IEEE Transactions on Intelligent Transportation Systems最新文献

Corrections to “Traffic Count Estimation at Basis Links Without Path Flow and Historic Data”

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3546115

Subhrasankha Dey;Stephan Winter;Martin Tomko;Niloy Ganguly

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3550766

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IEEE Intelligent Transportation Systems Society Information

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3550767

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IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3548893

Simona Sacone

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Collaborative Collision Avoidance Approach for USVs Based on Multi-Agent Deep Reinforcement Learning

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-14 DOI: 10.1109/TITS.2025.3547775

Zhiwen Wang;Pengfei Chen;Linying Chen;Junmin Mou

{"title":"Collaborative Collision Avoidance Approach for USVs Based on Multi-Agent Deep Reinforcement Learning","authors":"Zhiwen Wang;Pengfei Chen;Linying Chen;Junmin Mou","doi":"10.1109/TITS.2025.3547775","DOIUrl":"https://doi.org/10.1109/TITS.2025.3547775","url":null,"abstract":"Unmanned Surface Vehicles (USVs) have garnered extensive interest for their potential in enhancing navigation safety and efficiency. To further improve their intelligence, we propose a collaborative collision avoidance decision-making approach for USVs based on Multi-Agent Deep Reinforcement Learning (MADRL). Firstly, a collision risk assessment model is established using Closest Point of Approach and Quaternion Ship Domain to guide USVs to take timely and effective collision avoidance actions. Secondly, we adopt Deep Recurrent Q-Network algorithm to overcome the challenges posed by multi-ship scenarios, and the Decentralized Partially Observable Markov Decision Process framework is employed in it to accurately establish multi-ship collaborative collision avoidance model. Moreover, to introduce a novel collaborative collision avoidance mechanism for multiple ships, we improved the network update mechanism of DRQN: by synergistically integrating the local Q value of each agent to acquire the multi-agent joint action Q value and the network parameters are subsequently updated based on the calculated global loss. Finally, we designed various experiments in a real water to validate the applicability and efficacy of the proposed approach. The experimental results indicate that this approach has the capacity to enable the ships to make effective collision avoidance actions in accordance with navigation practices and good seamanship. Comparative analysis with Deep Q-Network (DQN), DDQN, Dueling DQN, and Artificial Potential Field underscores the superior safety and rationality of the proposed approach in collision avoidance. This research offers a new perspective for the cooperative collision avoidance decision-making and has theoretical reference significance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4780-4794"},"PeriodicalIF":7.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725116","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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MDGNN: Multiple Flight Safety Incidents Prediction Model Based on Dynamic Graph Neural Networks

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-11 DOI: 10.1109/TITS.2025.3526946

Lei Song;Xu Li;Hongtao Liu;Lin Wu;Hong Sun;Linjiang Zheng;Jiaxing Shang

{"title":"MDGNN: Multiple Flight Safety Incidents Prediction Model Based on Dynamic Graph Neural Networks","authors":"Lei Song;Xu Li;Hongtao Liu;Lin Wu;Hong Sun;Linjiang Zheng;Jiaxing Shang","doi":"10.1109/TITS.2025.3526946","DOIUrl":"https://doi.org/10.1109/TITS.2025.3526946","url":null,"abstract":"Flight safety incidents, such as hard landings and tail strike risks, represent critical concerns during the landing phase. Although Quick Access Recorder (QAR) systems collect extensive multivariate flight data, previous studies have faced challenges in effectively modeling the complex interdependencies between flight parameters, which has limited their ability to predict multiple safety incidents simultaneously. To address this issue, we propose a novel model, named MDGNN, to capture hidden spatio-temporal dependencies and predict both hard landing and tail strike risk incidents. Specifically, we employ temporal convolutional networks (TCNs) to extract both localized representations and long-term temporal trends from multivariate flight data, ensuring the standardization of flight parameters across varying frequencies. Additionally, we are the first to construct a dynamic graph to model temporal relationships, applying a dynamic graph neural network and a temporal convolution module to accurately capture intricate spatial and temporal dependencies. Extensive experiments conducted on 37,904 Airbus A320 flight samples demonstrate that the MDGNN model surpasses state-of-the-art baselines with high prediction accuracy. Furthermore, a case study visualizing key flight parameters highlights the model’s ability to reveal the root causes of safety exceedances, offering valuable insights for flight safety analysis.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5598-5612"},"PeriodicalIF":7.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726427","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

Disturbance Compensation-Based Deep Reinforcement Learning Control Strategy for Underactuated Overhead Crane Systems: Design and Experiments

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-11 DOI: 10.1109/TITS.2025.3547322

Panlong Tan;Junjie Liu;Mingwei Sun;Zengqiang Chen

{"title":"Disturbance Compensation-Based Deep Reinforcement Learning Control Strategy for Underactuated Overhead Crane Systems: Design and Experiments","authors":"Panlong Tan;Junjie Liu;Mingwei Sun;Zengqiang Chen","doi":"10.1109/TITS.2025.3547322","DOIUrl":"https://doi.org/10.1109/TITS.2025.3547322","url":null,"abstract":"Transportation systems often exhibit uncertainty, dynamics, and nonlinearity, making them highly susceptible to perturbations. Overhead crane systems, being a crucial mode of transportation, find wide application in engineering practice. This paper proposes a compound control strategy for underactuated overhead crane systems, combining a deep reinforcement learning controller with a novel observer-based disturbance compensation algorithm. The aim is to suppress model uncertainties and external disturbances. Initially, an equivalent state variable is constructed to stabilize the overhead crane system, simplifying controller design. Subsequently, the model uncertainties and external disturbances are treated as generalized disturbances and incorporated as a new state. This state is estimated and compensated through a super-twisting extended state observer (STESO) with finite-time convergence. Lyapunov-based analysis demonstrates that the established state observer can reduce the residual estimate error to zero. Moreover, the Deep Deterministic Policy Gradient (DDPG) algorithm, a form of deep reinforcement learning, is employed to effectively eliminate tracking errors. The proposed scheme enables the underactuated system to achieve satisfactory performance, with adaptive control effort suited to different control states and disturbances. Simulation and hardware experiments are conducted, comparing the proposed control strategy with traditional methods, to illustrate its effectiveness and robustness.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4380-4390"},"PeriodicalIF":7.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735345","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

Safety Field-Based Vehicle-Infrastructure Cooperative Perception for Autonomous Driving Using 3D Point Clouds

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-11 DOI: 10.1109/TITS.2025.3546980

Cong Zhao;Delong Ding;Cailin Lei;Shiyu Wang;Yuxiong Ji;Yuchuan Du

{"title":"Safety Field-Based Vehicle-Infrastructure Cooperative Perception for Autonomous Driving Using 3D Point Clouds","authors":"Cong Zhao;Delong Ding;Cailin Lei;Shiyu Wang;Yuxiong Ji;Yuchuan Du","doi":"10.1109/TITS.2025.3546980","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546980","url":null,"abstract":"Cooperative perception, using vehicle-to-everything (V2X) technologies for perceptual data sharing between autonomous vehicles (AVs) and intelligent infrastructure, is considered a solution to many single-agent perception challenges. Early fusion, a data fusion scheme for the cooperative perception of AVs, provides a universally available data-sharing approach but has been criticized for its huge bandwidth consumption. This paper proposes a safety field (SF)-based vehicle-infrastructure cooperative perception approach by quantifying the driving risk in complex traffic scenarios. Leveraging the SF theory and point cloud downsampling, we design a delay-aware early fusion framework with adaptive communication volume control. We propose a latency-compensation error (LCE) for performance evaluation considering data transmission delay. The proposed framework is tested and verified in simulated city environments and simulated and real-world datasets. The experimental results show that the proposed approach increases the average precision (AP) and reduces the LCE compared with base models within a limited communication budget.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4676-4691"},"PeriodicalIF":7.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726382","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 Multi-Rank Federated Distillation Framework for Data-Imbalance Fault Diagnosis of Multi-Railway High-Speed Train Bogies

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-11 DOI: 10.1109/TITS.2025.3546688

Jiahao Du;Na Qin;Deqing Huang;Xinming Jia;Yiming Zhang

{"title":"A Multi-Rank Federated Distillation Framework for Data-Imbalance Fault Diagnosis of Multi-Railway High-Speed Train Bogies","authors":"Jiahao Du;Na Qin;Deqing Huang;Xinming Jia;Yiming Zhang","doi":"10.1109/TITS.2025.3546688","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546688","url":null,"abstract":"To address the challenge of secure federated modeling in fault diagnosis under imbalanced data scenarios for multi-railway high-speed train bogies, this study proposes a multi-rank federated distillation (MFD) framework aimed at enhancing the generalization capacity of clients with limited sample sizes. First, the MFD framework is designed to perform multiple distillation tasks, with each task’s loss function decoupled into two components to balance losses between target and non-target classes. Second, an adaptive weight adjustment strategy is introduced to efficiently train models by coordinating the loss outputs across these tasks. Third, to mitigate the learning costs associated with the MFD, clients share a foundational shallow network via model transfer while incorporating personalized modules to improve adaptability. By validating the proposed framework on datasets from high-speed train bogies across multiple railways, this study demonstrates its effectiveness in addressing challenges associated with secure federated modeling while maintaining satisfactory diagnostic performance. The findings present a viable solution for implementing federated learning among clients with imbalanced data in industrial applications.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4823-4836"},"PeriodicalIF":7.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725110","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

Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3546810

Nooshin Yousefzadeh;Rahul Sengupta;Yashaswi Karnati;Anand Rangarajan;Sanjay Ranka

{"title":"Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation","authors":"Nooshin Yousefzadeh;Rahul Sengupta;Yashaswi Karnati;Anand Rangarajan;Sanjay Ranka","doi":"10.1109/TITS.2025.3546810","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546810","url":null,"abstract":"Traffic congestion poses significant economic, environmental, and social challenges. High-resolution loop detector data and signal state records from Automated Traffic Signal Performance Measures (ATSPM) offer new opportunities for traffic signal optimization at intersections. However, additional factors such as geometry, traffic volumes, Turning-Movement Counts (TMCs), and human driving behaviors complicate this task. Existing simulators (e.g., SUMO, Vissim) are computationally intensive, while machine learning models often lack lane-specific traffic flow estimation. To address these issues, we propose two computationally efficient Attentional Graph Auto-Encoder frameworks as “Digital Twins” for urban traffic intersections. Leveraging graph representations and Graph Attention Networks (GAT), our models capture lane-level traffic flow dynamics at entry and exit points while remaining agnostic to intersection topology and lane configurations. Trained on over 40,000 hours of realistic traffic simulations with affordable GPU parallelization, our framework produces fine-grained traffic flow time series. This output supports critical applications such as estimating Measures of Effectiveness (MOEs), scaling to urban freeway corridors, and integrating with signal optimization frameworks for improved traffic management.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5082-5093"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740347","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