IEEE Transactions on Intelligent Transportation Systems最新文献_第9页

Spatiotemporal Generalization Graph Neural Network-Based Prediction Models by Considering Morphological Diversity in Traffic Networks 考虑交通网络形态多样性的时空概化图神经网络预测模型

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-16 DOI: 10.1109/TITS.2025.3557427

Limei Liu;Peibo Duan;Zhuo Chen;Jinghui Zhang;Siyuan Feng;Wenwei Yue;Yibo Wang;Jia Rong

{"title":"Spatiotemporal Generalization Graph Neural Network-Based Prediction Models by Considering Morphological Diversity in Traffic Networks","authors":"Limei Liu;Peibo Duan;Zhuo Chen;Jinghui Zhang;Siyuan Feng;Wenwei Yue;Yibo Wang;Jia Rong","doi":"10.1109/TITS.2025.3557427","DOIUrl":"https://doi.org/10.1109/TITS.2025.3557427","url":null,"abstract":"The morphological diversity, referring to the variations in traffic network topologies defined in this paper, often emerges and brings difficulties in successfully transferring a pre-trained prediction model from one traffic network to another. Moreover, most existing research primarily assumes that traffic data in source and target networks follow independent and identically distributed (i.i.d.) patterns, which is usually not consistent with real-world situations, particularly when considering morphological diversity. For this inconsistency, many efforts have been made, but they mainly concentrate on temporal aspects, which significantly differ from traffic prediction due to spatial and temporal correlations among road segments, influenced by variations in road topology and traffic behavior. This paper introduces a causality-based spatiotemporal out-of-distribution (OOD) generalization method, which is adaptable to most GNNs for diverse, large-scale, dynamic traffic systems with zero-shot. Furthermore, to enhance the generalization and adaptability of the proposed method, we introduce graph matching and equal-sized graph partitioning to alleviate spatial shift between the source and target traffic networks, reduce and align the scale of the networks. Experiments carried out on traffic flow datasets demonstrate that our method significantly improves the performance of various GNN-based traffic predictors in the situation of morphological diversity, achieving a maximum reduction in MAE of 33.08%. Compared to other OOD-driven baselines, our approach also shows a notable improvement, with up to a 40.58% decrease in MAE.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9993-10007"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536298","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}

引用次数: 0

Evaluating Crowd Flow Forecasting Algorithms for Indoor Pedestrian Spaces: A Benchmark Using a Synthetic Dataset 评估室内行人空间的人群流量预测算法：使用合成数据集的基准

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-16 DOI: 10.1109/TITS.2025.3559353

Weiming Mai;Dorine Duives;Panchamy Krishnakumari;Serge Hoogendoorn

{"title":"Evaluating Crowd Flow Forecasting Algorithms for Indoor Pedestrian Spaces: A Benchmark Using a Synthetic Dataset","authors":"Weiming Mai;Dorine Duives;Panchamy Krishnakumari;Serge Hoogendoorn","doi":"10.1109/TITS.2025.3559353","DOIUrl":"https://doi.org/10.1109/TITS.2025.3559353","url":null,"abstract":"Crowd management plays a vital role in urban planning and emergency response. Accurate crowd prediction is important for venue operators to respond effectively to adverse crowd dynamics during large gatherings. Although many studies have tried to predict crowd densities or movement dynamics with data-driven predictive models, their validation is often limited to data within the same scenario. As a result, the predictability of the data-driven model in unseen scenarios, such as evacuation scenarios, remains unknown due to the challenges of collecting out-of-distribution data regarding emergency conditions. To address this problem, we present an evaluation pipeline to evaluate different kinds of data-driven models. A method is proposed to generate realistic scenarios by simulation and collect synthetic data from these scenarios to acquire a comprehensive dataset. With these synthetic data, we evaluated different predictive models, from traditional machine learning methods to deep learning time-series prediction models, to explore their generalizability. Furthermore, we propose a weighted average metric, which is better suited to determine the performance of forecasting algorithms under adverse conditions. Through extensive experimentation, we showcase the heterogeneity and diversity of the simulation dataset. The evaluation results also revealed that all the data-driven models performed poorly in unseen scenarios, highlighting the urgent need to develop a robust and generalizable model for predicting crowd flow in indoor spaces.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7953-7968"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206173","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}

引用次数: 0

MtpNet: Multi-Task Panoptic Driving Perception Network MtpNet：多任务全景驾驶感知网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-16 DOI: 10.1109/TITS.2025.3558467

Zheng Li;Xiaohui Yuan;Bifan Sun;Yuting Xia;Tingting Jiang;Chao Wang;Wentao Ma;Shuai Yang;Siyuan Liu;Lichuan Gu

{"title":"MtpNet: Multi-Task Panoptic Driving Perception Network","authors":"Zheng Li;Xiaohui Yuan;Bifan Sun;Yuting Xia;Tingting Jiang;Chao Wang;Wentao Ma;Shuai Yang;Siyuan Liu;Lichuan Gu","doi":"10.1109/TITS.2025.3558467","DOIUrl":"https://doi.org/10.1109/TITS.2025.3558467","url":null,"abstract":"Panoramic driving systems are crucial for autonomous driving but face challenges in real-time performance and reliability. This paper proposes an end-to-end, multi-tasking MtpNet that reduces latency and enhances detection accuracy. The convolution was upgraded using the Efficient Layer Aggregation Network, and precise multi-task loss functions and more effective training strategies were devised. Our results demonstrate improved performance in small object detection, partial occlusion handling, and drivable area segmentation. The recall of the traffic object detection is 1.3% higher than that of the state-of-the-art model, reaching 94.1%, the mAP50 is 6.4% higher, reaching 89.8%, and the mIoU of the drivable area segmentation is 2.7% higher, reaching 95.9%. Additionally, the accuracy of lane detection reached 88.7%. The visual comparison using three datasets TuSimple, CityScapes, and CULane demonstrates that MtpNet has good detection segmentation and strong robustness under various conditions. Codes are available at <uri>https://github.com/ErLinErYi/mtpnet</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7600-7609"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196609","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}

引用次数: 0

End-Point Drive and Reverse Enhanced Decoding- Based Traffic Participants Trajectory Prediction Under Bird’s Eye View 基于端点驱动和反向增强解码的鸟瞰交通参与者轨迹预测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-16 DOI: 10.1109/TITS.2025.3554714

Chunsheng Liu;Jincan Xie;Faliang Chang;Tuo Li;Shuang Li;Yinhai Wang

{"title":"End-Point Drive and Reverse Enhanced Decoding- Based Traffic Participants Trajectory Prediction Under Bird’s Eye View","authors":"Chunsheng Liu;Jincan Xie;Faliang Chang;Tuo Li;Shuang Li;Yinhai Wang","doi":"10.1109/TITS.2025.3554714","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554714","url":null,"abstract":"Trajectory prediction under Bird’s Eye View (BEV) refers to predicting the movement intention of agents based on the historical observation trajectories, which is of great significance for autonomous driving, driving safety and social navigation. The traffic prediction trajectory is multimodality with multiple reasonable trajectories and various prediction time, which often suffer complex agents interaction, cumulative errors and a large number of agents. To overcome these problems, we explore the BEV-based traffic participants trajectory prediction problem and propose the novel End-point Drive and Reverse Enhanced Decoding Network (EDRED-TPNet), based on the mechanisms of end-point driving and reverse enhanced decoding. Firstly, the Encoder based on Dynamic Spatio-temporal Graph and Multimodality Coding Fusion (ST-MC-Encoder) are constructed to effectively represent complex traffic scenario with changeable agents, encode social interactions with historical trajectories, social interaction, future trajectory and multimodality. Secondly, the End-Point Drive Module is proposed to predict the end point before predicting the complete trajectory, thus providing more accurate trajectory prediction; Lastly, to further improve the long-term prediction performance, the Reverse Enhanced Decoder (RE-Decoder) is proposed to fuse forward and reverse hidden state vectors to obtain diverse trajectories that conform to physical and social acceptability rules. We build the first AAV-captured Trajectory Prediction Dataset (UTP-Dataset) for traffic participants trajectory prediction. Experimental results show that the proposed methods can fulfill the multi-target trajectory prediction task in complex traffic scenarios and achieve high performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7792-7806"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206193","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}

引用次数: 0

Less-Conservative Robust Path Tracking Control With Intrinsic Bump-Free Feature for Autonomous Vehicles: A Sub-Polytope Integrated Approach 具有无碰撞特征的自动驾驶汽车非保守鲁棒路径跟踪控制：一种子多面体集成方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-16 DOI: 10.1109/TITS.2025.3554157

Liqin Zhang;Manjiang Hu;Yougang Bian;Hui Zhang;Anh-Tu Nguyen

{"title":"Less-Conservative Robust Path Tracking Control With Intrinsic Bump-Free Feature for Autonomous Vehicles: A Sub-Polytope Integrated Approach","authors":"Liqin Zhang;Manjiang Hu;Yougang Bian;Hui Zhang;Anh-Tu Nguyen","doi":"10.1109/TITS.2025.3554157","DOIUrl":"https://doi.org/10.1109/TITS.2025.3554157","url":null,"abstract":"This paper proposes a novel sub-polytope integrated approach (sPIA) that features an intrinsic bump-free transition, aiming to reduce conservatism in the design of path tracking control for autonomous vehicles with large range time-varying longitudinal velocity. The approach encapsulates the interdependent time-varying parameters associated with longitudinal velocity as a set of finite-vertex sub-polytopes interconnected via junction points, thereby reducing the conservatism induced by modeling overbounding. The integration of junction points and the formulation of sub-region activation rules provide a theoretical foundation for avoiding abrupt changes in feedback gains, ensuring a bump-free transition between sub-regions. A gain-scheduling state feedback controller is designed, employing parameter-dependent Lyapunov functions to further attenuate design conservatism. The effectiveness of the proposed method in reducing design conservatism is demonstrated by a comparative analysis of the optimal <inline-formula> <tex-math>${mathcal {H}}_{infty }$ </tex-math></inline-formula> performance indices across various sub-polytope integration schemes. Furthermore, the superiority of the method is exemplified via simulations within real-world driving scenarios, utilizing the high-fidelity CarSim-Simulink platform. The results indicate that the proposed sPIA outperforms traditional polytopic methods in path tracking performance. This improvement, together with the effective avoidance of bumps during sub-regional transitions, confirms the efficacy of the proposed approach. Moreover, the real-time performance of the method is verified by hardware-in-the-loop experiments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10428-10442"},"PeriodicalIF":7.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536439","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}

引用次数: 0

Truck Parking Usage Prediction With Decomposed Graph Neural Networks 基于分解图神经网络的货车停车预测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3556229

Rei Tamaru;Yang Cheng;Steven T. Parker;Ernie Perry;Bin Ran;Soyoung Ahn

{"title":"Truck Parking Usage Prediction With Decomposed Graph Neural Networks","authors":"Rei Tamaru;Yang Cheng;Steven T. Parker;Ernie Perry;Bin Ran;Soyoung Ahn","doi":"10.1109/TITS.2025.3556229","DOIUrl":"https://doi.org/10.1109/TITS.2025.3556229","url":null,"abstract":"Truck parking on freight corridors faces the major challenge of insufficient parking spaces. This is exacerbated by the Hour-of-Service (HOS) regulations, which often result in unauthorized parking practices, causing safety concerns. It has been shown that providing accurate parking usage prediction can be a cost-effective solution to reduce unsafe parking practices. In light of this, existing studies have developed various methods to predict the usage of a truck parking site and have demonstrated satisfactory accuracy. However, these studies focused on a single parking site, and few approaches have been proposed to predict the usage of multiple truck parking sites considering spatio-temporal dependencies, due to the lack of data. This paper aims to fill this gap and presents the Regional Temporal Graph Convolutional Network (RegT-GCN) to predict parking usage across the entire state to provide more comprehensive truck parking information. The framework leverages the topological structures of truck parking site locations and historical parking data to predict the occupancy rate considering spatio-temporal dependencies across a state. To achieve this, we introduce a Regional Decomposition approach, which effectively captures the geographical characteristics of the truck parking locations and their spatial correlations. Evaluation results demonstrate that the proposed model outperforms other baseline models, showing the effectiveness of our regional decomposition. The code is available at <uri>https://github.com/raynbowy23/RegT-GCN</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7690-7699"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205813","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}

引用次数: 0

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3556803

Shan Cao;Chunyue Song;Jie Zhang;Xiangrui Zhang

{"title":"Road Network Similarity-Based Transfer Learning Method for Traffic Volume Estimation in Undetected Road Segments","authors":"Shan Cao;Chunyue Song;Jie Zhang;Xiangrui Zhang","doi":"10.1109/TITS.2025.3556803","DOIUrl":"https://doi.org/10.1109/TITS.2025.3556803","url":null,"abstract":"Estimating traffic state, particularly traffic volume, is crucial in Intelligent Transportation Systems (ITS). Due to the absence or malfunction of detectors, some road segments are undetected, leading to a complete absence of volume data and thereby weakening the traffic monitoring capability of ITS. The existing estimation methods are either inapplicable to this scenario or yield poor results due to a lack of available data, which will compromise the traffic monitoring capability of ITS. To handle it, this work proposes a novel Road Network Similarity-based Transfer Learning method (RNS-TL) for real-time traffic estimation. Firstly, the Small-scale Road Network Similarity Evaluation Module (SSEM) is initially proposed which aims to identify the most similar road segments and their small-scale road networks for the undetected segments, serving as the source domain for transfer learning. Then, based on SSEM, a transfer learning framework is proposed where a traffic estimation model trained on the source domain is fine-tuned for the target undetected road segment. Finally, the results from two real-world traffic cases show that the estimation errors, MAE and RMSE, for the proposed method are 7.813 and 6.383, and 10.689 and 8.892, respectively, outperforming all comparison methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7700-7714"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206187","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}

引用次数: 0

A Highly Transferable Camouflage Attack Against Object Detectors in the Physical World 物理世界中针对目标探测器的高度可转移伪装攻击

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3553847

Yizhou Wang;Libing Wu;Yue Cao;Jiong Jin;Zhuangzhuang Zhang;Enshu Wang;Chao Ma;Yu Zhao

{"title":"A Highly Transferable Camouflage Attack Against Object Detectors in the Physical World","authors":"Yizhou Wang;Libing Wu;Yue Cao;Jiong Jin;Zhuangzhuang Zhang;Enshu Wang;Chao Ma;Yu Zhao","doi":"10.1109/TITS.2025.3553847","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553847","url":null,"abstract":"To assess the vulnerability of deep neural networks in the physical world, many studies have introduced adversarial examples and applied them to computer vision tasks such as object detection in recent years. Compared to patch-based adversarial attacks, camouflage-based attacks have received more and more attention due to their ability to attack detectors from multiple viewpoints. However, existing adversarial examples often rely on glass-box models and exhibit limited transferability to closed-box models, which remains a significant challenge. To address this issue, we propose the highly transferable camouflage attack, a novel physical adversarial attack framework designed to generate robust and efficient adversarial camouflage that can mislead object detectors in diverse scenarios. Specifically, we introduce a distraction method to distribute the features of the attention map between models, and propose enhanced transfer strategies to improve adversarial transferability through augmenting the input data and the attacked models. Extensive experiments demonstrate that our highly transferable camouflage attack can effectively mislead object detectors in both digital and physical worlds, enhancing the transferability of adversarial camouflage on multiple mainstream detectors.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"10373-10385"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536410","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}

引用次数: 0

Improving Surface Defect Detection for Trains Based on Visual-Language Knowledge Guidance on Tiny Datasets 基于小数据集的视觉语言知识引导改进列车表面缺陷检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3532731

Kaiyan Lei;Zhiquan Qi;Jin Song

{"title":"Improving Surface Defect Detection for Trains Based on Visual-Language Knowledge Guidance on Tiny Datasets","authors":"Kaiyan Lei;Zhiquan Qi;Jin Song","doi":"10.1109/TITS.2025.3532731","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532731","url":null,"abstract":"Efficient and accurate detection of surface defects on trains is crucial for ensuring train safety. However, the insufficient defect samples and their diverse patterns make defect detection in complex environments highly challenging. This paper proposes a novel train surface defect detection model (ViLG) via visual-language knowledge guidance. By leveraging broad semantic knowledge of CLIP, the model compensates for the insufficient defect semantics in tiny datasets and enhances the ability to recognize unseen defects. First, we propose Visual Feature Guidance with CLIP, which enriches and enhances the global representation capabilities of backbone while preserving its self-learning ability for visual representations. This improves semantic understanding of complex scenarios and diverse defects. Second, we propose Defect Query Selector, which selects defect queries based on the semantic relevance between texts and global feature embeddings. This increases attention to potential defects and reduces missed detections. Finally, we propose Semantic Consistency Loss, which semantically aligns defect queries with defect prompts. With additional cross-modal supervision signals, it refines the semantics of defects. For real-world scenarios with normal reference images, we propose ViLG+, which effectively filters false positives using feature similarity. It further verifies that the global embeddings effectively represent the overall structure of visual scenes as well as subtle local features. Compared with other advanced methods on two train surface defect datasets and two public defect datasets, ViLG shows higher precision, recall, and average precision on unseen defects with relatively faster speed, with average improvements of 23.58, 3.23, and 6.05, and has a more balanced false positive rate and false negative rate.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"9080-9093"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213622","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}

引用次数: 0

Bidirectional Temporal Convolutional Graph Attention Networks for Key Node Identification in Traffic Monitoring 交通监控中关键节点识别的双向时间卷积图注意网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-15 DOI: 10.1109/TITS.2025.3555540

Yan Zhao;Can Wang;Yikang Rui;Wenqi Lu;Linheng Li;Bin Ran;Zhijun Chen

{"title":"Bidirectional Temporal Convolutional Graph Attention Networks for Key Node Identification in Traffic Monitoring","authors":"Yan Zhao;Can Wang;Yikang Rui;Wenqi Lu;Linheng Li;Bin Ran;Zhijun Chen","doi":"10.1109/TITS.2025.3555540","DOIUrl":"https://doi.org/10.1109/TITS.2025.3555540","url":null,"abstract":"Efficient identification of key nodes is crucial to optimizing detector deployment and enhancing traffic monitoring in intelligent transportation systems. However, existing approaches often struggle to adapt to dynamic traffic variations, leading to suboptimal coverage and increased deployment costs. We propose bidirectional temporal convolutional graph attention networks (BTC-GATs) to address these limitations. This novel framework integrates bidirectional attention mechanisms to capture upstream and downstream dependencies, temporal convolutional networks for multiscale feature extraction, and graph attention networks for spatial information aggregation. BTC-GATs incorporates adaptive temporal modeling to capture nonlinear traffic dynamics, gradient-based variation analysis to quantify node influence, and a ranking mechanism that fuses attention coefficients with topological attributes to further enhance robustness and interoperability. In addition, a key node coverage study is conducted to examine the trade-off between accuracy and deployment efficiency. Extensive experiments on the California Highway PeMS04 dataset demonstrate that BTC-GATs outperforms benchmark methods in key node identification, offering superior accuracy and stability. Further analysis confirms its robustness under varying traffic conditions and initialization settings, highlighting its potential as a scalable, adaptive, and cost-effective solution for intelligent traffic monitoring. By facilitating efficient sensor placement, BTC-GATs contributes to improved data collection and congestion management in large-scale transportation networks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8720-8737"},"PeriodicalIF":7.9,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206136","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}

引用次数: 0