Computers Environment and Urban Systems最新文献

{"title":"Route planning of mobile sensing fleets for repeatable environmental monitoring tasks","authors":"Wen Ji ,&nbsp;Ke Han ,&nbsp;Qian Ge","doi":"10.1016/j.compenvurbsys.2025.102285","DOIUrl":"10.1016/j.compenvurbsys.2025.102285","url":null,"abstract":"<div><div>Vehicle-based mobile sensing is a new paradigm for urban data collection. Certain urban sensing scenarios require sensing vehicles for highly targeted monitoring, such as air pollutant and accident site investigation. A hallmark of these scenarios is that the points of interest (POIs) need to be repeatedly visited by a set of agents, whose routes should provide sufficient sensing coverage with coordinated overlap at certain important POIs. For these applications, this paper presents the <em>open team orienteering problem with repeatable visits</em> (OTOP-RV) and specifically tailors an adaptive large neighborhood search (ALNS) algorithm to address it. Test results on randomly generated datasets show that the ALNS significantly outperforms the greedy algorithm (by 7.2 % to 32.4 %), and a heuristic based on sequential orienteering problem (by about 6 %). Finally, a real-world air pollution sensing case study demonstrates the unique applicability of the OTOP-RV and the effectiveness of the proposed algorithms in enhancing sensing capabilities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102285"},"PeriodicalIF":7.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical urban change and its socio-environmental impact: Insights from street view imagery","authors":"Yingjie Liu ,&nbsp;Zeyu Wang ,&nbsp;Siyi Ren ,&nbsp;Runying Chen ,&nbsp;Yixiang Shen ,&nbsp;Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102284","DOIUrl":"10.1016/j.compenvurbsys.2025.102284","url":null,"abstract":"<div><div>Urban transformation not only reshapes physical spaces but also impacts public perception, influencing how people experience their environments. This study utilizes Street View Imagery (SVI) as an emerging, human-level data source to assess urban changes, providing perspective beyond traditional datasets. Existing studies often focus on either urban physical changes or human perception changes, without bridging the two. This research integrates both aspects by combining a change detection model, trained on a self-labeled dataset, and a human perception model based on the crowdsourced Place Pulse 2.0 dataset with input from 81,630 online volunteers, to analyze urban transformation in New York City and Memphis from 2007 to 2023. Our findings reveal differences between the two cities: New York City exhibited small, isolated changes often driven by community needs, while Memphis transitioned from concentrated to more dispersed development patterns. This study provides insights into how physical changes influence public perception within these two cities. It demonstrates how thoughtful, well-planned urban transformation can improve neighborhood's perception such as safety and livability, while also pointing out potential challenges like gentrification or social fragmentation. These findings provide policymakers with valuable insights into human perception, aiding in the creation of more inclusive, vibrant, and resilient urban transformation. This helps ensure that urban transformation efforts are based on community desires and align with long-term sustainability goals.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102284"},"PeriodicalIF":7.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science","authors":"Koichi Ito ,&nbsp;Yihan Zhu ,&nbsp;Mahmoud Abdelrahman ,&nbsp;Xiucheng Liang ,&nbsp;Zicheng Fan ,&nbsp;Yujun Hou ,&nbsp;Tianhong Zhao ,&nbsp;Rui Ma ,&nbsp;Kunihiko Fujiwara ,&nbsp;Jiani Ouyang ,&nbsp;Matias Quintana ,&nbsp;Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102283","DOIUrl":"10.1016/j.compenvurbsys.2025.102283","url":null,"abstract":"<div><div>Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases. This package is openly available at <span><span><span>https://github.com/koito19960406/ZenSVI</span></span><svg><path></path></svg></span>, and it is supported by documentation including tutorials (<span><span><span>https://zensvi.readthedocs.io/en/latest/examples/index.html</span></span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102283"},"PeriodicalIF":7.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping urban villages in China: Progress and challenges","authors":"Rui Cao ,&nbsp;Wei Tu ,&nbsp;Dongsheng Chen ,&nbsp;Wenyu Zhang","doi":"10.1016/j.compenvurbsys.2025.102282","DOIUrl":"10.1016/j.compenvurbsys.2025.102282","url":null,"abstract":"<div><div>The shift toward high-quality urbanization has brought increased attention to the issue of “urban villages”, which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation: 1) establish a unified standard of urban villages in China to accommodate significant variances and fuzzy concepts, 2) address the challenges of data availability via flexible use of available multisource data and explore potential use of data-driven image super-resolution approaches, 3) create public benchmarks to ensure fair comparison and focus on the scalability, transferability, and interpretability of urban village recognition approaches, 4) initiate a crowdsourcing program to enable effective and efficient data collection and validation as well as application. This review not only supports urban village-related research in China, but also contributes valuable knowledge from a Chinese perspective to global informal settlements mapping research and the achievement of the United Nations' Sustainable Development Goals (SDGs).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102282"},"PeriodicalIF":7.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantify relationships between bike network connectivity and bike safety: A comparative analysis of connectivity metrics conducted in two California cities","authors":"Jiahua Chen ,&nbsp;Peter Kedron ,&nbsp;Trisalyn Nelson ,&nbsp;Dan Willett ,&nbsp;Achituv Cohen ,&nbsp;Colin Ferster","doi":"10.1016/j.compenvurbsys.2025.102271","DOIUrl":"10.1016/j.compenvurbsys.2025.102271","url":null,"abstract":"<div><div>To motivate people to use bikes for transportation, cities are shifting their focus from constructing isolated bike lanes to building interconnected bike networks. The effectiveness of these networks is measured by their level of connectivity, specifically how easily individuals of all ages and abilities can reach their destinations by bike. While most researchers and policymakers hypothesize that well-connected bike networks will reduce crash risk by offering bicyclists extended protection from traffic, most studies find positive or null associations between network connectivity and bike crashes. This discrepancy may arise either from actual processes, such as increased ridership in high-traffic areas, or from variability in how connectivity is measured. Our study aims to understand relationships between bike safety and various connectivity metrics at the neighborhood level by deconstructing and comparing different metrics. We critique previous constructs of density-based metrics rely solely on bike infrastructure and introduce new density-based and routing-based metrics derived from low-stress networks. Using a negative binomial regression model, we examine the association between bike crashes and connectivity metrics across 125 block groups in Santa Barbara and Goleta, California. We find that increased density-based connectivity in both bike infrastructure and low-stress networks correlates with fewer crashes. In contrast, routing-based connectivity measures, which reflect bike access to key destinations, are positively associated with crashes. We conclude that different connectivity metrics can alter the direction of connectivity-safety associations. Our proposed metrics, which incorporate low-stress networks and routing algorithms, provide a more nuanced understanding of how connectivity is related to bicycling safety.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102271"},"PeriodicalIF":7.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation","authors":"Peiran Li ,&nbsp;Haoran Zhang ,&nbsp;Wenjing Li ,&nbsp;Dou Huang ,&nbsp;Zhiling Guo ,&nbsp;Jinyu Chen ,&nbsp;Junxiang Zhang ,&nbsp;Xuan Song ,&nbsp;Pengjun Zhao ,&nbsp;Jinyue Yan ,&nbsp;Shibasaki Ryosuke ,&nbsp;Noboru Koshizuka","doi":"10.1016/j.compenvurbsys.2025.102252","DOIUrl":"10.1016/j.compenvurbsys.2025.102252","url":null,"abstract":"<div><div>The importance of personal mobility data is widely recognized in various fields. However, the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to generate pseudo personal mobility data that accurately reflects real-world mobility patterns while safeguarding user privacy. Nevertheless, existing methods for generating pseudo mobility data, mostly focusing on trip or trajectory generation, have limitations in capturing sufficient individual heterogeneity. To address these gaps, taking pseudo-person(avatar) as ground-zero, a novel individual-based human mobility generator named GeoAvatar has been proposed – which considering individual heterogeneity in spatial and temporal decision-making, incorporates demographic characteristics. Our method utilizes a deep generative model to generate heterogeneous individual life patterns, a variation inference model for inferring individual demographic characteristics, and a Bayesian-based approach for generating spatial choices considering individual demographic characteristics. Through our method, we have achieved generating realistic pseudo personal human mobility data - we evaluated the proposed method based on physical features – obeying common law of human mobility, activity features – showing diverse and realistic activities, and spatial-temporal characteristics – presenting high-accuracy in terms of temporal grid population and od-count, demonstrating its good performance, with both a big mobile phone GPS trajectory dataset from Tokyo Metropolis and a big mobile phone CDR dataset from Shanghai. Furthermore, this method maintains extensibility for broader applications, making it a promising framework for generating pseudo personal human mobility data.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102252"},"PeriodicalIF":7.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling active travel accessibility at the micro-scale using multi-source built environment data","authors":"Corin Staves ,&nbsp;Irena Itova ,&nbsp;Belen Zapata-Diomedi ,&nbsp;Audrey de Nazelle ,&nbsp;Jenna Panter ,&nbsp;Lucy Gunn ,&nbsp;Alan Both ,&nbsp;Yuchen Li ,&nbsp;Ismail Saadi ,&nbsp;James Woodcock ,&nbsp;S.M. Labib","doi":"10.1016/j.compenvurbsys.2025.102270","DOIUrl":"10.1016/j.compenvurbsys.2025.102270","url":null,"abstract":"<div><div>Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people's willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers' experienced environment.</div><div>This study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policy-relevant accessibility indicators for Greater Manchester.</div><div>In the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102270"},"PeriodicalIF":7.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From theory to deep learning: Understanding the impact of geographic context factors on traffic violations","authors":"Hao Yang ,&nbsp;X. Angela Yao ,&nbsp;Farnoosh Roozkhosh ,&nbsp;Ruowei Liu ,&nbsp;Gengchen Mai","doi":"10.1016/j.compenvurbsys.2025.102268","DOIUrl":"10.1016/j.compenvurbsys.2025.102268","url":null,"abstract":"<div><div>Traffic violations pose significant threats to road safety, often leading to accidents, injuries, fatalities, and property damage. While driving behavior results from a dynamic interaction among drivers, vehicles, and the environment, most studies have primarily focused on human factors, neglecting the critical influence of social and environmental elements of the geographical context. To address this gap, we first develop a theoretical framework that explores how vehicles, drivers, and environmental factors interact, with particular emphasis on the environmental impact on driving behavior and road safety. Building on this framework, we introduce TraVio, a multitask deep learning model that combines Graph Convolutional Networks (GCNs) with Multilayer Perceptrons (MLPs) to detect and classify traffic violations. Through a case study in Montgomery County, Maryland, we demonstrate that TraVio outperforms traditional machine learning baselines in both binary and multi-label classification tasks. The results underscore the critical role of geographic context and time factors in shaping traffic violations. This study not only provides a robust modeling technique for predicting traffic violations but also offers a theoretical foundation that can be extended to other driving-related themes, contributing to safer road environments through informed urban planning and policymaking.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102268"},"PeriodicalIF":7.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A planning support framework to enable smart mobility: Integrating multi-objective spatial optimization and GIS to enhance commuting efficiency","authors":"Reza Mortaheb ,&nbsp;Piotr Jankowski ,&nbsp;Alan Murray","doi":"10.1016/j.compenvurbsys.2025.102269","DOIUrl":"10.1016/j.compenvurbsys.2025.102269","url":null,"abstract":"<div><div>Planning support approaches can play a transformative role in shaping sustainable, resilient, and equitable urban landscapes that promote efficient mobility patterns. This paper develops a prescriptive framework that integrates Geographic Information Systems with a variant of the transportation problem to evaluate planning initiatives and inform land use policies and growth management strategies aimed at enhancing commuting efficiency at both local and regional levels. A multi-step approach is structured including 1) a multi-objective spatial optimization model that simulates the impacts of alterations to urban locational structure on work trip durations, capturing normative commuting patterns across three major workforce groups under varying urban growth scenarios; and 2) a modified gravity model that estimates regional commuting efficiency at the economic-sector level based on optimized inputs. Results indicate that this framework enables a critical evaluation of urban spatial configurations and corresponding commuting efficiency indicators under both conventional and alternative planning systems. The proposed framework also supports tactical and strategic land use and transportation planning, allowing planners and policymakers to analyze potential urban forms across different development scenarios, dissect commuting efficiency outcomes by industry, and identify sectors with critical spatial mismatches between job locations and housing. The ability to guide more balanced urban development and foster more efficient commuting patterns is demonstrated for Central Florida.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102269"},"PeriodicalIF":7.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network","authors":"Dongsheng Chen ,&nbsp;Yu Feng ,&nbsp;Xun Li ,&nbsp;Mingya Qu ,&nbsp;Peng Luo ,&nbsp;Liqiu Meng","doi":"10.1016/j.compenvurbsys.2025.102267","DOIUrl":"10.1016/j.compenvurbsys.2025.102267","url":null,"abstract":"<div><div>Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of <strong>co</strong>re urban <strong>mo</strong>rphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call <strong>CoMo</strong>. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14 %, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R² = 0.721, <em>p</em> &lt; 0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102267"},"PeriodicalIF":7.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}