Computers Environment and Urban Systems最新文献_第5页

Mapping priority zones for urban heat mitigation in Shanghai: Heat risk vs. shelter provision 绘制上海城市热缓解优先区域：热风险与住房供应

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-07-26 DOI: 10.1016/j.compenvurbsys.2025.102330

Wenqi Qian , Fujie Rao , Xiaoyu Li , Dayi Lai

Global climate change has intensified heat wave events, raising their intensity, duration, and frequency. Outdoor urban green spaces and indoor air-conditioned spaces serve as critical ‘heat shelters’, providing crucial cooling relief to extreme heat. However, there is a lack of studies focused on the spatial distribution of potential heat shelters and how shelters in different urban areas match varying degrees of heat risk. To address this research gap, we quantify and map heat risks and shelter provisions of administrative neighborhoods (often the smallest level of urban governance) and walkable grids of 500 × 500 m (a commonly-used comfortable walking distance for vulnerable groups such as the elderly people), and identify vulnerable areas where heat mitigation interventions should be prioritized. We select Shanghai – a metropolis of around 25 million people experiencing increasingly extreme heat wave events - for the case study. We measure heat risk by a composite index incorporating heat hazard, exposure and vulnerability. We largely measure heat provision by the number of indoor air-conditioned venues and outdoor green spaces, weighted by their time availability. Our findings reveal a general decrease in heat mitigation priority levels from the urban core to the suburbs, a pattern that is consistent between neighborhoods and grids at the metropolitan scale. This said, at smaller scales, significant differences between these two types of spatial units emerged in the degree and distribution of heat mitigation priority levels, revealing nuanced, inequitable capacities of different urban areas to tackle extreme heat. Our study provides a novel and systematic lens for assessing heat mitigation priority levels, informing more effective strategies for planning and managing heat shelters and allocating heat mitigation resources.

全球气候变化加剧了热浪事件，增加了它们的强度、持续时间和频率。室外城市绿地和室内空调空间作为关键的“热庇护所”，为极端高温提供关键的冷却缓解。然而，缺乏对潜在热避难所的空间分布以及不同城市地区的避难所如何匹配不同程度的热风险的研究。为了解决这一研究差距，我们量化并绘制了行政街区（通常是城市治理的最小层面）和500 × 500米（老年人等弱势群体常用的舒适步行距离）的步行网格的热风险和住所供应图，并确定了应优先采取热缓解干预措施的脆弱区域。我们选择了拥有2500万人口的大都市上海作为案例研究对象，上海正在经历越来越多的极端热浪事件。我们通过结合热危害、暴露和脆弱性的综合指数来衡量热风险。我们主要通过室内空调场地和室外绿地的数量来衡量热量供应，并根据它们的可用性进行加权。我们的研究结果揭示了从城市核心到郊区的热缓解优先级普遍下降，这种模式在大都市尺度上在社区和网格之间是一致的。也就是说，在较小的尺度上，这两种类型的空间单元在热缓解优先级的程度和分布上出现了显著差异，揭示了不同城市地区应对极端高温的细微差别和不公平能力。我们的研究为评估热减排优先级提供了一种新颖而系统的视角，为规划和管理热庇护所以及分配热减排资源提供了更有效的策略。

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引用次数: 0

An agent-based model for estimating daily face-to-face contact networks in large urban systems 基于智能体的大型城市系统日常面对面接触网络估计模型

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-07-17 DOI: 10.1016/j.compenvurbsys.2025.102321

Ismaïl Saadi , Etienne Côme , Liem Binh Luong Nguyen , Mahdi Zargayouna

Detailed contact data is important to model disease transmission in dense urban areas, as human mobility and social interactions significantly impact spread. However, linking mobility, activities, and social contacts in large cities is challenging. Current models often rely on contact surveys and overlook travel behaviors. Here we present a novel modeling framework for estimating large-scale, multi-setting contact networks by leveraging high-resolution synthetic activity-travel data. Our approach introduces a new mathematical formalism to construct multi-setting contact networks from spatiotemporal co-location patterns, enabling constraints based on key statistics (e.g., contact rates per setting, proportions of each contact type), incorporation of location types, and individual activity purposes. Efficient algorithms extract co-presence events, generating validated, individual-based contact networks, from which age-specific contact matrices are derived. The approach is tested using EQASIM, an open and reproducible activity-based travel demand model that relies on publicly available data for France’s Île-de-France region. We also evaluated the spatial effects of work-from-home policies on contact patterns by modifying individuals’ activity-travel diaries. Results show that multi-setting contact networks — representing 12 million individuals distributed across 1,714,920 unique locations — can be generated in minutes while accurately reproducing setting- and age-specific spatial contact patterns.

详细的接触数据对于在人口密集的城市地区建立疾病传播模型非常重要，因为人类的流动性和社会互动会对传播产生重大影响。然而，将大城市的流动性、活动和社会联系联系起来是一项挑战。目前的模型往往依赖于接触调查，而忽略了旅行行为。在这里，我们提出了一个新的建模框架，通过利用高分辨率的综合活动-旅行数据来估计大规模的、多设置的接触网络。我们的方法引入了一种新的数学形式，从时空共定位模式构建多设置接触网络，实现基于关键统计数据（例如，每个设置的接触率，每种接触类型的比例）的约束，结合位置类型和个人活动目的。有效的算法提取共同存在事件，生成经过验证的、基于个体的接触网络，并从中派生出特定年龄的接触矩阵。该方法使用EQASIM进行了测试，EQASIM是一个开放的、可重复的基于活动的旅行需求模型，它依赖于法国Île-de-France地区的公开数据。我们还通过修改个人的活动-旅行日记来评估在家工作政策对接触模式的空间效应。结果表明，在几分钟内就可以生成分布在1,714,920个不同地点的1200万人的多环境接触网络，同时准确地再现了特定环境和年龄的空间接触模式。

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引用次数: 0

Modeling spatial and temporal urban environmental noise using street view imagery and machine learning 利用街景图像和机器学习建模时空城市环境噪声

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-07-11 DOI: 10.1016/j.compenvurbsys.2025.102327

Devin Yongzhao Wu , Jue Wang

This study proposes a framework for modeling environmental noise pollution by integrating land use regression (LUR) with machine learning models and street built environments. Using noise data collected from 128 locations over nine consecutive days in Mississauga, Ontario, Canada, the research demonstrates that incorporating finer-scale street built environment features derived from street view images significantly improves noise prediction accuracy. The model using XGBoost and street view-derived variables significantly outperforms traditional LUR-based models. The results indicate that street-level characteristics, particularly terrain, play a critical role in modeling noise levels, complementing traditional land use and NDVI-based greenness. Furthermore, the research highlights the importance of using non-linear models like XGBoost to capture complex relationships between noise and urban features. This approach offers valuable insights for advancing environmental noise modeling, which further supports future public health studies investigating the impact of noise exposure on population health.

本研究提出了一个将土地利用回归（LUR）与机器学习模型和街道建筑环境相结合的环境噪声污染建模框架。通过对加拿大安大略省密西沙加市128个地点连续9天收集的噪声数据进行分析，研究表明，结合来自街景图像的更精细尺度的街道建筑环境特征，可以显著提高噪声预测的准确性。使用XGBoost和街景衍生变量的模型明显优于传统的基于lur的模型。结果表明，街道水平特征，特别是地形，在模拟噪声水平方面起着关键作用，补充了传统的土地利用和基于ndvi的绿化。此外，该研究强调了使用像XGBoost这样的非线性模型来捕捉噪音和城市特征之间复杂关系的重要性。该方法为推进环境噪声建模提供了有价值的见解，进一步支持未来调查噪声暴露对人口健康影响的公共卫生研究。

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引用次数: 0

Housing segregation in Chinese major cities: A K-nearest neighbor analysis of longitudinal big data 中国主要城市的住房隔离：纵向大数据的k近邻分析

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-07-01 DOI: 10.1016/j.compenvurbsys.2025.102326

Sebastian Kohl , Bo Li , Can Cui

Most studies on residential segregation in China have primarily relied on decennial population census data, which lacks the granularity and timeliness needed to capture segregation dynamics with higher frequency. Drawing on georeferenced housing market transaction data between 2012 and 2023 in Shanghai and Beijing, and employing fine-grained spatial segregation analysis techniques, including k-nearest neighbor approaches (k−NN) and modifiable grids, we find that housing segregation by price and size increased between 2012 and 2018, followed by a decline thereafter, particularly in the larger-sized and higher-priced market segments. While segregation levels are generally comparable between the two cities, Shanghai exhibits higher segregation for the top 20 % of apartments, while Beijing shows greater segregation for the bottom 20 %. Segregation is highest for prices, followed by rents, with housing size showing the lowest segregation. Expanding the analysis to 11 major Chinese cities, we suggest that high and rising housing prices are associated with increasing segregation, particularly in cities with lower initial segregation. Methodologically, this paper demonstrates that leveraging big transaction and listing data, alongside utilizing fine-grained spatial analysis, can advance our understanding of urban inequalities.

大多数关于中国居住隔离的研究主要依赖于十年一次的人口普查数据，缺乏更高频率捕捉隔离动态所需的粒度和及时性。利用2012年至2023年上海和北京的地理参考住房市场交易数据，并采用包括k-近邻方法（k - NN）和可修改网格在内的细粒度空间隔离分析技术，我们发现，2012年至2018年期间，价格和规模的住房隔离有所增加，之后有所下降，特别是在规模较大和价格较高的细分市场。虽然两个城市的隔离程度大致相当，但上海的前20%的公寓隔离程度更高，而北京的后20%的公寓隔离程度更高。房价的隔离程度最高，其次是租金，住房面积的隔离程度最低。将分析扩展到中国11个主要城市，我们认为高房价和不断上涨的房价与日益加剧的隔离有关，特别是在最初隔离程度较低的城市。在方法上，本文表明，利用大交易和上市数据，以及利用细粒度空间分析，可以促进我们对城市不平等的理解。

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引用次数: 0

Social areas revisited through the lens of mobility: A comparative study of the traditional and mobility approaches 通过流动性视角重新审视社会领域：传统方法与流动性方法的比较研究

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-29 DOI: 10.1016/j.compenvurbsys.2025.102325

Run Shi , Anthony Gar-On Yeh , Fang Bian

Social area analysis is a framework for understanding residential social structure as a product of urbanization and economic development. Building on our previous findings that socioeconomically similar residents exhibit different mobility patterns, this study examines urban socio-spatial structure by incorporating commuting patterns from mobile phone data with census in Shenzhen, China. We conduct a comparative analysis to explore differences between the traditional and mobility approaches. Principal Component Analysis (PCA) results reveal that mobility is an essential dimension of socio-spatial differentiation at the aggregated neighborhood committee level. The derived residential social structure explicitly highlights mobility disparities, providing evidence for possible segregation and potential improvements in urban planning. By analyzing the interplays of economic, political, and social forces, we conceptualize mobility as a sub-dimension of social space. The contribution of this study lies in two folds. First, we propose a framework for integrating mobile phone data with census data to capture mobility disparities at the aggregated level with the concept of activity space. Second, we explore the role of mobility in delineating urban socio-spatial structure, providing a novel perspective for examining the internal spatial structure of cities.

社会区域分析是将住宅社会结构作为城市化和经济发展的产物来理解的一个框架。基于我们之前的研究结果，社会经济相似的居民表现出不同的流动模式，本研究通过结合移动电话数据和中国深圳人口普查的通勤模式来研究城市社会空间结构。我们进行了比较分析，以探讨传统方法和流动方法之间的差异。主成分分析（PCA）结果表明，流动性是综合居委会社会空间分异的重要维度。由此衍生的住宅社会结构明确强调了流动性差异，为可能的隔离和城市规划的潜在改进提供了证据。通过分析经济、政治和社会力量的相互作用，我们将流动性概念化为社会空间的一个子维度。本研究的贡献在于两个方面。首先，我们提出了一个整合移动电话数据和人口普查数据的框架，通过活动空间的概念在总体水平上捕捉移动差异。其次，我们探讨了流动性在描述城市社会空间结构中的作用，为研究城市内部空间结构提供了一个新的视角。

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引用次数: 0

Enhancing transparency in land use change modeling: Leveraging eXplainable AI techniques for urban growth prediction with spatially distributed insights 提高土地利用变化建模的透明度：利用可解释的人工智能技术进行具有空间分布洞察力的城市增长预测

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-28 DOI: 10.1016/j.compenvurbsys.2025.102322

Zelin Wang , Tianshu Feng , Abolfazl Safikhani , Emre Tepe

Recent applications of machine learning (ML) and deep learning (DL) techniques in land-use change modeling have demonstrated significant success in capturing the intricate dynamics of land development. However, their “black-box” nature restricts their utility in various contexts, such as uncovering the underlying drivers of urban expansion. To mitigate this issue, we propose to utilize eXplainable AI (XAI) techniques in ML/DL methods, which presents a promising solution to this primary constraint. To that end, we introduce DL methods to investigate and predict the non-linear dynamics of land use changes. These methods achieved notably high accuracy scores and were more computationally viable than traditional statistical approaches. Moreover, the proposed approach employed in this study surpassed the parameter interpretation capabilities of statistical methods. More specifically, the proposed XAI approach not only highlights the average effects of features on the outcome but also elucidates the factors influencing specific decisions regarding land use changes, including the number of vacant parcels, the share of single-family parcels, and certain time-lagged neighborhood features. Such analyses provide invaluable insights for researchers, practitioners, and policymakers.

最近机器学习（ML）和深度学习（DL）技术在土地利用变化建模中的应用在捕捉土地发展的复杂动态方面取得了重大成功。然而，它们的“黑盒子”性质限制了它们在各种情况下的效用，例如揭示城市扩张的潜在驱动因素。为了缓解这个问题，我们建议在ML/DL方法中使用可解释的AI （XAI）技术，这为这个主要约束提供了一个有希望的解决方案。为此，我们引入DL方法来研究和预测土地利用变化的非线性动态。这些方法取得了显著的高准确性分数，并且比传统的统计方法更具计算可行性。此外，本研究所采用的方法超越了统计方法的参数解释能力。更具体地说，提出的XAI方法不仅强调了特征对结果的平均影响，而且阐明了影响土地利用变化的具体决策的因素，包括空置地块的数量、单户地块的份额和某些时间滞后的社区特征。这些分析为研究人员、从业者和政策制定者提供了宝贵的见解。

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引用次数: 0

Mapping multidimensional energy deprivation: Socio-spatial inequalities and policy implications in Great Britain 映射多维能源剥夺：社会空间不平等和政策影响在英国

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-28 DOI: 10.1016/j.compenvurbsys.2025.102324

Meixu Chen , Caitlin Robinson , Alex Singleton

This work provides a thorough Energy Deprivation Segmentation (EDS) for Great Britain, which aims to address the complex and varied aspects of energy poverty in different small regions. By proposing a reproducible analytical framework, we combine many data sources to provide a comprehensive segmentation that encompasses various dimensions such as energy efficiency, accessibility, demand and supply, housing conditions, and financial vulnerability. The results indicate notable disparities in energy deprivation based on social and spatial factors. We observed higher degrees of deprivation in the peripheral areas of major cities and suburbs in the northern regions of England, southern regions of Wales, and central regions of Scotland. The created EDS identifies six top-level Supergroups and 14 finer Groups and was validated internally and externally to confirm its robustness and applicability. This segmentation offers a more comprehensive insights into the characteristics and distribution of energy-deprived neighbourhoods than traditional measures. This research facilitates policymakers to design targeted strategies and resource allocation to combat specific vulnerabilities within communities and foster sustainable and equitable urban growth. Additionally, a practical tool is provided for monitoring and evaluating the effectiveness of policies aimed at reducing energy poverty.

这项工作为英国提供了一个彻底的能源剥夺分割（EDS），旨在解决不同小地区能源贫困的复杂和不同方面。通过提出一个可重复的分析框架，我们结合了许多数据源，提供了一个全面的细分，包括能源效率、可及性、需求和供应、住房条件和金融脆弱性等各个方面。结果表明，基于社会和空间因素的能量剥夺存在显著差异。我们观察到，在英格兰北部地区、威尔士南部地区和苏格兰中部地区的主要城市和郊区的外围地区，贫困程度更高。创建的EDS识别了6个顶级超组和14个精细组，并进行了内部和外部验证，以确认其稳健性和适用性。与传统方法相比，这种细分方法可以更全面地了解能源匮乏社区的特征和分布。这项研究有助于决策者设计有针对性的战略和资源分配，以应对社区内的特定脆弱性，促进可持续和公平的城市增长。此外，还提供了一个实用的工具来监测和评价旨在减少能源贫穷的政策的效力。

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引用次数: 0

Renewable energy adoption in urban residential communities in China: An agent-based model for assessing intervention impact 中国城市居民社区采用可再生能源：基于主体的干预影响评估模型

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-19 DOI: 10.1016/j.compenvurbsys.2025.102323

Hua Du , Qi Han , Bauke de Vries , Jun Sun

Designing effective policy interventions is an essential instrument to promote the widespread adoption of photovoltaic (PV) systems in the residential sector. Designing such policies and evaluating their effectiveness requires an approach that allows for simulation in the complex system setting of the built environment. In this study we applied Agent-Based Modelling to evaluate the effectiveness of two policies (i.e., information campaign and demonstration projects) and two community factors (i.e., community size and required agreement rate) to promote the adoption of residential community PV diffusion in Chinese cities. This model is developed based on the empirical results of a previous discrete choice experiment. The results show that lowering the required agreement rate for community decisions contributes to an increase in PV adoption, while community size has little impact on adoption diffusion. We found that combining the two policy interventions or combining them with a community factor (i.e., lowering the required agreement rate) can effectively promote the adoption of community PV. Policy intervention implications and suggestions are presented.

设计有效的政策干预措施是促进光伏系统在住宅部门广泛采用的重要手段。设计这样的政策和评估其有效性需要一种在建筑环境的复杂系统设置中进行模拟的方法。在本研究中，我们运用基于agent的模型来评估两项政策（即信息宣传活动和示范项目）和两个社区因素（即社区规模和所需的协议率）对促进中国城市采用住宅社区光伏扩散的有效性。这个模型是基于先前的离散选择实验的经验结果发展起来的。结果表明，降低社区决策的共识率有助于提高光伏采用率，而社区规模对采用扩散的影响不大。我们发现，将两种政策干预相结合或将其与社区因素相结合（即降低所需的同意率）可以有效促进社区光伏的采用。提出了政策干预的影响和建议。

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引用次数: 0

Measuring nuanced walkability: Leveraging ChatGPT's vision reasoning with multisource spatial data 测量细微差别的步行性：利用ChatGPT的视觉推理与多源空间数据

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-18 DOI: 10.1016/j.compenvurbsys.2025.102319

Donghwan Ki , Hojun Lee , Keundeok Park , Jaehyun Ha , Sugie Lee

Recent advances in urban analytical tools, particularly street view image (SVI) data and computer vision (CV) algorithms, such as semantic segmentation, have enhanced walkability measurement by enabling the automated assessment of mesoscale features, such as greenery proportions. However, while SVI data contain rich environmental information, off-the-shelf CV models generally struggle to capture microscale features—design details attached to mesoscale elements, such as the quality of greenery or sidewalk maintenance. Moreover, because CV algorithms typically evaluate environmental features in isolation, they often fail to account for spatial arrangements and visual harmony among features, limiting their ability to support a holistic assessment of walkability. Recently, multimodal large language models (MLLMs), particularly ChatGPT, have introduced a transformative approach to image analysis by mimicking human perception. This study proposes a comprehensive walkability measurement framework that leverages ChatGPT's vision reasoning across multiple spatial data, including SVIs and GIS land use and road network maps. To validate this framework, we compare ChatGPT-generated walkability ratings with human assessments and examine their relationship with reported walking behavior data. Furthermore, by comparing ChatGPT-generated outcomes with evaluations from conventional walkability measurement tools, such as GIS and off-the-shelf CV models, we highlight the novel contribution of ChatGPT in walkability assessment. This research advances the literature by introducing a ChatGPT-based framework for a more comprehensive walkability assessment.

城市分析工具的最新进展，特别是街景图像（SVI）数据和计算机视觉（CV）算法，如语义分割，通过自动评估中尺度特征（如绿化比例），增强了步行性测量。然而，虽然SVI数据包含丰富的环境信息，但现成的CV模型通常难以捕捉到与中尺度元素（如绿化质量或人行道维护）相关的微尺度特征-设计细节。此外，由于CV算法通常孤立地评估环境特征，它们往往无法考虑空间安排和特征之间的视觉和谐，从而限制了它们支持可步行性整体评估的能力。最近，多模态大型语言模型（mllm），特别是ChatGPT，通过模仿人类感知引入了一种变革性的图像分析方法。本研究提出了一个综合的步行性测量框架，该框架利用ChatGPT在多个空间数据中的视觉推理，包括svi和GIS土地利用和道路网络地图。为了验证这一框架，我们将chatgpt生成的步行性评级与人类评估进行了比较，并检查了它们与报告的步行行为数据的关系。此外，通过将ChatGPT生成的结果与传统的步行性测量工具（如GIS和现成的CV模型）的评估结果进行比较，我们强调了ChatGPT在步行性评估中的新贡献。本研究通过引入基于chatgpt的框架来进行更全面的步行性评估，从而推动了文献的发展。

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引用次数: 0

Architecting urban epidemic defense: A hierarchical region-individual control framework for optimizing large-scale individual mobility interventions 构建城市流行病防御：优化大规模个人流动干预的分层区域-个人控制框架

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-14 DOI: 10.1016/j.compenvurbsys.2025.102312

Yuxiao Luo , Ling Yin , Kemin Zhu , Kang Liu

In urban areas, high population density and extensive mobility can foster rapid transmission of emerging infectious diseases, particularly acute respiratory infections, which could lead to significant public health challenges and widespread social impact. EPidemic Control (EPC) strategies like mobility interventions tailored for each individual effectively mitigate these risks, balancing the safeguarding of public health with the socio-economic impacts. However, a large number of urban residents (e.g., millions) with complex spatiotemporal activities in modern cities pose a large-scale challenge of optimizing mobility interventions at an individual-level. To address this issue, this study introduces a framework of Hierarchical Region-Individual Control for Epidemic (Hi-RICE) to dynamically adapt specific interventions to large-scale individuals in complex urban epidemic scenarios with given control objectives. Hi-RICE initially assesses the dynamic infectious risk and contact risk for each individual according to their spatiotemporal behaviors. Subsequently, regional control agents, utilizing multi-agent reinforcement learning, optimize the appropriate intervention intensity for each region. Finally, specific mobility interventions are applied to high-risk individuals in each region according to their optimized control intensities. Utilizing Shenzhen, China, as a case of a megacity, simulations validate the proposed framework’s effectiveness and adaptability across various epidemic conditions, demonstrating its capacity to optimally balance epidemic control and socio-economic sustainability.

在城市地区，高人口密度和广泛的流动性可能促进新出现的传染病，特别是急性呼吸道感染的迅速传播，这可能导致重大的公共卫生挑战和广泛的社会影响。流行病控制（EPC）战略，如为每个人量身定制的流动干预措施，有效地减轻了这些风险，平衡了公共卫生的保障与社会经济影响。然而，在现代城市中，大量城市居民（如数百万人）具有复杂的时空活动，这对优化个人层面的流动性干预措施构成了大规模挑战。为了解决这一问题，本研究引入了分层区域-个体流行病控制（Hi-RICE）框架，在给定控制目标的情况下，对复杂的城市流行病场景中的大规模个体动态调整特定的干预措施。Hi-RICE首先根据个体的时空行为对其动态感染风险和接触风险进行评估。随后，区域控制代理利用多智能体强化学习优化每个区域的适当干预强度。最后，根据各区域的最优控制强度，对高危人群实施针对性的流动性干预。以中国深圳为例，模拟验证了所提出的框架在各种疫情条件下的有效性和适应性，展示了其在疫情控制和社会经济可持续性之间的最佳平衡能力。

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引用次数: 0