Computers Environment and Urban Systems最新文献_第3页

Decoding human safety perception with eye-tracking systems, street view images, and explainable AI 通过眼动追踪系统、街景图像和可解释的人工智能解码人类的安全感知

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-01 DOI: 10.1016/j.compenvurbsys.2025.102356

Yuhao Kang , Junda Chen , Liu Liu , Kshitij Sharma , Martina Mazzarello , Simone Mora , Fábio Duarte , Carlo Ratti

The way residents perceive safety plays an important role in how they use public spaces, and it informs city planning and public policy. Recent studies have combined street view images and advanced computer vision techniques to measure human safety perceptions of urban environments. Despite their success, such studies have often overlooked the specific environmental visual factors that draw human attention and trigger people’s feelings of safety perceptions. In this study, we introduce a computational framework that enriches the existing body of literature on place perception by using eye-tracking systems with street view images and explainable AI approaches. Eye-tracking systems measure what users are looking at and how long they engage with specific environmental elements. This allows us to explore the nuance of which visual environmental factors influence human safety perceptions. We conducted our research and recruited volunteers in Helsingborg, Sweden. By examining participants’ focus on specific features using Mean Object Ratio in Highlighted Regions (MoRH) and Mean Object Hue (MoH), we identified key visual elements that attract human attention when perceiving safe environments. For instance, certain urban infrastructure (e.g., stairways and signboards) and public space (flags and chairs) features draw more human attention while the sky is less relevant in influencing safety perceptions. These insights offer a more human-centered understanding of which urban features influence human safety perceptions. Furthermore, we compared the real human attention from eye-tracking systems with attention maps obtained from eXplainable Artificial Intelligence (XAI) results. Several XAI models were tested, and we observed that XGradCAM and EigenCAM most closely align with human safety perceptual patterns. Our framework provides a valuable approach to enhance the interpretability and trustworthiness of XAI models by comparing them with empirically observed human behavior data. This study demonstrates the limitations of previous place perception studies that solely rely on street view images and computer vision techniques, which may not comprehensively capture the nuanced human experiences and behaviors at place. The inclusion of technologies such as eye-tracking not only deepens our comprehension of human subjective experiences, but also contributes to the development of safer environments and communities.

居民感知安全的方式在他们如何使用公共空间方面发挥着重要作用，并为城市规划和公共政策提供信息。最近的研究结合了街景图像和先进的计算机视觉技术来测量人类对城市环境的安全感知。尽管这些研究取得了成功，但往往忽略了引起人们注意并引发人们安全感的特定环境视觉因素。在本研究中，我们引入了一个计算框架，通过使用带有街景图像的眼动追踪系统和可解释的人工智能方法，丰富了现有的关于地点感知的文献。眼球追踪系统测量用户在看什么，以及他们与特定环境元素互动的时间。这使我们能够探索视觉环境因素影响人类安全感知的细微差别。我们在瑞典赫尔辛堡进行了研究并招募了志愿者。通过使用高亮区域平均物体比（MoRH）和平均物体色相（MoH）检查参与者对特定特征的关注，我们确定了在感知安全环境时吸引人类注意力的关键视觉元素。例如，某些城市基础设施（如楼梯和招牌）和公共空间（旗帜和椅子）的特征更吸引人们的注意，而天空在影响安全观念方面的相关性较低。这些见解提供了一种更以人为中心的理解，即城市特征会影响人类的安全感知。此外，我们将眼动追踪系统获得的真实人类注意力与可解释人工智能（eXplainable Artificial Intelligence， XAI）结果获得的注意力图进行了比较。几个XAI模型进行了测试，我们观察到XGradCAM和EigenCAM最接近人类安全感知模式。通过将XAI模型与经验观察到的人类行为数据进行比较，我们的框架提供了一种有价值的方法来增强XAI模型的可解释性和可信度。这项研究表明，以往的地点感知研究仅仅依赖于街景图像和计算机视觉技术，可能无法全面捕捉细微的人类体验和行为。眼球追踪等技术的加入不仅加深了我们对人类主观体验的理解，而且有助于更安全的环境和社区的发展。

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引用次数: 0

Adaptive continuity-preserving simplification of street networks 自适应保持连续性的街道网络简化

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-10-31 DOI: 10.1016/j.compenvurbsys.2025.102354

Martin Fleischmann , Anastassia Vybornova , James D. Gaboardi , Anna Brázdová , Daniela Dančejová

Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification — the process of converting a highly granular input network into its most simple morphological form — is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm — available in the neatnet Python package — outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.

街道网络数据被广泛用于研究人类活动和城市结构。通常，这些数据是面向交通应用程序的，这些应用程序需要高度细化的、有向的图形来捕捉潜在交通模式的复杂关系。虽然这种级别的网络细节对于某些细粒度的移动模型至关重要，但它代表了与街道网络形态有关的研究的障碍。对于后一种情况，街道网络简化——将高度细化的输入网络转换为最简单的形态形式的过程——是必要的，但非常繁琐的预处理步骤，特别是在手动进行时。在本文中，我们开发并提出了一种新的自适应算法，用于简化街道网络，该算法既完全自动化，又能够模拟通过手动简化程序获得的结果。该算法（在netnet Python包中可用）在将这些方法与人工简化的数据进行比较时，优于当前最先进的程序，同时保持了网络的连续性。

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引用次数: 0

Exploring the inequality of the particulate matter exposure level of public bicycle scheme users using an agent-based model 基于agent模型的公共自行车使用者颗粒物暴露水平的不均衡性研究

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-10-29 DOI: 10.1016/j.compenvurbsys.2025.102367

Suyeon Hwang , Heesoo Rhim , Jeon-Young Kang , Jinmu Choi

Air pollution negatively affects human health and understanding its relationship with mobility is crucial for assessing these effects. Although previous studies have relied on individual GPS trajectories, employing data gathered at the individual level frequently results in ecological fallacies. This study constructed a spatially explicit agent-based model (ABM) to illustrate the uneven interactions between the mobility patterns of bicycle users and their exposure to particulate matter (PM). We found that bicycle users who visited specific rental stations were exposed to higher PM levels. We conducted scenario analyses that included the closure of rental stations with higher average PM exposure levels or the introduction of detour routes to evaluate the influence of rental station utilization on exposure levels. When rental stations were completely closed and users were directed to detour to nearby accessible stations for both departure and arrival, they were observed to explore new mobility behaviors. During this process, users increased their exposure levels by selecting routes characterized by high PM concentrations and longer travel times. Conversely, when implementing a temporary closure that restricted only departures, the measured exposure levels showed a significant reduction compared with both the baseline and complete closure scenarios. The scenario analyses verified that individuals' exposure to PM was significantly affected by mobility patterns. The spatially explicit ABM can aid in identifying inequality in individuals' PM exposure levels by considering their mobility and can provide insights for improving urban transportation system management.

空气污染对人类健康有负面影响，了解其与流动性的关系对于评估这些影响至关重要。虽然以前的研究依赖于个人GPS轨迹，但采用个人层面收集的数据经常导致生态谬误。本研究构建了一个空间显性主体模型（ABM）来说明自行车使用者的出行模式与其暴露于颗粒物（PM）之间的不均匀相互作用。我们发现，去特定租赁站骑自行车的人暴露在更高的PM水平。我们进行了情景分析，包括关闭PM平均暴露水平较高的出租站或引入绕行路线，以评估出租站使用对暴露水平的影响。当租赁站完全关闭，用户被引导绕道到附近的无障碍站点出发和到达时，观察到他们探索新的移动行为。在这一过程中，用户通过选择高PM浓度和较长旅行时间的路线来增加他们的暴露水平。相反，当实施临时关闭，只限制离开时，与基线和完全关闭情景相比，测量的暴露水平都显着降低。情景分析证实，个体对PM的暴露受到流动模式的显著影响。空间显性ABM可以通过考虑其流动性来帮助识别个体PM暴露水平的不平等，并可以为改善城市交通系统管理提供见解。

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引用次数: 0

A critical assessment of selected urban resilience decision-support tools in the United States 对美国选定的城市弹性决策支持工具的关键评估

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-10-22 DOI: 10.1016/j.compenvurbsys.2025.102352

Sarbeswar Praharaj

The concept of resilience has gained increasing prominence in research and policy discussions. Various web-based data visualization tools have been developed to communicate hazard risks and make complex climate models more accessible to the community. This article presents a comprehensive assessment of 30 selected resilience tools across the U.S. to examine their decision-support capabilities. We conducted an extensive search to shortlist tools from diverse sectors, including public, private, non-profit, and academic organizations, addressing different hazard contexts, such as floods, heatwaves, wildfires, storms, infrastructure failures, and land degradation. We designed and applied a broad-based analytical framework to assess the tools' performance, encompassing the comprehensiveness of indicator systems, robustness of design and visual communication, scenario planning to handle uncertainties, participatory and interactive approaches, and decision-support and action-enabling characteristics. Several notable findings emerge from this study: (a) resilience tools overwhelmingly include environmental datasets while overlooking social and institutional dimensions, necessary to capture the complexity of urban systems; (b) minimal integration of city and neighborhood level information poses barriers to localized assessments and adaptation planning; (c) while the tools effectively communicate vulnerability, future innovations in scenario-based planning utilizing AI, real-time data, and predictive modeling capabilities are needed to plan for a dynamic and uncertain future; and (d) these tools must foster an interoperable design for users to combine new data layers to adjust their resilience assessments to local contexts. This study outlines future research and development avenues, providing resilience planners and decision-makers with a strategic foresight to enhance computer-based applications for assessing, planning, and managing disaster risks to human and urban systems.

弹性的概念在研究和政策讨论中越来越突出。已经开发了各种基于网络的数据可视化工具，以传达灾害风险，并使社区更容易获得复杂的气候模型。本文对美国30个选定的弹性工具进行了全面评估，以检查其决策支持能力。我们进行了广泛的搜索，从不同部门（包括公共、私人、非营利和学术组织）获得了候选工具，以应对不同的灾害背景，如洪水、热浪、野火、风暴、基础设施故障和土地退化。我们设计并应用了一个基础广泛的分析框架来评估工具的性能，包括指标体系的全面性、设计和视觉传达的稳健性、处理不确定性的情景规划、参与性和互动性方法、决策支持和行动支持特征。本研究得出了几个值得注意的发现：(a)复原力工具绝大多数包括环境数据集，而忽略了社会和制度层面，这是捕捉城市系统复杂性所必需的；(b)城市和社区一级信息的最低限度整合对本地化评估和适应性规划构成障碍；(c)虽然这些工具有效地传达了脆弱性，但未来需要利用人工智能、实时数据和预测建模能力进行基于场景的规划创新，以规划动态和不确定的未来；(d)这些工具必须培养一种可互操作的设计，以便用户结合新的数据层，根据当地情况调整其弹性评估。本研究概述了未来的研究和发展途径，为弹性规划者和决策者提供战略远见，以加强基于计算机的应用，以评估、规划和管理人类和城市系统面临的灾害风险。

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引用次数: 0

A hierarchical cross-departmental agent-based approach to explore the impacts of policy interplay on land use dynamics 以分层跨部门代理为基础的方法探讨政策相互作用对土地使用动态的影响

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-10-14 DOI: 10.1016/j.compenvurbsys.2025.102355

Jing Gao , Jian Gong , Nina Schwarz , Richard Sliuzas

Understanding the interactions between competing land policies is crucial for identifying governance challenges and assisting urban planners and policy analysts in making informed decisions. However, a methodology for incorporating land use patterns and the policy implementation processes within the framework of hierarchical land management remains underexplored. Here, we employ an agent-based model (ABM) to investigate how land use change occurs as policies intersect across different hierarchical levels and branches of government in Wuhan, China. Changes in land use arise from the interplay between five agents—the central level, the local level that incorporates three departments, and the village collective level—in the decisions on land acquisition, conversion, and reclamation. Four parameters characterize the enforcement levels of relevant policies, and multi-objective optimization with genetic algorithms was applied to calibrate them. The results show that: (1) Our ABM exhibits a figure of merit value of 0.3 at the city level and 0.58 in the larger urban area, indicating its capability to simulate real land use dynamics. (2) Policy implementation gaps led to high land conversion and low farmland reclamation. (3) The dynamic enforcement scenarios provide a viable pathway for negotiated governance, enabling demand-responsive rate attenuation and conflict mitigation, which is distinct from the exacerbated land use conflicts observed under the other scenarios. (4) Policy should incorporate adaptive mechanisms to maintain a buffer between competing land demands rather than binary constraints. This ABM introduces a novel hierarchical framework to decode policy interplay and implementation tensions, advancing sustainable land governance and urban planning insights.

了解相互竞争的土地政策之间的相互作用对于确定治理挑战和协助城市规划者和政策分析师做出明智决策至关重要。但是，在分级土地管理框架内结合土地使用模式和政策执行过程的方法仍未得到充分探讨。在此，我们采用基于主体的模型（ABM）来研究中国武汉不同层级和政府部门之间政策交叉时土地利用变化的发生情况。土地利用的变化源于五个主体——中央层面、由三个部门组成的地方层面和村集体层面——在土地征用、转换和复垦决策方面的相互作用。采用遗传算法进行多目标优化，对相关政策的执行水平进行了标定。结果表明：(1)ABM模型在城市层面的优值为0.3，在较大城市区域的优值为0.58，表明ABM模型具有模拟真实土地利用动态的能力。(2)政策执行缺口导致土地流失率高，耕地复垦率低。(3)动态执行情景为协商治理提供了可行途径，使需求响应率衰减和冲突缓解成为可能，这与其他情景下观察到的土地利用冲突加剧有所不同。(4)政策应纳入适应性机制，在相互竞争的土地需求之间保持缓冲，而不是二元约束。该ABM引入了一个新的分层框架来解读政策相互作用和实施紧张关系，促进可持续土地治理和城市规划见解。

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引用次数: 0

Prediction of particulate matter (PM) in rural built environments based on Generative Adversarial Network (GAN) 基于生成对抗网络的农村建筑环境颗粒物（PM）预测

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-10-09 DOI: 10.1016/j.compenvurbsys.2025.102357

Liqiang Zhong , Hao Zheng

Particulate matter (PM) is a key parameter for characterizing outdoor air quality. PM concentration is closely related to features of built environment. Rural built environment elements at block scale, such as building massing, impermeable surfaces, and farmlands, significantly impact the PM concentration. However, current research has focused on large-scale and broad-spectrum forecasting models, which are difficult for guide designers to apply because they lack rapid, detailed forecasting and specific visualization. This study proposes an automated design procedure using the Generative Adversarial Network (GAN) model to perform spatial planning oriented by environmental performance in rural blocks. This study collected and obtained data, including satellite land cover maps and PM concentrations, to construct a prediction model. Then, the model was used to quickly and accurately predict the concentrations of three kinds of particulate matter, PM₁, PM_2.5, and PM₁₀ under different design scenarios. This study found that first, the ratio between industrial and residential buildings (IB:RB) was positively correlated with PM concentration. The buildings with a short-strip configuration exhibited the lowest PM concentration in their environment compared to clusters of buildings in long strips or block-form structures. Second, the ratio between roads and small squares (R:SS) showed a positive correlation with PM concentration. The impervious surfaces characterized by large block configurations demonstrated the lowest PM concentration among the five planar forms evaluated. Third, farmland coverage exhibited a weak negative correlation with PM concentration. Farmlands with small blocks had the lowest PM₁₀ levels among five different planar forms, and small dotted farmlands had the lowest PM₁ and PM_2.5 levels. Finally, the model was used to simulate PM concentration under different design scenarios and suggested interactive strategies for future rural spatial planning design.

颗粒物（PM）是表征室外空气质量的关键参数。PM浓度与建筑环境的特征密切相关。街区尺度的农村建筑环境要素，如建筑体量、不透水表面和农田，对PM浓度有显著影响。然而，目前的研究主要集中在大规模、广谱的预测模型上，这些模型缺乏快速、详细的预测和具体的可视化，给导盲设计人员的应用带来了困难。本研究提出了一个使用生成对抗网络（GAN）模型的自动化设计程序，以农村街区的环境绩效为导向进行空间规划。本研究收集并获得了包括卫星土地覆盖图和PM浓度在内的数据，以构建预测模型。然后，利用该模型快速准确地预测了不同设计场景下PM1、PM2.5和PM10三种颗粒物的浓度。本研究发现，首先，工业建筑与住宅建筑之比（IB:RB）与PM浓度呈正相关。与长条形或块状结构的建筑群相比，短条形结构的建筑在其环境中表现出最低的PM浓度。②道路与小广场之比（R:SS）与PM浓度呈正相关。以大块状结构为特征的不透水表面在评估的五种平面形式中显示出最低的PM浓度。耕地盖度与PM浓度呈弱负相关。5种不同平面形态中，地块面积较小的农田PM10水平最低，地块面积较小的农田PM1和PM2.5水平最低。最后，利用该模型对不同设计情景下的PM浓度进行了模拟，并提出了未来乡村空间规划设计的互动策略。

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引用次数: 0

Strategic bi-objective optimization for electric vehicle fleet replacement leveraging shared charging facilities 基于共享充电设施的电动汽车置换策略双目标优化

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-09-18 DOI: 10.1016/j.compenvurbsys.2025.102353

Shouzheng Pan , Ran Wei , Xiaoyue Cathy Liu , Jeff Phillips , Bei Wang

Electrification of vehicle fleets has advanced significantly in recent years to achieve net-zero greenhouse gas (GHG) emissions. As a cost-effective strategy, shared charging facilities are increasingly used by public and private sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge other electric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizations considering transitions to electrified mobility. It is especially difficult when considering the variability in daily fleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimization model designed to strategically guide the replacement of vehicle fleets with EV. The model aligns the spatial-temporal dynamics of vehicle routes with the availability of shared charging facilities. It is particularly relevant for organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals of minimizing GHG emissions from fuel consumption and vehicle idling, and reducing operational delays (e.g. detour and charging time for the EV fleet). We applied this model to the University of Utah campus fleet, utilizing shared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies for replacing vehicles with varied operational characteristics, offering detailed plans and schedules that balance GHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assess the effects of different battery sizes, station disruptions, and traffic delays on the model's outcomes and a feasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation for fleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage shared charging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenance costs. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems.

近年来，为了实现温室气体净零排放，汽车电气化取得了显著进展。作为一项具有成本效益的策略，公共和私营部门越来越多地使用共享充电设施。例如，可以利用公共汽车充电站的空闲时间为其他电动汽车充电。这种共享使用模式为考虑向电动交通转型的组织带来了机遇和挑战。当考虑到车队日常运营的可变性和充电基础设施的可用性时，这尤其困难。本文提出了一种双目标优化模型，用于对电动汽车置换进行战略指导。该模型将车辆路线的时空动态与共享充电设施的可用性保持一致。对于正在考虑向电动汽车战略转型的车队管理组织来说，这一点尤为重要，因为他们的目标是最大限度地减少燃油消耗和车辆怠速造成的温室气体排放，并减少运营延误（例如，电动汽车车队的绕行和充电时间）。我们将此模型应用于犹他大学校园车队，利用犹他交通局运营的共享充电设施。研究结果展示了更换具有不同运行特性的车辆的有效策略，提供了平衡温室气体减排与运行效率的详细计划和时间表。此外，我们还进行了敏感性分析，以评估不同电池尺寸、车站中断和交通延误对模型结果的影响，并进行了可行性分析，以优先替换高用途车辆。我们的研究为车队代理商制定战略性的电动汽车更换计划提供了基础，这些计划考虑了多个目标，并利用共享充电基础设施，最终实现充电设施利用率的优化和维护成本的降低。这些策略支持城市车队系统更高效、可靠和可持续的运营。

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引用次数: 0

Walking smart in the heat: A dynamic shade-oriented pathfinding approach to enhance pedestrian comfort in arid cities 在炎热中聪明地行走：一种动态的以阴影为导向的寻路方法，提高干旱城市中行人的舒适度

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-09-11 DOI: 10.1016/j.compenvurbsys.2025.102337

Jianxiu Wen , Diaa Addeen Abuhani , Martina Mazzarello , Fabio Duarte , Leslie Norford , Ruohan Xu , Nyuk Hien Wong , Carlo Ratti

In recent decades, rapid population growth in Middle Eastern cities has led to increased motorization and rising carbon emissions, particularly in car-oriented, high-density urban environments like Dubai. In response, cities are expanding their public transportation systems and promoting walking and cycling. However, scorching temperatures make the choice of active mobility challenging. This study presents a methodology to evaluate pedestrian walking environments by comparing the alignment between the shortest and optimal shadiest paths around metro stations in Dubai. Unlike existing models that apply a constant shade reward, we develop an optimal shadiest path method that assigns dynamic rewards to buildings, trees, and indoor shade, which better reflects real-world conditions. Additionally, we highlight the often-overlooked role of indoor paths in enhancing pedestrian thermal comfort, particularly in arid cities. To quantify both the shortcut effect and the shade improvement of indoor paths, we introduce the Indoor Contribution Index (ICI), a metric that assesses the role of indoor routes in improving pedestrian thermal comfort and walking efficiency. The results reveal that, on average, the optimal shadiest path is 1.3% longer than the shortest path, with an 8.9% increase in building shade and an 8.8% decrease in sun exposure compared to the shortest path. Additionally, both temporal factors and spatial characteristics significantly influence the utility of indoor routes in enhancing thermal walking comfort. Our findings highlight the need for further research into smart pathfinding algorithms that can guide pedestrians through hot environments, as well as the integration of indoor networks into urban mobility systems, particularly in Middle Eastern cities and other regions with harsh outdoor conditions.

近几十年来，中东城市人口的快速增长导致了机动化和碳排放的增加，尤其是在迪拜这样以汽车为导向的高密度城市环境中。作为回应，城市正在扩大公共交通系统，促进步行和骑自行车。然而，炎热的气温使得主动移动的选择具有挑战性。本研究提出了一种通过比较迪拜地铁站周围最短路径和最佳阴影路径之间的对齐来评估行人步行环境的方法。与现有的应用恒定荫蔽奖励的模型不同，我们开发了一种最优荫蔽路径方法，该方法为建筑物、树木和室内荫蔽分配动态奖励，从而更好地反映了现实世界的条件。此外，我们强调了室内路径在提高行人热舒适方面经常被忽视的作用，特别是在干旱城市。为了量化室内路径的捷径效应和遮荫改善，我们引入了室内贡献指数（ICI），这是一个评估室内路径在改善行人热舒适和步行效率方面的作用的指标。结果显示，平均而言，最优荫蔽路径比最短路径长1.3%，与最短路径相比，建筑物荫蔽增加8.9%，日照减少8.8%。此外，时间因素和空间特征都显著影响室内路径在提高热步行舒适度方面的效用。我们的研究结果强调，需要进一步研究智能寻路算法，以引导行人穿过炎热的环境，并将室内网络整合到城市交通系统中，特别是在中东城市和其他室外条件恶劣的地区。

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引用次数: 0

Modeling shared e-micromobility as a label propagation process for detecting overlapping communities 将共享电子微流动性建模为用于检测重叠社区的标签传播过程

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-09-10 DOI: 10.1016/j.compenvurbsys.2025.102336

Peng Luo , Chengyu Song , Hao Li , Di Zhu , Songhua Hu , Fábio Duarte

Shared micro-mobility such as electric scooters (e-scooters) has gained significant popularity in many cities. While many studies have analyzed the spatiotemporal patterns of shared micro-mobility using individual-level trip data, the spatial structure of e-scooter mobility networks and their socio-economic implications remain underexplored. Examining these mobility networks through the lens of network science — such as analyzing their community structures — can provide valuable insights for urban policy and planning. For example, allocating e-scooters at the overlapping locations of two communities may improve the operational efficiency of e-scooter distribution. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns.We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. Additionally, we discovered significant social segregation within the overlapping communities: areas belong to multiple communities tend to be wealthier with shorter commute times. Our results provide a potential explanation for the community structure in human mobility networks and may offer insights for urban planning and policymaking aimed at creating a more equitable and accessible mobility system.

共享的微型交通工具，如电动滑板车（e-scooters），在许多城市都很受欢迎。虽然许多研究利用个人层面的出行数据分析了共享微出行的时空模式，但电动滑板车出行网络的空间结构及其社会经济影响仍未得到充分探讨。通过网络科学的视角审视这些移动网络——比如分析它们的社区结构——可以为城市政策和规划提供有价值的见解。例如，在两个社区的重叠位置分配电动滑板车，可以提高电动滑板车配送的运营效率。然而，现有的检测移动网络中社区结构的方法往往忽略了社区之间潜在的重叠。在本研究中，我们将城市空间中的共享微交通概念化为一种信息交换的过程，通过电动滑板车连接各个地点，促进社区关系的互动和传播。因此，相似的位置被分配了相同的标签。基于这一概念，我们通过设计一个考虑地理距离衰减的说话-听者标签传播算法（SLPA），开发了一个地理空间交互传播模型（GIP），并结合异常检测来确保衍生的社区结构反映有意义的空间模式。我们将该模型应用于华盛顿特区的电动滑板车系统中的重叠社区检测。结果表明，我们的算法在效率和模块化方面都优于现有的重叠社区检测模型。此外，我们发现在重叠的社区中存在显著的社会隔离：属于多个社区的地区往往更富裕，通勤时间更短。我们的研究结果为人类移动网络中的社区结构提供了一个潜在的解释，并可能为城市规划和政策制定提供见解，旨在创造一个更公平和可访问的移动系统。

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引用次数: 0

Urban visual uniqueness: A landmark-free framework to quantify city's identity and distinctiveness from everyday scenes 城市视觉独特性：一个无地标的框架，从日常场景中量化城市的身份和独特性

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-09-06 DOI: 10.1016/j.compenvurbsys.2025.102351

Song Guo , Kee Moon Jang , Fábio Duarte , Yuhao Kang , Carlo Ratti

The visual appearance of a city is shaped by a complex interplay of factors, including cultural backgrounds, geographical features, historical developments, and policy decisions. But measuring cities' visual uniqueness remains a challenge. Previous studies often focused on iconic landmarks, neglecting everyday scenes that people are likely to encounter. By examining how and to what extent different visual patterns build up unique characteristics of cities, we propose a data-driven framework to measure visual uniqueness in terms of identity and distinctiveness. We performed bottom-up visual clustering on Google Street View (GSV) images in the six most visited Japanese cities. We found that 8 representative visual clusters explain each city's visual identity and relative distinctiveness. This research demonstrates how artificial intelligence applied to visual data can reveal subtle differences in urban environments. In the era of growing globalization, with frequent tourism and intercity visits, the cultivation of a city's unique visual characteristics can help avoid the homogenization of urban landscapes, and stimulate the development of urban tourism by shaping an imageable city.

城市的视觉外观是由文化背景、地理特征、历史发展和政策决定等因素的复杂相互作用形成的。但衡量城市的视觉独特性仍然是一个挑战。以前的研究通常集中在标志性地标上，而忽略了人们可能会遇到的日常场景。通过研究不同的视觉模式如何以及在多大程度上构建城市的独特特征，我们提出了一个数据驱动的框架，从身份和独特性方面衡量视觉独特性。我们对日本六个访问量最大的城市的谷歌街景（GSV）图像进行了自下而上的视觉聚类。我们发现8个具有代表性的视觉集群解释了每个城市的视觉识别和相对独特性。这项研究展示了人工智能如何应用于视觉数据，从而揭示城市环境中的细微差异。在日益全球化的时代，旅游和城际往来频繁，城市独特的视觉特征的培养有助于避免城市景观的同质化，并通过塑造可想象的城市来刺激城市旅游的发展。

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引用次数: 0