Planning Approach Research Articles

Geomorphic characterization by integrating automated approaches for sustainable land use planning in the earthquake-prone Himalayan region of Uttarakhand, India

This study leverages two cutting-edge automated landform classification approaches—Topographic Position Index (LCTPI) and Geomorphon (LCGm)—to map and categorize landforms in Uttarakhand, a strategically vital region in the Himalayas frequently impacted by natural hazards. Situated at the subduction boundary of the Indian and Eurasian tectonic plates, Uttarakhand's complex landscape demands innovative approaches for sustainable land use planning. Our analysis links classified landforms to critical factors such as slope, geology, soil types, land use land cover (LULC) patterns, fault density, and regional seismicity. Results reveal that high ridges, deep valleys (LCTPI), and slopes (LCGm), predominantly covered by trees and rangelands, are associated with lithosols and dystric cambisols soil types from the Precambrian period. In contrast, flat areas, shoulders, footslopes, U-shaped valleys, open slopes, and mild slope ridges, mainly used for agriculture and urban development, exhibit lower seismic activity. Notably, shallow earthquakes are more frequent in deep valleys and high ridges, which have low to moderate fault density and are majorly occupied by trees and rangelands. This research offers a robust framework for geomorphic characterization that can be adapted to other regions, despite the inherent limitations of automated studies, such as DEM resolution and neighbourhood cell size. Our approach supports sustainable land use planning, biodiversity preservation, natural resource management, and disaster risk reduction, ultimately fostering resilient futures in the built environment. This study emphasizes the necessity of integrating geomorphic insights into the planning process to mitigate natural hazards and enhance environmental sustainability in the Himalayan region by providing actionable insights for land use planners and policymakers.

Protocol for COACH, an evidence-based intervention for improved head impact safety in youth American football developed using a community-engaged approach.

Subconcussive, repetitive head impacts sustained in collision sports may negatively affect brain health. American football practices are controlled environments amenable to intervention. Engaging community members is essential for successful development, implementation, and sustainability of viable interventions. The objective of this study is to develop and pilot test an evidence-based intervention to reduce head impact exposure in youth American football (i.e., football), using a community-engaged approach. This manuscript describes the co-design of the intervention and associated implementation plan and the study protocol for evaluating the effectiveness and feasibility of the intervention and implementation plan. In the first part of this study, focus groups with parents and coaches, and individual interviews with organizational leaders associated with two teams at the middle school level were conducted. An anonymous survey assessing beliefs and perceptions of non-concussive head impacts was given to parents, coaches, and organizational leaders within the local youth football league. Following the football season, qualitative and quantitative data describing determinants of head acceleration events in football were shared with 12 stakeholders of coaches, league and school administrators, parents, an athletic trainer, and local university player development director. Together, we co-designed COACH (COmmunities Aligned to reduce Concussion and Head impact exposure) and implementation plan using a strategic planning approach. The preliminary effectiveness and feasibility were assessed in the second part of this study. Youth football players participating on the teams in year 1 (control teams) were fitted with mouthpiece-based head kinematic sensors which measure head acceleration events (HAEs). HAEs were collected and quantified during team activities. Preliminary effectiveness of the intervention to reduce HAEs was measured among two new teams pilot testing COACH with mouthpiece-based sensors, while simultaneously monitoring implementation of the intervention. We report our study design and evaluation, and opportunities and challenges with our approach. The results will inform a future full-scale pragmatic trial to assess the implementation and effectiveness of the intervention program. NCT04908930.

An effective path planning approach for robot welding considering redundant kinematics

Robot welding with redundant kinematics demands precise coordination of the welding path and redundant axis motion for stable operation. This paper introduces an effective G3 continuous path planning approach for a 6-DOF friction stir welding robot, integrating redundant axis motion to enhance welding stability and quality. The method encompasses a two-pronged strategy: a five-axis path corner smoothing technique and a motion synchronization strategy. Initially, a robust toolpath preprocessing algorithm is presented to mitigate curvature extremities of the inserted smoothing curves within predefined error limits. Subsequently, leveraging G3 continuity criteria, a B-spline-based smoothing method is advanced for refining tool position and orientation paths, with an analytical determination of spline control points facilitated by an explicit smoothing error expression. An effective motion synchronization technique is then proposed, which formulates the tool position, tool orientation, and redundant axis path as explicit functions of the position path arc length. The effectiveness of the proposed method is demonstrated through simulations and experiments on a robot welding platform, with the integration of a jerk-continuous feedrate profile for smooth motion execution. The findings indicate a significant improvement in robotic welding quality by integrating the proposed path planning method with existing workpiece posture optimization techniques.

Unsupervised Active Visual Search With Monte Carlo Planning Under Uncertain Detections.

We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more plausible and robust to detector failures compared to state-of-the-art methods: i) it is unsupervised as it does not need any training sessions. ii) During the exploration, a probability distribution on the 2D floor map is updated according to an intuitive mechanism, while an improved belief update increases the effectiveness of the agent's exploration. iii) We incorporate the awareness that an object detector may fail into the aforementioned probability modelling by exploiting the success statistics of a specific detector. Our solution is dubbed POMP-BE-PD (Pomcp-based Online Motion Planning with Belief by Exploration and Probabilistic Detection). It uses the current pose of an agent and an RGB-D observation to learn an optimal search policy, exploiting a POMDP solved by a Monte-Carlo planning approach. On the Active Vision Dataset Benchmark, we increase the average success rate over all the environments by a significant 35 % while decreasing the average path length by 4 % with respect to competing methods. Thus, our results are state-of-the-art, even without any training procedure.

Adaptive demand planning models: Utilizing reinforcement learning to address contingencies and dynamic market conditions in supply chains

There are remarkable opportunities in supply chain management associated with using the reinforcement learning (RL) approach in demand planning. As opposed to numerous other techniques, such as modeling and forecasting and applying them over a certain period, RL allows for the making and adapting these decisions in real-time, depending on the current demand and other conditions in the market. Supplementary to this, RL-based models enable supply chains to constantly adapt to shifts since they directly update knowledge from incoming data, making them less susceptible to economic shocks or other supply uncertainties. This flexibility is particularly important for contemporary supply chains in uncertain global environments where conventional, deterministic demand planning techniques cannot address changing needs. In this research, we discover how RL-based models could reduce demand volatility and react to contingencies. As such, it is best suited for industries that are required to respond flexibly and faster. This paper enlightens RL on the benefits of demand planning. It provides live examples that give insight into how it may be used to improve inventory, reduce cost, and improve decision-making. Regarding the specific research questions, this study helps investigate the methodologies, evaluation metrics, and case outcomes that shape RL's potential for changing demand planning and improving supply chain adaptability for future research on adaptive supply chain management.

Concept of architectural formation of construction production complexes in the cities of Donbass

The scientific research is a continuation of the author’s publications and for the first time provides a complete conceptual set of principles and techniques, systematically covering all levels of the architectural and planning organization of construction production complexes. A concept for the architectural formation of construction industrial complexes in the cities of Donbass is proposed, which includes recommendations for the formation of these complexes. The urban planning approach to the selection of territories of non-operating coal mines is generalized. An analysis of existing buildings and structures of the coal mine and the features of construction production complexes was also carried out.

What Affects Citizens’ Perception of Public Infrastructure Decision-Making in a Disaster Setting?

This study examines citizens’ views on public-private partnerships (PPPs) and their sector-based preferences between private or nonprofit organizations as partners with government entities in providing public services for local infrastructures. Using survey data collected in Texas, this study finds that risk awareness and trust in institutional actors significantly influence citizens’ perceptions of public service delivery models, while a variety of information sources differently affect their perceptions. This study highlights citizens’ perception of nonprofits’ role in cross-sector collaborative governance, suggesting their involvement in implementing community resilience policies based on citizen preferences. Empirical findings provide insights for policymaking, advocating inclusive approaches in resilience planning and engaging diverse institutional actors, including citizens, in critical infrastructure development.

Urban travel carbon emission mitigation approach using deep reinforcement learning.

The urbanization process has led to a significant increase in energy consumption and carbon emissions, which can be mitigated through scientific urban planning and management. This research proposes a bottom-up urban carbon emission mitigation strategy based on deep reinforcement learning (DRL). Using Ningbo City as a case study, multi-source urban data, including points of interest (POI) data and urban transportation system data, are utilized, along with varying carbon emission coefficients for different travel modes, to construct a comprehensive carbon emission environment for urban areas. The proposed DRL model adopts an Actor-Critic framework, which iteratively optimizes the land use configuration and building type proportions within the urban matrix to achieve the goal of mitigating travel carbon emissions. Experimental results demonstrate that this approach exhibits significant carbon reduction effects in urban scenario. By adjusting the discount rate of the reward function, various optimization strategies can be obtained, such as short-term and long-term strategies, achieving reductions of 0.47% and 0.61%, respectively, which are notably higher than the 0.39% reduction expected if travel emissions were uniformly distributed across the matrix. The findings highlight the potential of DRL-based approaches in urban planning to achieve adaptive and data-driven strategies for carbon emission mitigation.

Towards maximizing geospatial data usage in north eastern India using open-source scalable user-centric applications

Abstract. The north eastern region of India is characterized by limited physical connectivity and challenging terrain, that requires innovative approaches for effective developmental planning and monitoring using geospatial data and user-centric applications. This paper focuses on leveraging space-based data and applications to address these challenges and enhance decision-making processes in governance. A unified framework for interoperable spatial data sharing has been developed, ensuring that data is easily discoverable, searchable, and consumable through metadata standards and OGC interoperable data services. A dedicated spatial data repository has been established, offering real-time access to over 1400 geospatial datasets and facilitating better decision-making and geo-analytics among diverse user groups. The repository operates on an open-source platform with integrated map viewers and geo-analytics tools, allowing for interactive exploration and visualization of geospatial data. Additionally, 35 user-centric geospatial applications have been operationalized, all developed using open-source geospatial tools and standards. Capacity building programs have been conducted to increase awareness and maximize the utilization of geospatial data services and applications across the region. The platform has boosted utilization of geospatial data services and applications in the region for overall planning and developmental activities for smart governance and societal benefits.

Strategic Planning for the Management of Educational Institutions: Sistematic Review

This study aimed to analyze the trends and approaches of strategic planning in the management of educational institutions based on a systematic review of the literature of the last five years. Through a comprehensive analysis of 10 studies, the main challenges and opportunities in the implementation of strategic planning in the educational field were identified. The results of the review reveal the importance of strategic planning as a tool to improve educational quality, optimize resources and respond to the demands of an increasingly complex educational environment. However, recurring challenges are identified, such as lack of staff training, resistance to change and scarcity of resources. The active participation of the entire educational community, the adaptation of strategic plans to the particularities of each context and continuous evaluation are key elements for the success of strategic planning. Furthermore, the study highlights the need to strengthen transformational leadership and take advantage of the opportunities offered by information and communication technologies. In conclusion, strategic planning emerges as a fundamental tool to improve educational quality, but its effective implementation requires a comprehensive approach that considers the particularities of each context and the active participation of all the actors involved. Future research is recommended that delve into aspects such as evaluation, educational innovation and the impact of digital transformation on strategic planning.

Factors hindering the implementation of urban planning tools in Central Africa: Case study of reference urban plan for Sarh Town (Chad)

The reference urban plan is an urban planning tool often used to orient the development of Chadian cities. However, expanding Chadian urban centers, such as Sarh, face challenges in implementing urban planning orientations of their urban plans within the set deadlines. The objective of this study is to identify the factors impeding the effective implementation of the reference urban plan for Sarh town. The methodology employed encompasses a literature review, individual interviews with urban planning experts, geographic information system (GIS) data, household surveys and statistical analysis. The results revealed that less than a quarter (19.72%) of the households surveyed were aware of the reference urban plan. The applied logistic regression model identified age, occupation and level of education as the main factors influencing public participation in the preparation of the reference urban plan. On average, 33.33% of the urban planning guidelines and 21.74% of the projected urban projects were implemented, with a difference of 1631.28 hectares (ha) between the projected plan and the actual plan for the town. Five factors were identified as contributing to the failure to implement the reference urban plan for Sarh town, including low funding, inadequate land management, a lack of political will, weak governance and poor communication. Consequently, participatory and inclusive planning approaches, effective financial mobilisation, strong governance, and the use of modern technologies such as GIS tools are recommended to enhance the implementation of urban planning tools.

Performance Evaluation of Various Path Planning Methods for Robotics and Computational Geometry

INTRODUCTION: In integrating Spiral Coverage into Cellular Decomposition, which combines structured grid-based techniques with flexible, quick spiral traversal, time efficiency is increased.OBJECTIVES: In the field of robotics and computational geometry, the study proposes a comparative exploration of two prominent path planning methodologies—Boustrophedon Cellular Decomposition and the innovative Spiral Coverage. Boustrophedon coverage has limitations in time efficiency due to its back-and-forth motion pattern, which can lead to lengthier coverage periods, especially in congested areas. Nevertheless, it is useful in some situations. It is critical to address these time-related issues to make Boustrophedon algorithms more useful in practical settings. METHODS: The research centres on achieving comprehensive cell coverage, addressing the complexities arising from confined spaces and intricate geometries. While conventional methods emphasise route optimization between points, the coverage path planning approach seeks optimal paths that maximize coverage and minimize associated costs. This study delves into the theory, practical implementation, and application of Spiral Coverage integrated with established cellular decomposition techniques.RESULTS: Through comparative analysis, it illustrates the advantages of spiral coverage over boustrophedon coverage in diverse robotics and computational applications. The research highlights Spiral Coverage's superiority in terms of path optimization, computational efficiency, and adaptability, proposing a novel perspective into cell decomposition. The methodology integrates the Spiral Coverage concept, transcending traditional techniques reliant on grids or Voronoi diagrams. Rigorous evaluation validates its potential to enhance path planning, exemplifying a substantial advancement in robotics and computational geometry.CONCLUSION: Our findings show that spiral coverage is on an average 45% more efficient than conventional Boustrophedon coverage. This paper set the basis for the future work on how different algorithms can traverse different shapes more efficiently.

Path Planning Approaches in Multi‐robot System: A Review

ABSTRACTThe essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

Perspectives of municipal professionals on adopting a dementia-friendly and inclusive approach in urban planning and design in British Columbia, Canada

ABSTRACT An accessible and navigable neighbourhood physical environment is a critical part of dementia-friendly and inclusive communities (DFCs). Municipalities in British Columbia (B.C.), Canada have committed to the vision of DFCs with action plans outlining priorities in planning, design, engineering, and services. City planners and designers play an instrumental role in implementing these plans. Our study aims to understand their needs and challenges in implementing dementia-inclusive planning and design. Semi-structured interviews were conducted with 16 planners and designers in Metro Vancouver, B.C. Findings reflect two broad themes: 1) augmenting DFC-related knowledge and awareness and 2) integrating DFC approach in general planning and design.

Towards Pragmatic Temporal Alignment in Stateful Generative AI Systems: A Configurable Approach

Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or ``state'' across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a position framework for temporal alignment with configurable toggles. We present five alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world application scenarios. This paper discusses the potential benefits and challenges of each alignment method and positions the importance of a configurable system in advancing progress in stateful generative AI systems.

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

Who is (not) in the room? An epistemic justice perspective on low-carbon transport transitions

ABSTRACT Car-centric planning exacerbates the climate crisis, compromises public health, and erodes public space. Street-space reallocation programmes, which redistribute road space from cars to active travel, leisure and urban green, are an important strategy to respond to these challenges. In line with a wider shift towards collaborative planning, many of these programmes include public participation. However, participatory planning approaches face criticism for being exclusionary, favouring a loud or privileged minority. Following an epistemic justice perspective, this paper invites to consider the question: ‘who is (not) in the room?’ Based on in-depth qualitative analysis of the participatory processes linked to a pedestrianisation scheme in Berlin, we focus on the link between non-participation in invited participatory spaces and the emergence of claimed spaces of participation. We found all participants to have participated in one of them. The decision of how to participate is a complex interplay between the relevance of the scheme and the feeling of being heard. We show that it seems irrelevant whether this interplay applies to oneself or to one’s social network. Based on our findings, we argue that epistemically just participatory planning approaches to transitions need to go beyond invited spaces to include claimed spaces both spatially and temporally.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

Coordinated planning for offshore wind and electricity–hydrogen system based on SDDP: A case study of coastal provinces in China

The coordinated development of offshore wind and electricity–hydrogen systems (OW-EHS) has the potential to enhance the utilization of renewable energy sources and achieve carbon neutrality. This article presents a case study of nine coastal provinces in China and designs four scenarios to analyze the economic benefits and potential hydrogen production capacity of OW-EHS. A multi-stage stochastic dual dynamic programming based approach is proposed for integrating OW-EHS, exploring offshore wind energy as both a primary electricity source for coastal regions and a clean, sustainable energy source for electrolyzers. It accounts for variations in hydrogen demand, wind uncertainty, load profiles, and electricity price dynamics, offering a holistic perspective on the feasibility and benefits of collaborative offshore wind and electricity–hydrogen systems. A comparative analysis highlights the effectiveness of the proposed planning approach, demonstrating its capacity to maximize the overall system benefits, encompassing both the optimal levelized cost of energy and hydrogen production. The new discoveries elucidate the unique attributes and advantages of each coastal province in OW-EHS. This research provides a tailored blueprint for coastal provinces in China to harness their offshore wind potential and accelerate progress towards carbon neutrality.

Water safety planning application for aesthetic quality of drinking water

ABSTRACT The World Health Organization considers water quality aesthetic parameters affecting taste, odor, and appearance, as factors to be monitored to determine the overall safety of water. Water safety plans (WSPs) can be used by utilities to proactively identify aesthetic hazards, rank them by likelihood of occurrence and consequence to the utility, generate risk scores, and provide direction on how to monitor, mitigate, and verify that controls are in place. The City of Wichita Falls Public Water System (CWF PWS) used the water safety planning approach to outline how aesthetics may negatively affect the system from source water, in-plant processes, and into the distribution system. By proactively identifying these hazards and outlining what to do using a WSP, the utility has put measures in place to ensure that aesthetically acceptable water is delivered to their customers. This article outlines the water safety planning process for water quality aesthetics and how the CWF PWS developed and implemented its WSP.