kg N ha −1) with green manure roots returned in relative to normal rate (200 kg N ha −1) is a viable option to ensure spring wheat yield and alleviate soil N2O emission in saline-alkali agroecosystem.

Seyedeh Mobina Noorani, Shangde Gao, Changjie Chen et al.

Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for disaster planning, integrating the human-in-the-loop principle for transparency and adaptability. The platform is built on a representative initialized spatially constrained self-organizing map (RepSC-SOM), extending traditional SOM with adaptive geographic filtering and region-growing refinement, while AI agents can reason, plan, and act to guide the process by suggesting input features, guiding spatial constraints, and supporting interactive exploration. We demonstrate the capabilities of the platform through a case study on the flooding-related risk in Jacksonville, Florida, showing how it allows users to explore, generate, and evaluate regionalization interactively, combining computational rigor with user-driven decision making.

Deepak Prakash Kumar, Swaroop Darbha, Satyanarayana Gupta Manyam et al.

In this paper, we propose a new modeling approach and a fast algorithm for 3D motion planning, applicable for fixed-wing unmanned aerial vehicles. The goal is to construct the shortest path connecting given initial and final configurations subject to motion constraints. Our work differs from existing literature in two ways. First, we consider full vehicle orientation using a body-attached frame, which includes roll, pitch, and yaw angles. However, existing work uses only pitch and/or heading angle, which is insufficient to uniquely determine orientation. Second, we use two control inputs to represent bounded pitch and yaw rates, reflecting control by two separate actuators. In contrast, most previous methods rely on a single input, such as path curvature, which is insufficient for accurately modeling the vehicle's kinematics in 3D. We use a rotation minimizing frame to describe the vehicle's configuration and its evolution, and construct paths by concatenating optimal Dubins paths on spherical, cylindrical, or planar surfaces. Numerical simulations show our approach generates feasible paths within 10 seconds on average and yields shorter paths than existing methods in most cases.

Mattia Piccinini, Sebastiano Taddei, Johannes Betz et al.

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material.

Sebastiano Taddei, Mattia Piccinini, Francesco Biral

In this work, we present a novel approach to bias the driving style of an artificial race driver (ARD) for online time-optimal trajectory planning. Our method leverages a nonlinear model predictive control (MPC) framework that combines time minimization with exit speed maximization at the end of the planning horizon. We introduce a new MPC terminal cost formulation based on the trajectory planned in the previous MPC step, enabling ARD to adapt its driving style from early to late apex maneuvers in real-time. Our approach is computationally efficient, allowing for low replan times and long planning horizons. We validate our method through simulations, comparing the results against offline minimum-lap-time (MLT) optimal control and online minimum-time MPC solutions. The results demonstrate that our new terminal cost enables ARD to bias its driving style, and achieve online lap times close to the MLT solution and faster than the minimum-time MPC solution. Our approach paves the way for a better understanding of the reasons behind human drivers' choice of early or late apex maneuvers.

Dharshan Bashkaran Latha, Ömür Arslan

Safe autonomous navigation in a priori unknown environments is an essential skill for mobile robots to reliably and adaptively perform diverse tasks (e.g., delivery, inspection, and interaction) in unstructured cluttered environments. Hybrid metric-topological maps, constructed as a pose graph of local submaps, offer a computationally efficient world representation for adaptive mapping, planning, and control at the regional level. In this paper, we consider a pose graph of locally sensed star-convex scan regions as a metric-topological map, with star convexity enabling simple yet effective local navigation strategies. We design a new family of safe local scan navigation policies and present a perception-driven feedback motion planning method through the sequential composition of local scan navigation policies, enabling provably correct and safe robot navigation over the union of local scan regions. We introduce a new concept of bridging and frontier scans for automated key scan selection and exploration for integrated mapping and navigation in unknown environments. We demonstrate the effectiveness of our key-scan-based navigation and mapping framework using a mobile robot equipped with a 360$^{\circ}$ laser range scanner in 2D cluttered environments through numerical ROS-Gazebo simulations and real hardware~experiments.

Muhammad Nur Fajri, Wisnu Pradoto

This research examines the involvement of the community in creating and managing neighbourhood parks in Perumnas Mojosongo residential area, Surakarta, Indonesia, with the risk of transgression. The neighbourhood parks were undeveloped until residents intervened in these government land without legal permission. This research aims to explore the motives for intervening in these sites and the processes that drive the success of self-organised actions by local residents. It expands on previous studies by examining a different setting: spaces intended for public facilities in a planned residential area. The research began with a quantitative strand to select two sites that function properly as neighbourhood parks as the cases. The selection phase analysed the characteristics of the neighbourhood parks using frequency statistics by assessing the sites' condition from participant observation and official documents. Then, a multiple case study utilised semi-structured interviews to retrieve experiences from 16 key informants, residents with first-hand experiences regarding both parks’ development. The research concludes that residents' proximity to government-owned land motivates them to initiate park development, even on a small scale, when the government neglects the land. However, the legitimacy of their actions is only quasi-legitimate, as they lack formal permission from the government to utilise the land. Instead, their actions are supported by verbal permission, the perception of dispute resolution as a permit, or even the assumption of government funding as a form of approval. Social recognition from neighbourhood associations becomes a determinant of the safety of their actions.

Yuan Huang, Cheng-Tien Tsao, Tianyu Shen et al.

Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.

Konstantina Anastasiadou, Nikolaos Gavanas

The dependence of road transport on fossil fuels and its contribution to greenhouse gas (GHG) and pollutant emissions are main concerns leading to the need for shifting toward alternative energy sources and, namely, electromobility. The current paper aims to identify the key determinants affecting the consumer adoption of electric vehicles (EVs), focusing on private passenger cars. Toward this purpose, a systematic review of recent international literature is conducted in order to identify motivators and barriers, which are then categorized following the PESTLE (Political–Economic–Social–Technological–Legal–Environmental) approach. Based on the review results, main policy implications and recommendations are discussed. A main conclusion is that the recent literature highlights a wide array of determinants, without converging as to which ones are the most influential regarding EV adoption by consumers. Another conclusion is that the environmental aspects are less important for consumers than anticipated, despite the concerns about climate change and renewable energy transition.

Mohamed El Asawy, Eman Badawy Ahmed

تسعى الدولة الي حوکمة العمران في مصر وذلک من خلال إصدار العديد من القوانين والتشريعات التخطيطية لرفع کفاءة التجمعات العمرانية، وتعتبر التعديلات المقترح تنفيذها على بنود قانون البناء الموحد من أهم التشريعات القانونية محل الدراسة في وقتنا الحالي. تتناول الدراسة تحليل وتقييم الأثر البيئي جراء تطبيق التعديلات المقترحة على متوسط الطاقة المستهلکة بالوحدات السکنية سواء بالسلب أو الإيجاب، مع ذکر خاص لمدى توافق تلک التعديلات مع التوصيات المقترحة بأکواد البناء المصري المعنية بالنواحي البيئية للمباني السکنية، بالاضافة الي بعض التعديلات المقترحة والتي يوصي البحث بضرورة ضمها الي قانون البناء الموحد. منهجية البحث: يتبع البحث المنهج الاستقرائي من خلال دراسة القوانين والمعايير الحاکمة لتصميم الوحدات السکنية والتي تشمل قانون البناء الموحد رقم 119 لسنة 2008 والضوابط والاشتراطات التخطيطية والبنائية للمدن المصرية 2020, والکود المصري لتحسين کفاءة استخدام الطاقة في المباني, بالاضافة الي الکود المصري للتهوية في المباني. ثم المنهج التطبقي وذلک من خلال اقتراح النموذج السکني للدراسة التطبيقية واستخدام برامج المحاکاة البيئية (designbuilder and energy plus) لقياس تاثير المتغيرات التصميمية المقترحة (ارتفاع المبنى والمسافات البينية بين المباني المتقابلة, والبروزات الخارجية, وطبقات الغلاف الخارجي المصمت, وأبعاد ونسب الفتحات الخارجية, والمناور السکنية الداخلية) علي استهلاک الطاقة بالمبني السکني. هذا وتشير نتائج الدراسة البحثية إلى أن تعديلات قانون البناء الموحد بمنظومة الاشتراطات الجديده2020 ذات تأثير ايجابي في زيادة الوفر في الطاقة المستهلکة للوحدات السکنية عن مثيلاتها في حال تطبيق قانون البناء الموحد لمقدار التوفير في الطاقة المستهلکة بمعدل 4% للمناور السکنية وبنسبة تتراوح ما بين 14 : 17% للبروزات ومن 12 : 16% لتأثير عرض الطريق وعلاقته بارتفاع المبني. Egypt seeks to govern urbanization by issuing many planning laws to increase the efficiency of urban communities. The proposed amendments to the Building Law are considered one of the most important legal studies during these days. The research focuses on analyzing and evaluating the environmental impact of applying amendments on the average energy consumption in residential buildings, whether negatively or positively. In addition to some proposed amendments, which the research recommends be included in the amendments. Research Methodology depends on the inductive approach by studying the laws for the housing unit’s design, which include the Building Law No. 119 of 2008, the planning and building requirements for Egyptian cities 2020, the Egyptian Code for Energy in Buildings, and the Egyptian code for ventilation in buildings. The second part depends on the applied approach by proposing the residential model for the applied study and using the environmental simulation programs (design builder and energy plus) to measure the effectiveness of the proposed design variables (building height, distances between opposite buildings, external shades, components of the building's external envelope, openings and courtyard) on the energy consumption of the residential building. The results of the study indicate that the modification of the building law with the new requirements (2020) has a positive effect on the building's energy saving compared to the case of applying the building law. The modifications achieve 4% in energy savings for the courtyard, 14:17 % for the cantilevers, and 12:16 % for the relationship between road width and the building height.

Nadiya Tri Utami, Bitta Pigawati

Palembang city has experienced an increase in its population. Population growth results in an increase in activities which enlarge the built-up areas. The increase of built-up areas is one of the indicators of urban growth. The increase in built-up areas is inversely proportional to the vegetation area. Reduced vegetation area might cause an increase in land surface temperature. The aim of the study was to analyze the correlation between urban growth and changes in land surface temperature in Palembang City using descriptive quantitative method and spatial analysis on the data obtained from remote sensing images. The result shows that in 1998-2018, Palembang City has developed to the north (Sukarami District) and to the west (Ilir Barat I District). There has been an increase in the temperature, documented as 2.12°C. There is a correlation between urban growth and changes in land surface temperature in Palembang City

Seng Boon Lim, Tan Yigitcanlar

Participatory governance is widely viewed as an essential element of realizing planned smart cities. Nonetheless, the implementation of e-participation platforms, such as the websites and mobile applications of civic authorities, often offer ambiguous information on how public voices may influence e-decision-making. This study aims to examine the status of participatory governance from the angle of e-participation platforms and from the broader scope of linking e-platforms to a smart city blueprint. In order to achieve this aim, the study focuses on shedding light on the e-governance space given to smart city realization in a developing country context—i.e., Malaysia. The Putrajaya and Petaling Jaya smart cities of Malaysia were selected as the testbeds of the study, which used the multiple case study methodology and multiple data collection designs. The analyses were done through the qualitative observations and quantitative descriptive statistics. The results revealed that both of the investigated smart city cases remained limited in their provision of e-decision-making space. The inefficiency of implementing planned initiatives to link the city blueprints to e-platforms was also evidenced. The study evidenced that the political culture of e-decision-making is undersized in Malaysia, which hinders the achievement of e-democracy in the smart cities’ development. This study has contributed a case report on a developing country’s smart cities, covering the participatory issues from the angle of e-participation and e-platforms.

Ashley D. Ross, Ali Nejat, Virgie Greb

Adaptive planning is ideally suited for the deep uncertainties presented by climate change. While there is a robust scholarship on the theory and methods of adaptive planning, this has largely neglected how adaptive planning is affected by existing planning institutions and how to move forward within the constraints of traditional planning organizations. This study asks: How do existing traditional planning institutions support adaptive planning? We explore this for flood planning in the Lower Rio Grande Valley of Texas, United States. We draw on county hazard plan and regional flood plan documents as well as transcripts of regional flood planning meetings to explore the emergent topics of these institutional outputs. Using Natural Language Processing to analyze this large amount of text, we find that hazard plans and discussions developing these plans are largely lacking an adaptive approach.

Yingxue Rao, Jingyi Dai, Deyi Dai et al.

With rapid urbanization destroying the ecological environment, scholars have focused on ways to coordinate harmonious development using urban spatial layouts and landscape ecological security. To explore landscape ecological security (the landscape elements, spatial positions and connections that are of key significance to the health and safety of ecological processes) from the perspective of urban form evolution pattern will help to open a new perspective of urban management research, and become the basic work of urban space policy and the implementation of the beautiful China strategy. Based on urban growth and land use data from 356 cities in China, this study applied a geographically weighted regression (GWR) model to quantify the impact of China’s urban growth pattern on landscape ecological security at the spatial level. The research results show that: (1) To some extent, the infilling growth pattern has a certain effect on the enhancement of regional landscape ecological security; (2) In the three control variables (DEM, Population density and GDP), the following conclusions are drawn: regional landscape planning should reasonably allocate landscape resources according to the local topographic features to obtain a higher landscape ecological security; The increase of population density leads to the fragmentation and diversity of the landscape in some regions, which makes the landscape ecological security weak; more economically developed areas have stronger landscape ecological security. This paper highlights the importance of urban growth patterns to landscape ecological security. In addition, considering the different urban evolution trajectories in developed and developing countries, this study proposes targeted development recommendations, providing a reference for urban managers to formulate reasonable development policies and to realize sustainable development with the goal of landscape safety management and control.

Dongping Qi, Adam Dhillon, Alexander Vladimirsky

Classical deterministic optimal control problems assume full information about the controlled process. The theory of control for general partially-observable processes is powerful, but the methods are computationally expensive and typically address the problems with stochastic dynamics and continuous (directly unobserved) stochastic perturbations. In this paper we focus on path planning problems which are in between -- deterministic, but with an initial uncertainty on either the target or the running cost on parts of the domain. That uncertainty is later removed at some time $T$, and the goal is to choose the optimal trajectory until then. We address this challenge for three different models of information acquisition: with fixed $T$, discretely distributed and exponentially distributed random $T$. We develop models and numerical methods suitable for multiple notions of optimality: based on the average-case performance, the worst-case performance, the average constrained by the worst, the average performance with probabilistic constraints on the bad outcomes, risk-sensitivity, and distributional-robustness. We illustrate our approach using examples of pursuing random targets identified at a (possibly random) later time $T$.