The cover image is based on the Research Article Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques by Junghee Park et al., https://doi.org/10.1111/mice.13126.
封面图像基于 Junghee Park 等人的研究文章《通过以属性为中心的超采样技术的机器学习算法进行地质声学和地球物理数据驱动的海底沉积物分类》，https://doi.org/10.1111/mice.13126。
{"title":"Cover Image, Volume 39, Issue 14","authors":"","doi":"10.1111/mice.13296","DOIUrl":"https://doi.org/10.1111/mice.13296","url":null,"abstract":"<b>The cover image</b> is based on the Research Article <i>Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques</i> by Junghee Park et al., https://doi.org/10.1111/mice.13126.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141521610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cover image is based on the Research Article Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap by Donglian Gu et al., https://doi.org/10.1111/mice.13287.
{"title":"Cover Image, Volume 39, Issue 14","authors":"","doi":"10.1111/mice.13297","DOIUrl":"https://doi.org/10.1111/mice.13297","url":null,"abstract":"<b>The cover image</b> is based on the Research Article <i>Urban risk assessment model to quantify earthquake-induced elevator passenger entrapment with population heatmap</i> by Donglian Gu et al., https://doi.org/10.1111/mice.13287.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.
动锥阻力（DCR）是最近推出的一种土壤阻力指数，单位为应力。它是根据带仪器的动态锥形透度计顶端的动态响应确定的。然而，DCR 评估通常是一个手动、耗时且容易出错的过程。因此，本研究调查了使用叠加集合（SE）机器学习（ML）模型确定 DCR 的可行性，该模型利用了从动态锥入度测试中获得的信号。两个 ML 实验表明，仅使用力信号就能更准确地预测 DCR。此外，在这两种情况下，SE 技术都优于基础学习算法。总之，研究结果表明，ML 技术可用于利用力和加速度信号自动分析 DCR。
{"title":"Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization","authors":"Samuel Olamide Aregbesola, Yong‐Hoon Byun","doi":"10.1111/mice.13294","DOIUrl":"https://doi.org/10.1111/mice.13294","url":null,"abstract":"Dynamic cone resistance (DCR) is a recently introduced soil resistance index that has the unit of stress. It is determined from the dynamic response at the tip of an instrumented dynamic cone penetrometer. However, DCR evaluation is generally a manual, time‐consuming, and error‐prone process. Thus, this study investigates the feasibility of determining DCR using a stacked ensemble (SE) machine learning (ML) model that utilizes signals obtained from dynamic cone penetration testing. Two ML experiments revealed that using only force signals provides more accurate predictions of DCR. In addition, the SE technique outperformed the base learning algorithms in both cases. Overall, the findings suggest that ML techniques can be used to automate the analysis of DCR with force and acceleration signals.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.
{"title":"Computing‐efficient video analytics for nighttime traffic sensing","authors":"Igor Lashkov, Runze Yuan, Guohui Zhang","doi":"10.1111/mice.13295","DOIUrl":"https://doi.org/10.1111/mice.13295","url":null,"abstract":"The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Honghu Chu, Diran Yu, Weiwei Chen, Jun Ma, Lu Deng
High‐resolution (HR) crack images provide detailed structural assessments crucial for maintenance planning. However, the discrete nature of feature extraction in mainstream deep learning algorithms and computational limitations hinder refined segmentation. This study introduces a rendering‐based lightweight crack segmentation network (RLCSN) designed to efficiently predict refined masks for HR crack images. The RLCSN combines a deep semantic feature extraction architecture—merging Transformer with a super‐resolution boundary‐guided branch—to reduce environmental noise and preserve crack edge details. It also incorporates customized point‐wise refined rendering for training and inference, focusing computational resources on critical areas, and an efficient sparse training method to ensure efficient inference on commercial mobile computing platforms. Each RLCSN's components are validated through ablation studies and field tests, demonstrating its capability to enable unmanned aerial vehicle‐based inspections to detect cracks as narrow as 0.15 mm from a distance of 3 m, thereby enhancing inspection safety and efficiency.
{"title":"A rendering‐based lightweight network for segmentation of high‐resolution crack images","authors":"Honghu Chu, Diran Yu, Weiwei Chen, Jun Ma, Lu Deng","doi":"10.1111/mice.13290","DOIUrl":"https://doi.org/10.1111/mice.13290","url":null,"abstract":"High‐resolution (HR) crack images provide detailed structural assessments crucial for maintenance planning. However, the discrete nature of feature extraction in mainstream deep learning algorithms and computational limitations hinder refined segmentation. This study introduces a rendering‐based lightweight crack segmentation network (RLCSN) designed to efficiently predict refined masks for HR crack images. The RLCSN combines a deep semantic feature extraction architecture—merging Transformer with a super‐resolution boundary‐guided branch—to reduce environmental noise and preserve crack edge details. It also incorporates customized point‐wise refined rendering for training and inference, focusing computational resources on critical areas, and an efficient sparse training method to ensure efficient inference on commercial mobile computing platforms. Each RLCSN's components are validated through ablation studies and field tests, demonstrating its capability to enable unmanned aerial vehicle‐based inspections to detect cracks as narrow as 0.15 mm from a distance of 3 m, thereby enhancing inspection safety and efficiency.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efficient representation of complex infrastructure systems is crucial for system-level management tasks, such as edge prediction, component classification, and decision-making. However, the complex interactions between the infrastructure systems and their spatial environments increased the complexity of network representation learning. This study introduces a novel geometric-based multimodal deep learning model for spatially embedded network representation learning, namely the regional spatial graph convolutional network (RSGCN). The developed RSGCN model simultaneously learns from the node's multimodal spatial features. To evaluate the network representation performance, the introduced RSGCN model is used to embed different infrastructure networks into latent spaces and then reconstruct the networks. A synthetic network dataset, a California Highway Network, and a New Jersey Power Network were used as testbeds. The performance of the developed model is compared with two other state-of-the-art geometric deep learning models, GraphSAGE and Spatial Graph Convolutional Network. The results demonstrate the importance of considering regional information and the effectiveness of using novel graph convolutional neural networks for a more accurate representation of complex infrastructure systems.
{"title":"Modeling of spatially embedded networks via regional spatial graph convolutional networks","authors":"Xudong Fan, Jürgen Hackl","doi":"10.1111/mice.13286","DOIUrl":"https://doi.org/10.1111/mice.13286","url":null,"abstract":"Efficient representation of complex infrastructure systems is crucial for system-level management tasks, such as edge prediction, component classification, and decision-making. However, the complex interactions between the infrastructure systems and their spatial environments increased the complexity of network representation learning. This study introduces a novel geometric-based multimodal deep learning model for spatially embedded network representation learning, namely the <i>regional spatial graph convolutional network</i> (RSGCN). The developed RSGCN model simultaneously learns from the node's multimodal spatial features. To evaluate the network representation performance, the introduced RSGCN model is used to embed different infrastructure networks into latent spaces and then reconstruct the networks. A synthetic network dataset, a California Highway Network, and a New Jersey Power Network were used as testbeds. The performance of the developed model is compared with two other state-of-the-art geometric deep learning models, GraphSAGE and Spatial Graph Convolutional Network. The results demonstrate the importance of considering regional information and the effectiveness of using novel graph convolutional neural networks for a more accurate representation of complex infrastructure systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yin Zhou, Hong Zhang, Xingyi Hu, Jianting Zhou, Jinyu Zhu, Jingzhou Xin, Jun Yang
This study proposes a new method for the rapid and non-contact measurement of cable forces in cable-stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton-based cable force solving method is proposed. Furthermore, terrestrial laser scanning technology is used to collect geometric data of the cables. Meanwhile, automatic segmentation, noise reduction, and centerline extraction algorithms for the cable point cloud are proposed to accurately and efficiently obtain the cable shape. The correctness of the proposed cable force calculation method is verified in a well-designed experiment, with the measurement error of cable forces for 15 test samples being less than 1%. Finally, the proposed point cloud automation processing algorithm and cable force measurement method are fully tested on a cable-stayed bridge with a span of 460 m. The measurement accuracy of the proposed method for actual bridge cable tension is comparable to that of the frequency method, but the detection efficiency on site is nine times higher than that of the traditional frequency method. Overall, this study provides a new measurement method for construction control, health monitoring, intelligent detection, and other fields of cable-stayed bridges.
{"title":"Rapid measurement method for cable tension of cable-stayed bridges using terrestrial laser scanning","authors":"Yin Zhou, Hong Zhang, Xingyi Hu, Jianting Zhou, Jinyu Zhu, Jingzhou Xin, Jun Yang","doi":"10.1111/mice.13288","DOIUrl":"https://doi.org/10.1111/mice.13288","url":null,"abstract":"This study proposes a new method for the rapid and non-contact measurement of cable forces in cable-stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton-based cable force solving method is proposed. Furthermore, terrestrial laser scanning technology is used to collect geometric data of the cables. Meanwhile, automatic segmentation, noise reduction, and centerline extraction algorithms for the cable point cloud are proposed to accurately and efficiently obtain the cable shape. The correctness of the proposed cable force calculation method is verified in a well-designed experiment, with the measurement error of cable forces for 15 test samples being less than 1%. Finally, the proposed point cloud automation processing algorithm and cable force measurement method are fully tested on a cable-stayed bridge with a span of 460 m. The measurement accuracy of the proposed method for actual bridge cable tension is comparable to that of the frequency method, but the detection efficiency on site is nine times higher than that of the traditional frequency method. Overall, this study provides a new measurement method for construction control, health monitoring, intelligent detection, and other fields of cable-stayed bridges.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi-line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.
{"title":"Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment","authors":"Chengcheng Yang, Sheng Jin, Wenbin Yao, Donglei Rong, Congcong Bai, Jérémie Adjé Alagbé","doi":"10.1111/mice.13289","DOIUrl":"https://doi.org/10.1111/mice.13289","url":null,"abstract":"In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi-line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141334638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision-making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum-enhanced ML (QML), a rapidly advancing field, offers greater advantages over classical ML (CML) for large-scale data, enhancing the speed and accuracy of damage assessments. This study explores the viability of leveraging QML to evaluate the safety of reinforced concrete buildings after earthquakes, focusing on classification accuracy only. A QML algorithm is trained using simulation datasets and tested on real-world damaged datasets, with its performance compared to various CML algorithms. The classification results demonstrate the potential of QML to revolutionize seismic damage assessments, offering a promising direction for future research and practical applications.
{"title":"Quantum-enhanced machine learning technique for rapid post-earthquake assessment of building safety","authors":"Sanjeev Bhatta, Ji Dang","doi":"10.1111/mice.13291","DOIUrl":"https://doi.org/10.1111/mice.13291","url":null,"abstract":"Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision-making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum-enhanced ML (QML), a rapidly advancing field, offers greater advantages over classical ML (CML) for large-scale data, enhancing the speed and accuracy of damage assessments. This study explores the viability of leveraging QML to evaluate the safety of reinforced concrete buildings after earthquakes, focusing on classification accuracy only. A QML algorithm is trained using simulation datasets and tested on real-world damaged datasets, with its performance compared to various CML algorithms. The classification results demonstrate the potential of QML to revolutionize seismic damage assessments, offering a promising direction for future research and practical applications.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141299158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the “Pseudo-PFLOW” dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo-PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo-PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision-making and analysis.
{"title":"Nationwide synthetic human mobility dataset construction from limited travel surveys and open data","authors":"Takehiro Kashiyama, Yanbo Pang, Yuya Shibuya, Takahiro Yabe, Yoshihide Sekimoto","doi":"10.1111/mice.13285","DOIUrl":"https://doi.org/10.1111/mice.13285","url":null,"abstract":"In recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the “Pseudo-PFLOW” dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo-PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo-PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision-making and analysis.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":11.775,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141299166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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