Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.
ABSTRACT Urban land-use change is affected by urban planning and government decision-making. Previous urban simulation methods focused only on planning constraints that prevent urban growth from developing in specific regions. However, regional planning produces planning policies that drive urban development, such as traffic planning and development zones, which have rarely been considered in previous studies. This study aims to design two mechanisms based on a cellular automata-based future land-use simulation model to integrate different planning drivers into simulations. The first update mechanism considers the influence of traffic planning, while the second mechanism can model the guiding effect of planning development zones. The proposed mechanisms are applied to the Pearl River Delta region, which is one of the fastest growing areas in China. The first mechanism is validated using simulations from 2000–2013 and demonstrates that simulation accuracy is improved by the consideration of traffic planning. In the simulation from 2013–2052, the two mechanisms are implemented and yield more realistic urban spatial patterns. The simulation outcomes can be employed to identify potential urban expansion inside the master plan. The proposed methods can serve as a useful tool that assists planners in their evaluation of urban evolvement under the impact of different planning policies.
Abstract Using a 2017 travel survey dataset and crawled heatmaps and point-of-interests (POIs) data in Beijing, China, this study adopts a gradient boosting decision trees (GBDT) algorithm to measure the relative importance and nonlinear effects of local accessibility, regional accessibility, and transit access on household car ownership. Results show that local accessibility measures such as retail and service density and job density play a more critical role in predicting auto ownership than transit access, while regional access to city centers is the least important. Thus, for reducing car ownership, planning efforts should emphasize improving local accessibility through planning pedestrian-scale neighborhoods (i.e., life-circles). Moreover, nonlinear associations between accessibility measures and car ownership are common. The results suggest that within the 15-min neighborhood life circle, there should be 65–145 retail and service facilities per km2 and block size should be within 150–200 m. Furthermore, residential neighborhoods should be within 400 m of bus stops and 1200 m of metro stations. These findings provide meaningful policy implications for planning pedestrian-scale neighborhoods recently advocated in Chinese cities.
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentiable framework for planning-oriented optimization. We further advance this paradigm through a perception-in-plan framework design, which integrates perception into the planning process. This design facilitates targeted perception guided by evolving planning objectives over time, ultimately enhancing planning performance. Building on this insight, we introduce VeteranAD, a coupled perception and planning framework for end-to-end autonomous driving. By incorporating multi-mode anchored trajectories as planning priors, the perception module is specifically designed to gather traffic elements along these trajectories, enabling comprehensive and targeted perception. Planning trajectories are then generated based on both the perception results and the planning priors. To make perception fully serve planning, we adopt an autoregressive strategy that progressively predicts future trajectories while focusing on relevant regions for targeted perception at each step. With this simple yet effective design, VeteranAD fully unleashes the potential of planning-oriented end-to-end methods, leading to more accurate and reliable driving behavior. Extensive experiments on the NAVSIM and Bench2Drive datasets demonstrate that our VeteranAD achieves state-of-the-art performance.
The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environments. In this study, we propose an enhanced trust region sequential convex optimization (TR-SCO) algorithm for optimal trajectory planning of multiple drones performing thermal screening tasks. Our improved algorithm integrates a refined convex optimization formulation within a trust region framework, effectively balancing trajectory smoothness, obstacle avoidance, altitude constraints, and maximum screening coverage. Simulation results demonstrate that our approach significantly improves trajectory optimality and computational efficiency compared to conventional convex optimization methods. This research provides critical insights and practical contributions toward deploying efficient multi-drone systems for real-time thermal screening in urban areas. For reader who are interested in our research, we release our source code at https://github.com/Cherry0302/Enhanced-TR-SCO.
The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage heaters to construct a Pareto front, considering both economic and sustainable objectives. The integration of various heat sources from both generation and consumption sides enhances flexibility in utilization. The study introduces a novel optimization algorithm, the adaptive multi-objective Bayesian optimization (AMBO). Compared to other widely used multi-objective optimization algorithms, AMBO eliminates predefined parameters that may introduce subjectivity from planners. Beyond the algorithm, the proposed model incorporates a noise term to account for inevitable simulation deviations, enabling the identification of better-performing planning results that meet the unique requirements of cold regions. What's more, the characteristics of electric-thermal coupling scenarios are captured and reflected in the operation simulation model to make sure the simulation is close to reality. Numerical simulation verifies the superiority of the proposed approach in generating a more diverse and evenly distributed Pareto front in a sample-efficient manner, providing comprehensive and objective planning choices.
Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
The implementation of scenario planning approaches provides essential tools for local governments and stakeholders to manage uncertainty and risk, ensuring that development decisions are based on relevant and accurate information. This study aims to optimize the Regional Long-Term Development Plan (RPJPD) through a scenario planning approach. This approach allows for the formulation of multiple alternative scenarios that encompass various future conditions and the evaluation of their impacts on development goals. The study employs quantitative analysis with secondary data from the RPJPD of districts/cities in East Java Province. The findings indicated that although the RPJP and RPJPD documents are flexible and can change according to conditions, they still possess significant potential to enhance local planning capacity. This condition reflects variations in performance achievement among districts/cities in East Java, with Probolinggo Regency and Probolinggo City demonstrating the best performance, while Gresik Regency and Sampang Regency ranked the lowest. Time-series analysis revealed that performance achievements on several indicators remain low, particularly regarding access to improving quality of life. This study underscores the importance of continuous evaluation and adjustment of development plans to achieve desired outcomes. It is hoped that this study will contribute significantly to improving the quality of regional development planning documents and assist local governments in achieving established development goals.
Habitat quality (HQ) is a critical factor for regional ecosystem health and sustainable development, as well as an important basis for formulating ecological protection and land-use planning. The Qin-Mang River Basin, as an integral part of the biodiversity conservation area in the Yellow River Basin, plays a significant role in maintaining the balance and stability of the regional ecosystem. This study is based on land use/land cover changes (LUCC) data from 1992, 2002, 2012, and 2022. It employs a land use transfer matrix to analyze the dynamic trends and patterns of LUCC. HQ changes are evaluated using the InVEST model, and the GeoDetector model is used to identify the key driving factors and their interactions. Additionally, spatial autocorrelation analysis is applied to explore the spatial clustering characteristics of HQ. The results indicate that between 1992 and 2022, the cumulative area of land transfer in the study area exceeded 600 km2, primarily characterized by the conversion of cultivated land to built-up areas. The HQ index decreased from 0.3409 in 1992 to 0.2896 in 2022, with a significant increase in spatial heterogeneity. Altitude, vegetation coverage, temperature, precipitation, and slope are the main driving factors influencing HQ, with natural factors dominating, but human activities gradually playing an increasingly significant role. Furthermore, HQ exhibits significant spatial clustering characteristics, with hotspot and coldspot areas providing scientific evidence for ecological protection and restoration measures. To improve HQ, it is recommended to strictly enforce ecological protection red lines, control the expansion of built-up areas, improve ecological compensation mechanisms, and promote ecological restoration measures such as returning farmland to forest and grassland.
This review examines the effect of geometric properties and the spacing of road humps on vehicle speed and noise, with a particular emphasis on South Asian contexts, especially Malaysia. Road humps are widely used traffic-calming devices designed to reduce vehicle speed and enhance road safety. The effectiveness of these measures is strongly influenced by parameters such as height, width, profile, and placement intervals. While the geometric optimization of humps generally improves speed-reduction outcomes, several studies indicate that braking and acceleration at humps can lead to increased traffic noise, particularly in residential and high-density areas. This review also explores design strategies and material choices (e.g., asphalt use, sinusoidal profiles) that may help mitigate noise impacts. Overall, a balance between speed control and noise management is necessary to ensure both safety and community acceptance.
Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang
et al.
We introduce NATURAL PLAN, a realistic planning benchmark in natural language containing 3 key tasks: Trip Planning, Meeting Planning, and Calendar Scheduling. We focus our evaluation on the planning capabilities of LLMs with full information on the task, by providing outputs from tools such as Google Flights, Google Maps, and Google Calendar as contexts to the models. This eliminates the need for a tool-use environment for evaluating LLMs on Planning. We observe that NATURAL PLAN is a challenging benchmark for state of the art models. For example, in Trip Planning, GPT-4 and Gemini 1.5 Pro could only achieve 31.1% and 34.8% solve rate respectively. We find that model performance drops drastically as the complexity of the problem increases: all models perform below 5% when there are 10 cities, highlighting a significant gap in planning in natural language for SoTA LLMs. We also conduct extensive ablation studies on NATURAL PLAN to further shed light on the (in)effectiveness of approaches such as self-correction, few-shot generalization, and in-context planning with long-contexts on improving LLM planning.
José Manuel Palacios-Gasós, Danilo Tardioli, Eduardo Montijano
et al.
In this paper we tackle the problem of persistently covering a complex non-convex environment with a team of robots. We consider scenarios where the coverage quality of the environment deteriorates with time, requiring to constantly revisit every point. As a first step, our solution finds a partition of the environment where the amount of work for each robot, weighted by the importance of each point, is equal. This is achieved using a power diagram and finding an equitable partition through a provably correct distributed control law on the power weights. Compared to other existing partitioning methods, our solution considers a continuous environment formulation with non-convex obstacles. In the second step, each robot computes a graph that gathers sweep-like paths and covers its entire partition. At each planning time, the coverage error at the graph vertices is assigned as weights of the corresponding edges. Then, our solution is capable of efficiently finding the optimal open coverage path through the graph with respect to the coverage error per distance traversed. Simulation and experimental results are presented to support our proposal.
These are notes for lectures presented at the University of Stuttgart that provide an introduction to key concepts and techniques in AI Planning. Artificial Intelligence Planning, also known as Automated Planning, emerged somewhere in 1966 from the need to give autonomy to a wheeled robot. Since then, it has evolved into a flourishing research and development discipline, often associated with scheduling. Over the decades, various approaches to planning have been developed with characteristics that make them appropriate for specific tasks and applications. Most approaches represent the world as a state within a state transition system; then the planning problem becomes that of searching a path in the state space from the current state to one which satisfies the goals of the user. The notes begin by introducing the state model and move on to exploring classical planning, the foundational form of planning, and present fundamental algorithms for solving such problems. Subsequently, we examine planning as a constraint satisfaction problem, outlining the mapping process and describing an approach to solve such problems. The most extensive section is dedicated to Hierarchical Task Network (HTN) planning, one of the most widely used and powerful planning techniques in the field. The lecture notes end with a bonus chapter on the Planning Domain Definition (PDDL) Language, the de facto standard syntax for representing non-hierarchical planning problems.
Current research underlines the important role of arrival infrastructures in urban spaces in enabling and shaping migrants’ arrival. These include arrival brokers, individuals who help newcomers access resources. As yet, we have little knowledge on brokers’ informal and commercial practices in the context of arrival, especially in European cities, whereby brokers unsettle common “distinctions between ‘state’ and ‘market,’ as well as ‘formal’ and ‘informal’” (Lindquist, 2012, p. 75). This article aims to contribute to our understanding of arrival brokers by shedding light on commercial brokering in an arrival area in Dortmund, Germany, looking at the relations between brokers and newcomer clients. The study is based on ethnographic research, including one year of participant observation in a broker’s shop, and interviews with both brokers and newcomers. Covering both perspectives, this article analyses how commercial arrival brokering shapes newcomers’ access to resources. The findings offer new insights into arrival brokers’ multiple facets of in/formal and commercial infrastructuring. The article shows how brokers’ accessibility depends on spatial, social, financial, and temporal factors. It is relational both within the local context of service provision and through setting the conditions governing resource access. Arrival brokers can influence newcomers’ arrival processes by enabling, channelling (and sometimes blocking) resource access while also offering opportunities for newcomers to circumvent and compensate for other—more formal—forms of support. Commercial brokering evolves as a practice between brokers and newcomers within, parallel to, and beyond the support provided by more formal institutions.
Constructing an ecological network is crucial to maintaining ecosystem stability, optimizing ecological space, and ensuring regional ecological security. Using circuit theory, the study integrated habitat condition, ecosystem function, and landscape structure to construct ecological network in Ulanqab. The ecological networks consist of four basic components, including sources, resistance surfaces, corridors and nodes. Ecological sources refer to patches with high habitat quality, while the resistance to species movement between patches is named as ecological resistance surfaces, the important corridors for movement between patches are referred to as ecological corridors, and the vital spots in ecological network are referred to ecological nodes. In addition, the drivers affecting these changes were also analyzed using geographical detector. The results indicated that the area of ecological sources dropped by 19.1 % and the area of high value of ecological resistance surfaces increased from 2000 and 2020. The length of ecological corridors increased by 793.80 km. Mean annual precipitation is the main natural factor influencing the ecological network, and land use intensity and human footprint are the main anthropogenic factors. Our findings suggest that in this region, urban developing should minimize the encroachment on grasslands, forests and waters. Additionally, the availability of water resources should be considered in the planning and implementation of ecological restoration and protection. The findings of the study offer reasonable suggestions for the protection and restoration of priority regions, as well as a scientific foundation optimization and sustainable development of the regional ecological network.
Morten Horsholt Kristensen, Anne Ivalu Sander Holm, Christian Rønn Hansen
et al.
Introduction: Patients with failure after primary radiotherapy (RT) for head and neck squamous cell carcinoma (HNSCC) have a poor prognosis. This study investigates pattern of failure after primary curatively intended IMRT in a randomized controlled trial in relation to HPV/p16 status. Material and methods: Patients with HNSCC of the oral cavity, oropharynx (OPSCC), hypopharynx or larynx were treated with primary curative IMRT (+/-cisplatin) and concomitant nimorazole between 2007 and 12. Of 608 patients, 151 had loco-regional failure within five years, from whom 130 pairs of scans (planning-CT and diagnostic failure scan) were collected and deformably co-registered. Point of origin-based pattern of failure analysis was conducted, including distance to CTV1 and GTV, and estimated dose coverage of the point of origin. Results: Of 130 patients with pairs of scans, 104 (80 %) had at least one local or regional failure site covered by 95 % of prescribed dose and 87 (67 %) of the failures had point of origin within the high-dose CTV (CTV1). Of failures from primary p16 + OPSCC, the majority of both mucosal (84 %) and nodal (61 %) failures were covered by curative doses. For p16− tumors (oral cavity, OPSCC p16neg, hypopharynx and larynx), 75 % of mucosal and 66 % of nodal failures were high-dose failures. Conclusion: Radioresistance is the primary cause of failure after RT for HNSCC irrespective of HPV/p16 status. Thus, focus on predictors for the response to RT is warranted to identify patients with higher risk of high-dose failure that might benefit from intensified treatment regimens.
Medical physics. Medical radiology. Nuclear medicine, Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Özge Deniz Toköz, Ali Berkay Avci, Hasan Engin Duran
The study focuses on the factors affecting visitor numbers to archaeological sites in Turkey. The aim is to investigate the geographical, economic, and demographic factors underlying the visits using statistical methods. The study covers 117 archaeological site visits in 2019. Although existing studies analysed determinants of visits to archaeological sites of different countries, the evidence needs to be explicit. Methodologically, the classical linear regression models are primarily applied in the literature, whereas the incorporation of spatial dependence has largely been ignored. This study contributes to the literature by employing demographic, economic, and climatic factors and spatial relations between the sites. Therefore, spatial autoregressive (SAR) and spatial error models (SEM) are developed in the analyses. According to the results, WHL inscription and distance to the city centre are crucial factors for the visits. In addition, the study emphasizes the significant negative effect of spatial dependence on visitor numbers of archaeological sites near each other.
ABSTRACT The paper aims at discussing the reciprocity of developing a dialogue between urban planning and degrowth by arguing for two interactive processes: ‘spatialising degrowth’ and ‘degrowing planning’. Degrowth literature has not yet fully recognised the potentiality of urban/urban regional spatial development and planning in facilitating and driving the degrowth transformation for local and regional sustainability and justice. The possibility of urban planning to facilitate a downscaling of the economy, save the environment and secure distributive justice is predicated on the causal relationships between space and societal conditions. Therefore, planning has the potentiality of providing spatial instruments in a degrowth transformation. On the other hand, the mainstream growth-oriented planning paradigm is facing internal and external imperatives for transformation. Degrowth values and principles provide inspiration for urban planning to rethink its role and function in urban and societal development, specifically on three fronts: ideology, substantive values and utopianism. The paper further discusses the dilemmas and advantages of planners, being situated in the complex political and institutional landscape, in taking proactive transformative practices.
Deterministic planning assumes that the planning evolves along a fully predictable path, and therefore it loses the practical value in most real projections. A more realistic view is that planning ought to take into consideration partial observability beforehand and aim for a more flexible and robust solution. What is more significant, it is inevitable that the quality of plan varies dramatically in the partially observable environment. In this paper we propose a probabilistic contingent Hierarchical Task Network (HTN) planner, named High-Quality Contingent Planner (HQCP), to generate high-quality plans in the partially observable environment. The formalisms in HTN planning are extended into partial observability and are evaluated regarding the cost. Next, we explore a novel heuristic for high-quality plans and develop the integrated planning algorithm. Finally, an empirical study verifies the effectiveness and efficiency of the planner both in probabilistic contingent planning and for obtaining high-quality plans.