Computers Environment and Urban Systems最新文献_第6页

Optimizing urban green space spatial patterns for thermal environment improvement: A multi-objective approach in the context of urban renewal 基于热环境改善的城市绿地空间格局优化：城市更新背景下的多目标方法

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-12 DOI: 10.1016/j.compenvurbsys.2025.102320

Liangguo Lin , Yaolong Zhao , Juchao Zhao

The rapid and inevitable trend of urbanization has amplified urban thermal challenges, intensifying the urban heat island (UHI) effect. Given the constraints of limited urban land resources, optimizing the spatial patterns of urban green space (UGS) to maximize their cooling potential is essential for mitigating urban thermal environments and supporting effective urban renewal planning. This research integrates the XGBoost model with the NSGA-II algorithm to propose a multi-objective approach to optimize UGS spatial patterns for thermal environment improvement, using the central urban area of Guangzhou, China, as a case study in the context of urban renewal. To further assess the effectiveness of optimization, the Shapley additive explanation (SHAP) model was employed to examine how landscape pattern metrics, which characterize UGS spatial patterns, influence LST before and after optimization. The results demonstrate that optimized UGS spatial patterns, achieved through a controlled expansion of UGS area, significantly alleviated thermal stress by reducing the total LST by 2,799.82 °C and lowering its standard deviation by 0.04. Industrial zones, densely populated areas, and commercial districts exhibited the most pronounced LST reductions that spatially corresponded to changes in UGS spatial patterns. In addition, post-optimization analysis revealed notable changes in key landscape pattern metrics: patch cohesion index (COHESION), patch density (PD), landscape shape index (LSI), and percent of landscape (PLAND). Compared to pre-optimization conditions, their positive contributions to LST were weakened, while their cooling effects were enhanced. This research provides a “space-for-time” planning paradigm that offers intuitive and actionable decision-making support for urban renewal planners and policymakers.

城市化的快速和必然趋势加大了城市热挑战，加剧了城市热岛效应。在城市土地资源有限的约束下，优化城市绿地空间格局，最大限度地发挥其降温潜力，对于缓解城市热环境和支持有效的城市更新规划至关重要。本研究将XGBoost模型与NSGA-II算法相结合，以城市更新背景下的广州中心城区为例，提出了一种多目标优化UGS空间格局的热环境改善方法。为了进一步评估优化的有效性，采用Shapley加性解释（SHAP）模型研究了景观格局指标（表征UGS空间格局）在优化前后对地表温度的影响。结果表明，优化后的UGS空间格局通过控制UGS面积的扩大，使总地表温度降低2,799.82°C，标准差降低0.04，显著缓解了热应力。工业区、人口稠密地区和商业区的地表温度降低最为明显，这在空间上与UGS空间格局的变化相对应。此外，优化后分析显示，斑块衔接指数（cohesion）、斑块密度（PD）、景观形状指数（LSI）和景观百分比（PLAND）等关键景观格局指标发生了显著变化。与优化前相比，它们对地表温度的正贡献减弱，而冷却作用增强。本研究提供了一个“空间换时间”的规划范式，为城市更新规划者和决策者提供了直观和可操作的决策支持。

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引用次数: 0

From grids to dendrites: Quantifying spatial heterogeneity in urban road networks 从网格到树突：量化城市道路网络的空间异质性

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-03 DOI: 10.1016/j.compenvurbsys.2025.102309

Lin Zhang , Shenhong Li , Yaolin Liu , Haosheng Huang , Nico Van de Weghe

Road network spatial heterogeneity significantly influences urban development and infrastructure efficiency. We present a novel approach using Relational Graph Convolutional Networks (RGCN) to analyze road networks across 58 global cities from 2020 to 2024, introducing Hits@1 as a comprehensive measure of spatial heterogeneity. When nodes (Road intersections) exhibit high spatial heterogeneity, they are more diverse and distinct from each other, making the embedding process more straightforward for the RGCN model. A higher Hits@1 score indicates RGCN can better differentiate between nodes, directly correlating with greater spatial heterogeneity in the road network. Our analysis demonstrates that Hits@1 can effectively distinguish four road network typologies (Dendritic, Grid, Mixed, and Polygonal), with Dendritic networks showing the highest heterogeneity (Hits@1

\approx

0.57) and Grid networks the lowest (Hits@1

\approx

0.42). Statistical analysis reveals strong correlations between heterogeneity and urban metrics, including traffic index (R = 0.36), CO2 emissions (R = 0.43), and road density (R = 0.48). Temporal analysis of road evolution shows distinct regional patterns: developing regions trend toward higher heterogeneity, while Western cities demonstrate increasing uniformity. Chinese coastal cities exhibit increasing complexity, contrasting with inland cities’ movement toward organized patterns. These findings validate Hits@1 as an effective metric for understanding road network evolution and provide valuable insights for urban planning.

道路网络空间异质性显著影响城市发展和基础设施效率。我们提出了一种使用关系图卷积网络（RGCN）分析2020年至2024年全球58个城市道路网络的新方法，并引入Hits@1作为空间异质性的综合衡量标准。当节点（道路交叉口）表现出较高的空间异质性时，它们的多样性和差异性更强，使得RGCN模型的嵌入过程更加直观。Hits@1得分越高，表明RGCN对节点的区分能力越强，与路网空间异质性直接相关。我们的分析表明Hits@1可以有效区分四种道路网络类型（树突状、网格状、混合状和多边形），其中树突状网络的异质性最高（Hits@1≈0.57），网格网络的异质性最低（Hits@1≈0.42）。统计分析表明，异质性与交通指数（R = 0.36）、二氧化碳排放量（R = 0.43）和道路密度（R = 0.48）等城市指标之间存在很强的相关性。道路演变的时间分析显示出明显的区域格局：发展中地区的异质性增强，而西部城市的均一性增强。中国沿海城市呈现出日益增长的复杂性，与内陆城市形成鲜明对比。这些发现验证了Hits@1是理解道路网络演变的有效指标，并为城市规划提供了有价值的见解。

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引用次数: 0

Maximum unobstructed shortest path between multipart-continuous geometries: Enabling novel type of access evaluations for urban safety 多部分连续几何之间的最大无障碍最短路径：为城市安全提供新型通道评估

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-06-03 DOI: 10.1016/j.compenvurbsys.2025.102308

Jiwon Baik, Alan T. Murray

In safety planning, preparing for worst-case scenarios is critical. For instance, fire stations are strategically located aiming to respond within four minutes in the worst-case. Similarly, hydrant-to-structure access adheres to this principle. Fire codes require that the furthest projection on a building's exterior must be within a specified distance from fire access roads via an unobstructed route. This ensures that all parts of a building are reachable by a fire hose from parked fire apparatus. This requirement involves a novel spatial optimization problem: the Maximum Generalized Euclidean shortest path problem. The Euclidean shortest path problem is an approach for determining an unobstructed shortest path, however, constrained to single-point representations for origin and destination. This research generalizes this problem to identify unobstructed paths between multipart-continuous geometries, such as road segments and building structures. A novel solution approach is also proposed, expanding the scope of access evaluation and advocating safety planning.

在安全规划中，为最坏的情况做准备是至关重要的。例如，消防站的战略位置旨在在最坏的情况下在四分钟内做出反应。同样，消火栓到结构的访问也遵循这一原则。消防规范要求建筑物外部最远的投影必须在消防通道的指定距离内，通过畅通无阻的路线。这确保了建筑物的所有部分都可以通过停放的消防设备的消防水带到达。这一要求涉及到一个新的空间优化问题：最大广义欧几里得最短路径问题。欧几里得最短路径问题是一种确定无阻碍最短路径的方法，然而，它被限制为原点和目的地的单点表示。本研究将此问题推广到多部分连续几何体（如路段和建筑结构）之间的无障碍路径识别。提出了一种新的解决方法，扩大了通道评价的范围，倡导安全规划。

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引用次数: 0

On the power of CNNs to detect slums in Brazil 关于cnn探测巴西贫民窟的力量

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-31 DOI: 10.1016/j.compenvurbsys.2025.102306

João P. da Silva , José F. Rodrigues-Jr , João P. de Albuquerque

The rapid expansion of slums poses a critical challenge for urban planning in Low- and Middle-Income Countries (LMICs), where traditional data collection methods like censuses are often outdated and insufficient. This study examines the transferability and generalization capabilities of deep learning models, specifically Convolutional Neural Networks (CNNs), for automated slum detection across six Brazilian cities with varying urban morphologies: São Paulo, Rio de Janeiro, Belo Horizonte, Brasília, Salvador, and Porto Alegre. Utilizing Very High Resolution (VHR) and High Resolution (HR) satellite imagery, we trained and evaluated models based on the EfficientNetV2L architecture. Our experimental results show that CNN models trained on data from a single city achieved high accuracy within that city (F1 scores exceeding 0.90 with VHR imagery), but their performance significantly decreased when applied to other cities (F1 scores dropping below 0.80), highlighting the impact of regional variations in urban morphology. Conversely, a generalized model trained on combined data from all six cities maintained robust performance across all cities, achieving F1 scores above 0.80 with VHR imagery. These findings indicate that while CNNs are effective for automated slum mapping, regional diversity necessitates training on diverse datasets to ensure generalization. We provide a comprehensive methodology over an openly shared dataset, and code to facilitate future research and applications in urban geoscience. The aim is to enhance the scalability and generalization of remote sensing and deep learning methods for slum identification across diverse urban environments.

贫民窟的迅速扩大对低收入和中等收入国家的城市规划构成了严峻挑战，在这些国家，人口普查等传统数据收集方法往往过时且不充分。本研究考察了深度学习模型的可转移性和泛化能力，特别是卷积神经网络（cnn），用于在巴西六个城市进行自动贫民窟检测，这些城市具有不同的城市形态：圣保罗、里约热内卢、贝洛奥里藏特、Brasília、萨尔瓦多和阿雷格里港。利用甚高分辨率（VHR）和高分辨率（HR）卫星图像，我们训练和评估了基于EfficientNetV2L架构的模型。我们的实验结果表明，在单个城市数据上训练的CNN模型在该城市内获得了较高的准确性（VHR图像的F1分数超过0.90），但在其他城市中应用时，其性能显著下降（F1分数低于0.80），突出了城市形态区域差异的影响。相反，在所有六个城市的综合数据上训练的广义模型在所有城市都保持了稳健的表现，在VHR图像上获得了0.80以上的F1分数。这些发现表明，虽然cnn对于自动绘制贫民窟地图是有效的，但区域多样性需要在不同的数据集上进行训练，以确保泛化。我们在一个公开共享的数据集和代码上提供了一个全面的方法，以促进未来城市地球科学的研究和应用。其目的是提高遥感和深度学习方法在不同城市环境中识别贫民窟的可扩展性和通用性。

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引用次数: 0

Torque work of origin-destination flows: Quantifying urban place centrality from a physical perspective 起点-终点流的扭矩功：从物理角度量化城市场所中心性

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-31 DOI: 10.1016/j.compenvurbsys.2025.102311

Xiaorui Yan , Tao Pei , Ci Song , Zidong Fang , Xiaohan Liu , Tianyu Liu , Linfeng Jiang , Ying Gao , Guangdong Li , Jie Huang , Yaqin Sun

Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.

量化城市中心性，定义为其在服务周边地区方面的相对重要性，提供了对城市结构的见解，优化了资源配置，并支持了战略性城市规划。中心性的形成有三个方面：服务量、空间范围和方向多样性。然而，现有的措施往往单独评估这些因素，很少将它们同时结合起来。此外，中心性分析通常会忽略局部视角和内部动态。为此，我们提出了一种新颖的基于起点-终点流量的中心性度量，即总扭矩功（TTW），它将这三个方面整合为一个值，概念为“流量的扭矩功”，其中流量、长度和方向对应于力的大小、杠杆臂和角位移。仿真实验验证了该方法的有效性。我们使用出租车和共享单车流量数据，将这一方法应用于分析北京的宏观和微观中心性。宏观中心性表现为单中心结构，在火车站、机场和商业中心附近值较高。微中心性表现为多中心性，其中地铁站的中心性更高。时间序列聚类确定了宏观和微观中心的三种时间模式：两种与多功能活动和通勤相关的“白天主导”模式，以及居住区的“夜间主导”模式。该研究总结了对城市规划的几点启示，强调了结合多时空尺度的重要性。

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引用次数: 0

Inclusive Digital Planning – Co-designing a collaborative mapping tool to support the planning of accessible public space for all 包容性数字规划——共同设计一种协作测绘工具，以支持所有人的无障碍公共空间规划

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-29 DOI: 10.1016/j.compenvurbsys.2025.102310

Johannes Flacke , Fenna Imara Hoefsloot , Karin Pfeffer

Digital planning is loaded with the expectation to make planning more inclusive. However, digital tools currently used in spatial planning processes to facilitate communication and participation of stakeholders often exclude people with disabilities through their design. Consequently, the research question of this study is how to design digital tools to support inclusive participation in the planning and design of public spaces to make them accessible for all. To answer this question, this research aimed to co-design an inclusive collaborative mapping tool with people with disabilities to enhance their participation in the planning and design of accessible public spaces. Developed in collaboration with eight people with various disabilities from the city of Zwolle in the Netherlands, the open-source mapping tool allows the in-situ registration of accessibility issues and supports collaborative decision-making workshops. The co-design process served to identify barriers and obstacles to the accessibility of public spaces in the city as well as user requirements for the inclusive design of the collaborative mapping tool. The tool was tested and evaluated in a collaborative mapping session with people with disabilities and municipal planners from the case study city. Our findings show that the design of inclusive digital planning tools is not limited to software features but also relates to hardware functionalities and the environment in which a tool is used. Taking the lessons learned from the co-design process, we argue that digital, physical, social and procedural accessibility are key to achieving inclusive digital planning.

数字规划承载着使规划更具包容性的期望。然而，目前空间规划过程中用于促进利益攸关方沟通和参与的数字工具往往将残疾人排除在设计之外。因此，本研究的研究问题是如何设计数字工具来支持包容性参与公共空间的规划和设计，使其对所有人开放。为了回答这个问题，本研究旨在与残疾人共同设计一个包容性的协作地图工具，以增强他们对无障碍公共空间规划和设计的参与。这个开源地图工具是与来自荷兰兹沃勒市的8名不同残疾人士合作开发的，它允许对无障碍问题进行现场登记，并支持协作决策研讨会。协同设计过程有助于识别城市公共空间可达性的障碍和障碍，以及用户对协作地图工具包容性设计的要求。在与来自案例研究城市的残疾人和市政规划者的协作绘图会议中，对该工具进行了测试和评估。我们的研究结果表明，包容性数字规划工具的设计不仅限于软件功能，还与硬件功能和工具使用环境有关。根据共同设计过程的经验教训，我们认为数字、物理、社会和程序可及性是实现包容性数字规划的关键。

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引用次数: 0

Convolutional neural networks for predicting the perceived density of large urban fabrics 用于预测大型城市结构感知密度的卷积神经网络

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-22 DOI: 10.1016/j.compenvurbsys.2025.102304

Guy Austern , Roei Yosifof , Tomer Michaeli , Shahar Yadin , Dafna Fisher-Gewirtzman

Urban density, along with the associated urban morphology and topology, significantly influences human perception, emotions, and behavior, ultimately affecting our overall well-being. Over the past decades, experts have developed spatial analysis models and tools which evaluate how planning and design impact urban residents and the functionality of cities. One such spatial analyses model is the Urban Spatial Openness Index (USOI) which utilizes ray-casting to conduct 3D visibility analysis predicting the perceived density of entire cities on a macro-scale, represented as 2D heatmaps. In the urban scale, ray-casting analysis is computationally intense and requires significant resources, which hinders its effective application. In this paper, we use a Convolutional Neural Network (CNN) to train a model to predict perceived density in urban fabrics based on 2D heatmap images. The processes described in this paper include creating a dataset of corresponding USOI images and height images from several different cities, training a CNN model, and evaluating the model's performance. The model predicts USOI with a mean absolute error of 1.92 %, which is considered highly accurate for visual perception on the urban scale. This study showcases the capability of CNN models to predict perceived density as measured by the USOI. The use of a predictive model can significantly reduce the processing time of 3D visibility analysis on the urban scale.

城市密度，以及相关的城市形态和拓扑结构，显著影响着人类的感知、情感和行为，最终影响着我们的整体福祉。在过去的几十年里，专家们开发了空间分析模型和工具来评估规划和设计如何影响城市居民和城市功能。其中一个空间分析模型是城市空间开放指数（USOI），它利用光线投射进行三维可见性分析，预测宏观尺度上整个城市的感知密度，以2D热图表示。在城市尺度下，光线投射分析需要大量的计算量和资源，这阻碍了它的有效应用。在本文中，我们使用卷积神经网络（CNN）来训练一个基于二维热图图像的模型来预测城市织物的感知密度。本文描述的过程包括创建来自几个不同城市的相应USOI图像和高度图像的数据集，训练CNN模型，并评估模型的性能。该模型预测USOI的平均绝对误差为1.92%，对于城市尺度的视觉感知来说，这被认为是高度准确的。本研究展示了CNN模型预测由USOI测量的感知密度的能力。预测模型的使用可以显著缩短城市尺度三维能见度分析的处理时间。

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引用次数: 0

Deciphering Urban Soundscapes: A study of sensory experiences at Hong Kong Victoria harbour waterfronts using social media 解读城市声景：利用社会媒体在香港维多利亚港海滨进行感官体验研究

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-16 DOI: 10.1016/j.compenvurbsys.2025.102307

Haotian Wang, Zidong Yu, Xintao Liu

The impact of sensory experiences on physical and mental health in urban environments has gained significant attention, particularly the influence of soundscapes in waterfronts development. This study employed social media data from Twitter to quantitatively analyse the soundscape of Hong Kong Victoria Harbour waterfronts, offering a novel perspective in urban sensory research. Through comparative analysis between tourists and residents, it uncovered how different groups perceive soundscapes in these specific urban waterfronts setting. Utilizing a two-step analytical approach—initially applying rank-size distribution and mean difference index—this study mapped the spatial distribution of soundscapes and used global and local regression models to explore their correlations with key urban features such as building density, population density, and ethnic diversity. The findings revealed distinct spatial patterns in how soundscapes are experienced by tourists and residents at the Victoria Harbour waterfronts, influenced significantly by the built environment. For instance, while residents experience negative auditory sensory in high building density areas, tourists perceive these areas positively. Furthermore, this research underscored the differing correlations of population density on soundscape experience among these groups. Residents enjoy positive soundscape connections in bustling areas, whereas tourists prefer quieter environments. Moreover, the research also found the differences in how residents and tourists accept multicultural soundscapes. This study not only contributed theoretically by linking soundscapes to urban and socio-economic variables but also demonstrated the potential of social media data as a tool for studying urban sensory. The study findings could offer insights that are relevant to planning and design of urban waterfronts.

感官体验对城市环境中身心健康的影响已引起广泛关注，尤其是滨水开发中声景的影响。本研究利用Twitter上的社交媒体数据，定量分析香港维多利亚港海滨的声景，为城市感官研究提供了一个新的视角。通过对游客和居民的比较分析，揭示了不同群体在这些特定的城市滨水区环境中如何感知声景。本研究采用两步分析方法——首先应用秩-大小分布和平均差异指数——绘制了声景观的空间分布，并使用全球和局部回归模型来探索其与建筑密度、人口密度和种族多样性等关键城市特征的相关性。研究结果显示，在维多利亚港海滨，游客和居民对声景的体验有明显的空间模式，受建筑环境的显著影响。例如，在高建筑密度地区，居民的听觉感受是消极的，而游客对这些地区的感知是积极的。此外，本研究还强调了这些群体中人口密度与声景体验的不同相关性。居民喜欢热闹地区的正面音景连接，而游客更喜欢安静的环境。此外，研究还发现，居民和游客接受多元文化音景的方式存在差异。这项研究不仅通过将声景与城市和社会经济变量联系起来在理论上做出了贡献，而且还展示了社交媒体数据作为研究城市感官的工具的潜力。研究结果可以为城市滨水区的规划和设计提供相关的见解。

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引用次数: 0

Digital twins and AI for healthy and sustainable cities 数字孪生和人工智能为健康和可持续发展的城市服务

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-12 DOI: 10.1016/j.compenvurbsys.2025.102305

Mark Birkin , Patrick Ballantyne , Seth Bullock , Alison Heppenstall , Heeseo Kwon , Nick Malleson , Jing Yao , Anna Zanchetta

The paper discusses the relevance of the latest advances in data science and artificial intelligence for urban systems research. It has a particular focus on the importance of recent innovations in the context of ‘wicked’ urban problems which continue to confront decision-makers within practical policy settings. It is argued that the latest advances in AI such as large language models offer the potential for transformative research, but only if properly specified within the unique and distinctive context of geographical space. The idea of a digital twin requires careful articulation to support the management of expectations and appropriate alignment within a social setting. At the end of the day, AI is not a panacea for the problems of cities, nor is it a substitute for imaginative policy design or interventions through consensus and good government. However in a world which is characterised by vast riches of data alongside enormous complexity of process, the investment in new tools and methods is a social and intellectual imperative in driving human understanding to new levels.

本文讨论了数据科学和人工智能的最新进展与城市系统研究的相关性。它特别关注最近创新在“邪恶”城市问题背景下的重要性，这些问题在实际政策设置中继续面临决策者。有人认为，人工智能的最新进展，如大型语言模型，为变革性研究提供了潜力，但前提是在地理空间的独特和独特背景下适当指定。数字孪生的概念需要仔细表述，以支持期望管理和社会环境中的适当协调。归根结底，人工智能不是解决城市问题的灵丹妙药，也不能替代富有想象力的政策设计或通过共识和良好政府进行干预。然而，在一个以海量数据和极其复杂的过程为特征的世界里，对新工具和新方法的投资是推动人类理解达到新水平的社会和智力要求。

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引用次数: 0

So close, yet so far: A new method for identification of high-impact missing links in pedestrian networks 如此接近，却又如此遥远：一种识别行人网络中高影响缺失环节的新方法

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-05-12 DOI: 10.1016/j.compenvurbsys.2025.102290

Matthew Wigginton Bhagat-Conway , Audrey Compiano , E. Irene Ivie

Post-war suburban development is often characterized by a disconnected pod-and-collector street pattern. This creates significant barriers to active travel, forcing even short trips to take roundabout routes on busy arterial roads. However, it also creates a network of low-stress neighborhood streets. We hypothesize that there are many opportunities to add short, low-cost pedestrian and bicycle links to these street networks to increase connectivity.

A key challenge is identifying these links. While planners have a good idea of where major infrastructure investments are beneficial, they are unlikely to be familiar with every neighborhood street and potential connections between them. We introduce an algorithm to automatically and efficiently identify potential new links based only on existing network topology, with no need to prespecify potential projects. We score these links based on their contribution to accessibility. We apply this algorithm to the pedestrian network of Charlotte, North Carolina, USA, and find opportunities to improve connectivity through new links and safe crossings of major roads.

战后郊区发展的特点往往是一个不连贯的豆荚和收集器的街道模式。这对主动出行造成了重大障碍，甚至迫使短途旅行在繁忙的主干道上绕道而行。然而，它也创造了一个低压力的社区街道网络。我们假设有很多机会在这些街道网络中增加短的、低成本的步行和自行车连接，以增加连通性。一个关键的挑战是确定这些联系。虽然规划者很清楚大型基础设施投资在哪些地方是有益的，但他们不太可能熟悉每一条社区街道以及它们之间的潜在联系。我们引入了一种算法，可以根据现有的网络拓扑自动有效地识别潜在的新链路，而无需预先指定潜在的项目。我们根据它们对可访问性的贡献对这些链接进行评分。我们将该算法应用于美国北卡罗来纳州夏洛特市的行人网络，并通过新的连接和主要道路的安全交叉来寻找改善连通性的机会。

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引用次数: 0