Retrofitting building designs is crucial given the global aging infrastructure and increased in frequency of natural hazards like earthquakes. While traditional data-driven models are widely used for predicting building conditions, there has been limited exploration of recent artificial intelligence (AI) techniques in structural design. This study introduces a novel explainable AI framework that utilizes data-driven models for assessing, designing, and retrofitting of structures. The framework highlights the key global features of the model and further investigates them locally to adjust the input design parameters. It suggests the necessary changes in these inputs to achieve the desired structural performance. To achieve this, the framework employs interpretability techniques such as feature importance, feature interactions, Shapley Additive exPlanations, local interpretable model-agnostic explanations, partial dependence plot (PDP), and individual conditional expectation to highlight the important features. Additionally, a novel counterfactual) technique is applied for the first time as a design tool in seismic assessment and retrofitting of structures. The effectiveness of this framework is validated on a real benchmark structure through nonlinear time history analysis and natural earthquakes. The results show that the proposed framework is highly effective, especially under design-level earthquake conditions in achieving the necessary change in stiffness and strength of structures to meet the required seismic design objectives across different earthquake scenarios. This framework holds promise for wider adoption and applications in various other structural and civil engineering domains.
{"title":"Data-driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence","authors":"Khurram Shabbir, Mohamed Noureldin, Sung-Han Sim","doi":"10.1111/mice.13338","DOIUrl":"https://doi.org/10.1111/mice.13338","url":null,"abstract":"Retrofitting building designs is crucial given the global aging infrastructure and increased in frequency of natural hazards like earthquakes. While traditional data-driven models are widely used for predicting building conditions, there has been limited exploration of recent artificial intelligence (AI) techniques in structural design. This study introduces a novel explainable AI framework that utilizes data-driven models for assessing, designing, and retrofitting of structures. The framework highlights the key global features of the model and further investigates them locally to adjust the input design parameters. It suggests the necessary changes in these inputs to achieve the desired structural performance. To achieve this, the framework employs interpretability techniques such as feature importance, feature interactions, Shapley Additive exPlanations, local interpretable model-agnostic explanations, partial dependence plot (PDP), and individual conditional expectation to highlight the important features. Additionally, a novel counterfactual) technique is applied for the first time as a design tool in seismic assessment and retrofitting of structures. The effectiveness of this framework is validated on a real benchmark structure through nonlinear time history analysis and natural earthquakes. The results show that the proposed framework is highly effective, especially under design-level earthquake conditions in achieving the necessary change in stiffness and strength of structures to meet the required seismic design objectives across different earthquake scenarios. This framework holds promise for wider adoption and applications in various other structural and civil engineering domains.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"1 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235504","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}
Virtual reality (VR) technology is increasingly vital in various sectors, particularly for simulating real environments in training and teleoperation. However, it has primarily focused on static, controlled settings like indoor industrial shopfloors. This paper proposes a novel method for remotely controlling robots in hazardous environments safely, without compromising efficiency. Operators can execute tasks from remote locations ensuring continuity regardless of distance. Real-time efficiency is achieved by updating the virtual environment from on-site sensors and mirroring the real environment, utilizing 3D reconstruction, Google Images, and video streams. Communication between VR and the remote robot is facilitated through a remote robot operating system connection. The efficacy of this concept will be validated through real road maintenance interventions.
{"title":"Virtual reality-based dynamic scene recreation and robot teleoperation for hazardous environments","authors":"Angelos Christos Bavelos, Efthymios Anastasiou, Nikos Dimitropoulos, George Michalos, Sotiris Makris","doi":"10.1111/mice.13337","DOIUrl":"https://doi.org/10.1111/mice.13337","url":null,"abstract":"Virtual reality (VR) technology is increasingly vital in various sectors, particularly for simulating real environments in training and teleoperation. However, it has primarily focused on static, controlled settings like indoor industrial shopfloors. This paper proposes a novel method for remotely controlling robots in hazardous environments safely, without compromising efficiency. Operators can execute tasks from remote locations ensuring continuity regardless of distance. Real-time efficiency is achieved by updating the virtual environment from on-site sensors and mirroring the real environment, utilizing 3D reconstruction, Google Images, and video streams. Communication between VR and the remote robot is facilitated through a remote robot operating system connection. The efficacy of this concept will be validated through real road maintenance interventions.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"208 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198116","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 Article A multiscale model for wood combustion by H. L. Hao et al., https://doi.org/10.1111/mice.13187.
封面图片来自 H. L. Hao 等人撰写的文章《木材燃烧的多尺度模型》，https://doi.org/10.1111/mice.13187。
{"title":"Cover Image, Volume 39, Issue 19","authors":"","doi":"10.1111/mice.13341","DOIUrl":"10.1111/mice.13341","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>A multiscale model for wood combustion</i> by H. L. Hao et al., https://doi.org/10.1111/mice.13187.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 19","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Wang, Hong Xiao, Zhihai Zhang, Xiaoxuan Guo, Qiang Liu
The noise within train is a paradox; while harmful to passenger health, it is useful to operators as it provides insights into the working status of vehicles and tracks. Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key information and structure of the data. To provide foundational data for track fault detection, a representation learning framework for interior noise, named the interior noise representation framework, is introduced. The method includes: (i) using wavelet transform to represent the original noise signal and designing a soft and hard denoising module for dataset denoising; (ii) deep residual convolutional denoising variational autoencoder (VAE) module performs representation learning with a VAE and deep residual convolutional neural networks, enabling richer data augmentation for sparsely labeled samples by manipulating the embedding space; (iii) deep embedding clustering submodule balances the representation of reconstruction and clustering features through the joint optimization of these aspects, categorizing metro noise into three distinct classes and effectively discriminating significantly different features. The experimental results show that, compared to traditional mechanism‐based models for characterizing interior noise, this approach offers a data‐driven general analysis framework, providing a foundational model for downstream tasks.
{"title":"Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering","authors":"Yang Wang, Hong Xiao, Zhihai Zhang, Xiaoxuan Guo, Qiang Liu","doi":"10.1111/mice.13336","DOIUrl":"https://doi.org/10.1111/mice.13336","url":null,"abstract":"The noise within train is a paradox; while harmful to passenger health, it is useful to operators as it provides insights into the working status of vehicles and tracks. Recently, methods for identifying defects based on interior noise signals are emerging, among which representation learning is the foundation for deep neural network models to understand the key information and structure of the data. To provide foundational data for track fault detection, a representation learning framework for interior noise, named the interior noise representation framework, is introduced. The method includes: (i) using wavelet transform to represent the original noise signal and designing a soft and hard denoising module for dataset denoising; (ii) deep residual convolutional denoising variational autoencoder (VAE) module performs representation learning with a VAE and deep residual convolutional neural networks, enabling richer data augmentation for sparsely labeled samples by manipulating the embedding space; (iii) deep embedding clustering submodule balances the representation of reconstruction and clustering features through the joint optimization of these aspects, categorizing metro noise into three distinct classes and effectively discriminating significantly different features. The experimental results show that, compared to traditional mechanism‐based models for characterizing interior noise, this approach offers a data‐driven general analysis framework, providing a foundational model for downstream tasks.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"22 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160481","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}
This paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.
{"title":"A computer vision–aided methodology for bridge flexibility identification from ambient vibrations","authors":"Yuyao Cheng, Siqi Jia, Jianliang Zhang, Jian Zhang","doi":"10.1111/mice.13329","DOIUrl":"https://doi.org/10.1111/mice.13329","url":null,"abstract":"This paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"17 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142131013","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}
Recent studies in dam displacement monitoring primarily focus on single‐response monitoring or model updating using advanced techniques. Few studies involve the combination analysis of displacement with other synchronized responses utilizing their monitoring characteristics. In situ strain data provide a strength‐safety perspective for dam displacement monitoring. The challenge lies in that estimating displacement directly using limited discrete strain data may be misleading. This paper analyzes the relationship between displacement and global, and multipoint local strains from the perspective of the differences in load effects of gravity dams, and indicates that introducing appropriate state factors improves the estimation. A displacement estimation model driven by strain data and state factors is developed using stacked convolutional neural network, and the variable relationships within the model are interpretated via accumulated local effects. Incorporating specific strength criteria, a novel displacement monitoring indicator based on the tensile safety of the dam heel is proposed. A case study of a gravity dam showcases the effectiveness of the proposed approach in comparison with the solely strain‐based model and the traditional hydrostatic‐seasonal‐time factors‐based model.
{"title":"Gravity dam displacement monitoring using in situ strain and deep learning","authors":"Xin Wu, Dongjian Zheng, Xingqiao Chen, Yongtao Liu, Jianchun Qiu, Haifeng Jiang","doi":"10.1111/mice.13333","DOIUrl":"https://doi.org/10.1111/mice.13333","url":null,"abstract":"Recent studies in dam displacement monitoring primarily focus on single‐response monitoring or model updating using advanced techniques. Few studies involve the combination analysis of displacement with other synchronized responses utilizing their monitoring characteristics. In situ strain data provide a strength‐safety perspective for dam displacement monitoring. The challenge lies in that estimating displacement directly using limited discrete strain data may be misleading. This paper analyzes the relationship between displacement and global, and multipoint local strains from the perspective of the differences in load effects of gravity dams, and indicates that introducing appropriate state factors improves the estimation. A displacement estimation model driven by strain data and state factors is developed using stacked convolutional neural network, and the variable relationships within the model are interpretated via accumulated local effects. Incorporating specific strength criteria, a novel displacement monitoring indicator based on the tensile safety of the dam heel is proposed. A case study of a gravity dam showcases the effectiveness of the proposed approach in comparison with the solely strain‐based model and the traditional hydrostatic‐seasonal‐time factors‐based model.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"100 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117977","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 Article A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture by Shuangzhi Yu et al., https://doi.org/10.1111/mice.13179.
封面图像基于余双志等人撰写的文章《一种基于时空数据挖掘的浮动车数据交通状态预测方法》（A Traffic State prediction method based on spatial-temporal data mining of floating car data by using autoformer architecture），https://doi.org/10.1111/mice.13179。
{"title":"Cover Image, Volume 39, Issue 18","authors":"","doi":"10.1111/mice.13335","DOIUrl":"10.1111/mice.13335","url":null,"abstract":"<p><b>The cover image</b> is based on the Article <i>A traffic state prediction method based on spatial–temporal data mining of floating car data by using autoformer architecture</i> by Shuangzhi Yu et al., https://doi.org/10.1111/mice.13179.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure>\u0000 </p>","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"39 18","pages":""},"PeriodicalIF":8.5,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/mice.13335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142118224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial causality in urban traffic networks explores how events or conditions in one location affect those in another. Unveiling congestion spatial causality is crucial for identifying congestion‐inducing bottlenecks in traffic networks and offering valuable insights for traffic network management and control. This study introduces the traffic‐convergent‐cross‐mapping (T‐CCM) method, a state‐space‐reconstruction approach from the dynamic system perspective, to identify causality among roads within urban traffic networks using time series data. Simultaneously, it effectively addresses the intricate challenges of uncertainty and interdependency among sensors caused by traffic flow dynamics. Empirical findings from real‐world (PeMS‐Bay area) traffic speed data validate the effectiveness of the T‐CCM method in detecting causality. This study reveals bidirectional causal effects between downstream and upstream roads in short‐term congestion generation and dissipation periods, which can pinpoint congestion origins and inform quick traffic management response. Furthermore, it elucidates the long‐term causality impacts between distant roads, particularly with regard to traveler choices and road land use attributes, guiding infrastructure investment and public transit improvements.
{"title":"A convergent cross‐mapping approach for unveiling congestion spatial causality in urban traffic networks","authors":"Jiannan Mao, Hao Huang, Yu Gu, Weike Lu, Tianli Tang, Fan Ding","doi":"10.1111/mice.13334","DOIUrl":"https://doi.org/10.1111/mice.13334","url":null,"abstract":"Spatial causality in urban traffic networks explores how events or conditions in one location affect those in another. Unveiling congestion spatial causality is crucial for identifying congestion‐inducing bottlenecks in traffic networks and offering valuable insights for traffic network management and control. This study introduces the traffic‐convergent‐cross‐mapping (T‐CCM) method, a state‐space‐reconstruction approach from the dynamic system perspective, to identify causality among roads within urban traffic networks using time series data. Simultaneously, it effectively addresses the intricate challenges of uncertainty and interdependency among sensors caused by traffic flow dynamics. Empirical findings from real‐world (PeMS‐Bay area) traffic speed data validate the effectiveness of the T‐CCM method in detecting causality. This study reveals bidirectional causal effects between downstream and upstream roads in short‐term congestion generation and dissipation periods, which can pinpoint congestion origins and inform quick traffic management response. Furthermore, it elucidates the long‐term causality impacts between distant roads, particularly with regard to traveler choices and road land use attributes, guiding infrastructure investment and public transit improvements.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"99 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101192","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 reliance of contractor selection for specific construction activities on subjective judgments remains a complex decision‐making process with high stakes due to its impact on project success. Existing methods of contractor selection lack a data‐driven decision‐support approach, leading to suboptimal contractor assignments. Here, an advanced node2vec‐based recommendation system is proposed that addresses the shortcomings of conventional contractor selection by incorporating a broad range of quantitative performance indicators. This study utilizes semi‐supervised machine learning to analyze contractor records, creating a network in which nodes represent activities and weighted edges correspond to contractors and their performances, particularly cost and schedule performance indicators. Node2vec is found to display a prediction accuracy of 88.16% and 84.08% when processing cost and schedule performance rating networks, respectively. The novelty of this research lies in its proposed network‐based, multi‐criteria decision‐making method for ranking construction contractors using embedding information obtained from quantitative contractor performance data and processed by the node2vec procedure, along with the measurement of cosine similarity between contractors and the ideal as related to a given activity.
{"title":"Performance‐driven contractor recommendation system using a weighted activity–contractor network","authors":"Fatemeh Mostofi, Onur Behzat Tokdemir, Ümit Bahadır, Vedat Toğan","doi":"10.1111/mice.13332","DOIUrl":"https://doi.org/10.1111/mice.13332","url":null,"abstract":"The reliance of contractor selection for specific construction activities on subjective judgments remains a complex decision‐making process with high stakes due to its impact on project success. Existing methods of contractor selection lack a data‐driven decision‐support approach, leading to suboptimal contractor assignments. Here, an advanced node2vec‐based recommendation system is proposed that addresses the shortcomings of conventional contractor selection by incorporating a broad range of quantitative performance indicators. This study utilizes semi‐supervised machine learning to analyze contractor records, creating a network in which nodes represent activities and weighted edges correspond to contractors and their performances, particularly cost and schedule performance indicators. Node2vec is found to display a prediction accuracy of 88.16% and 84.08% when processing cost and schedule performance rating networks, respectively. The novelty of this research lies in its proposed network‐based, multi‐criteria decision‐making method for ranking construction contractors using embedding information obtained from quantitative contractor performance data and processed by the node2vec procedure, along with the measurement of cosine similarity between contractors and the ideal as related to a given activity.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"8 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101193","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}
Jiandong Qiu, Sicheng Fu, Jushang Ou, Kai Tang, Xinming Qu, Shixiao Liang, Xin Wang, Bin Ran
In modern transportation systems, network‐wide traffic flow estimation is crucial for informed decision making, strategic infrastructure planning, and effective traffic management. While the limited availability of observed road‐segment traffic flow data presents a significant challenge, the emerging collection of Global Navigation Satellite System (GNSS) speed data across the entire network provides an alternative method for estimating the missing traffic flow information. To this end, this paper introduces a novel approach to estimating network‐wide road‐segment traffic flow. This approach takes advantage of the abundantly available GNSS speed data, coupled with only sparsely observed traffic flow samples. By integrating the principles of dynamic traffic assignment models with sparse recovery techniques, we formulate the problem of traffic flow estimation as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization task. The efficacy and practical applicability of our proposed method are validated through evaluations using both hypothetical and real‐world case studies. The experimental findings exhibit a close alignment between the estimated and ground‐truth link flows across different time periods. Additionally, the method consistently produces low mean estimation errors for the majority of road segments, underlining the potential for our approach in effectively managing traffic flow estimation for large‐scale road networks, particularly in situations characterized by data scarcity.
{"title":"Estimating link flow through link speed with sparse flow data sampling","authors":"Jiandong Qiu, Sicheng Fu, Jushang Ou, Kai Tang, Xinming Qu, Shixiao Liang, Xin Wang, Bin Ran","doi":"10.1111/mice.13323","DOIUrl":"https://doi.org/10.1111/mice.13323","url":null,"abstract":"In modern transportation systems, network‐wide traffic flow estimation is crucial for informed decision making, strategic infrastructure planning, and effective traffic management. While the limited availability of observed road‐segment traffic flow data presents a significant challenge, the emerging collection of Global Navigation Satellite System (GNSS) speed data across the entire network provides an alternative method for estimating the missing traffic flow information. To this end, this paper introduces a novel approach to estimating network‐wide road‐segment traffic flow. This approach takes advantage of the abundantly available GNSS speed data, coupled with only sparsely observed traffic flow samples. By integrating the principles of dynamic traffic assignment models with sparse recovery techniques, we formulate the problem of traffic flow estimation as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization task. The efficacy and practical applicability of our proposed method are validated through evaluations using both hypothetical and real‐world case studies. The experimental findings exhibit a close alignment between the estimated and ground‐truth link flows across different time periods. Additionally, the method consistently produces low mean estimation errors for the majority of road segments, underlining the potential for our approach in effectively managing traffic flow estimation for large‐scale road networks, particularly in situations characterized by data scarcity.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"19 1","pages":""},"PeriodicalIF":11.775,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101217","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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