IEEE Transactions on Intelligent Transportation Systems最新文献

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY Ieee智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-07-02 DOI: 10.1109/TITS.2025.3579658

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IEEE Intelligent Transportation Systems Society Information IEEE智能交通系统学会信息

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-07-02 DOI: 10.1109/TITS.2025.3579612

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Scanning the Issue 扫描问题

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-07-02 DOI: 10.1109/TITS.2025.3580163

Simona Sacone

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Traffic Flow Crystallization Method for Trajectory Approximation and Lane Change Inference 交通流结晶法的轨迹逼近与变道推理

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-13 DOI: 10.1109/TITS.2025.3572623

Mohammad Ali Arman;Chris M. J. Tampère

{"title":"Traffic Flow Crystallization Method for Trajectory Approximation and Lane Change Inference","authors":"Mohammad Ali Arman;Chris M. J. Tampère","doi":"10.1109/TITS.2025.3572623","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572623","url":null,"abstract":"Whereas on many motorways, traffic operations are permanently monitored, and long historical logs of such data exist, they are not directly usable for lane change studies, as they only register local passages and speeds. This study proposes a novel method to transform discrete vehicle passage records of individual vehicle data (IVD) into approximations of vehicle trajectories and inference of lane change maneuvers (LCMs), such that large-scale LCM dataset can be retrieved from existing infrastructures where IVD is recorded at sufficiently close spacings (~600 meters). The method’s core is a probabilistic re-identification of individual vehicles in successive, lane-specific loop detectors. Dubbed Traffic Flow Crystallization (TFC), the methodology enhances traffic monitoring by providing vast and diverse LCM datasets. It consists of two key re-identification (ReID) modules: a lane-restricted module that matches vehicles strictly within the same lane and a non-lane-restricted module that recursively identifies lane-changing vehicles using boundary conditions imposed by previously matched vehicles. This recursive process resembles crystal growth, inspiring the method’s name. The ReID methodology is based on a weighted likelihood function consisting of Bayesian probability estimators that integrate three similarity measures: vehicle length, passage time, and passage speed. A lane-change feasibility filter ensures that re-identified vehicles satisfy plausible spatiotemporal constraints. The final module resolves inconsistencies and infers LCMs. The proposed method is trained and validated using CCTV footage, where visually-identified vehicles serve as ground truth. Validation results demonstrate a vehicle ReID success rate exceeding 96% and an inferred LCM rate with only a 2% underestimation compared to ground truth.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9305-9325"},"PeriodicalIF":7.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536404","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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Vehicle Cooperative Positioning With Tightly Coupled GNSS/INS/UWB Integration Based on Improved Multiple Fading Factors and Adaptive Cost Function 基于改进多重衰落因子和自适应成本函数的GNSS/INS/UWB紧密耦合集成车辆协同定位

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-13 DOI: 10.1109/TITS.2025.3575812

Jingang Zhao;Wei Sun;Wei Ding;Yadan Li;Pengxiang Sun;Peilun Sun

{"title":"Vehicle Cooperative Positioning With Tightly Coupled GNSS/INS/UWB Integration Based on Improved Multiple Fading Factors and Adaptive Cost Function","authors":"Jingang Zhao;Wei Sun;Wei Ding;Yadan Li;Pengxiang Sun;Peilun Sun","doi":"10.1109/TITS.2025.3575812","DOIUrl":"https://doi.org/10.1109/TITS.2025.3575812","url":null,"abstract":"Cooperative positioning technology based on multi-vehicle information fusion is essential for advanced applications in intelligent transportation systems (ITS). The integration of global navigation satellite systems (GNSS), inertial navigation system (INS), and ultra-wideband (UWB) technology holds significant promise for enhancing the continuity and reliability of vehicle cooperative positioning. In tightly coupled GNSS/INS/UWB integration, the tolerance against measurement outliers and state model perturbations is pivotal for fulfilling the specific requirements of critical ITS applications. To optimize the comprehensive performance of vehicle cooperative positioning under uncertain sensor observation environments, this paper proposes a robust multiple fading factors unscented Kalman filtering (RMFUKF) algorithm based on adaptive cost function. The proposed solution incorporates Huber M-estimation with an adaptive tuning strategy to perform measurement-specific outliers processing. Furthermore, the improved multiple fading factors based on an exponential weighting method are implemented to mitigate the effects of dynamic model mismatches. Experimental results from vehicular field experiments demonstrate that the proposed RMFUKF scheme significantly improves the robustness and adaptive performance of vehicle cooperative positioning under unpredictable, real-world operating conditions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9740-9754"},"PeriodicalIF":7.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536578","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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Practical Distributed Control for Cooperative VTOL UAVs Within a 3-D Roundabout 三维回旋处协同垂直起降无人机的实用分布式控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-13 DOI: 10.1109/TITS.2025.3570005

Rao Fu;Pengda Mao;Yangqi Lei;Kai-Yuan Cai;Quan Quan

{"title":"Practical Distributed Control for Cooperative VTOL UAVs Within a 3-D Roundabout","authors":"Rao Fu;Pengda Mao;Yangqi Lei;Kai-Yuan Cai;Quan Quan","doi":"10.1109/TITS.2025.3570005","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570005","url":null,"abstract":"With the rapid development of uncrewed aerial vehicle (UAV) technology in recent years, research on large-scale low-altitude UAV air traffic management (ATM) has gained attention. Unlike the traditional ATM, the number of small UAVs in the airspace may be in the millions, making air traffic management challenging. In an ATM, airspace is composed of airways, intersections, and nodes. In this paper, a three-dimensional (3-D) roundabout model is utilized as an airspace structure for air traffic intersections of known traffic network models, which is decomposed into a central island, several ramps, and buffer zones. In this paper, for simplicity, the distributed coordination of the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass through a 3-D roundabout is focused on, which is formulated as a 3-D roundabout passing-through problem. The corresponding control objectives include inter-agent conflict-free, keeping within the 3-D curved virtual tube, and avoiding local minima. Lyapunov-like functions are designed elaborately, and formal analysis is made to show that all UAVs can pass through the 3-D roundabout without getting trapped. Taking the kinematic model of VTOL UAVs into consideration, the horizontal control and attitude control channels are decoupled, which is more reasonable for practical applications. Numerical simulation and real experiment are given to show the effectiveness of the proposed method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9341-9357"},"PeriodicalIF":7.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536447","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

Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection 面向路面裂缝检测的多粒度上下文信息流建模

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-12 DOI: 10.1109/TITS.2024.3438883

Junbiao Pang;Baocheng Xiong;Jiaqi Wu;Qingming Huang

{"title":"Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection","authors":"Junbiao Pang;Baocheng Xiong;Jiaqi Wu;Qingming Huang","doi":"10.1109/TITS.2024.3438883","DOIUrl":"https://doi.org/10.1109/TITS.2024.3438883","url":null,"abstract":"Pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. To precisely localize crack from an image, it is critical to effectively extract and aggregate multi-granularity context, including the fine-grained local context around the cracks (in spatial-level) and the coarse-grained semantics (in semantic-level). In this paper, we apply the dilated convolution as the backbone feature extractor to model local context, then we build a context guidance module to leverage semantic context to guide local feature extraction at multiple stages. To handle label alignment between stages, we apply the Multiple Instance Learning (MIL) strategy to align the feature between two stages. In addition, to our best knowledge, we have released the largest, most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The experimental results on the three crack datasets demonstrate that the proposed method performs well and outperforms the current state-of-the-art methods. On BPC, the proposed model achieved AP 88.32% with the 16.89 M parameters under the 45.36 GFlops runing speed. Datset and code are publicly available at: <uri>https://github.com/pangjunbiao/BPC-Crack-Dataset</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9165-9174"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536408","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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ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction 交通速度预测的可解释因果时空扩散网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-12 DOI: 10.1109/TITS.2025.3574837

Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato

{"title":"ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction","authors":"Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato","doi":"10.1109/TITS.2025.3574837","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574837","url":null,"abstract":"Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9781-9798"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536695","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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VEH-Attack: Stealthy Tracking of Train Passengers With Side-Channel Attack on Vibration Energy Harvesting Wearables veh攻击：利用振动能量收集可穿戴设备的侧通道攻击对火车乘客进行隐形跟踪

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-12 DOI: 10.1109/TITS.2025.3576220

Marzieh Jalal Abadi;Sara Khalifa;Mahbub Hassan;Salil Kanhere;Mohamed Ali Kaafar

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Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service 以铁路运力和客运服务为导向的列车进站规划与旅客分配一体化调度

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-11 DOI: 10.1109/TITS.2025.3574789

Ruxin Wang;Lei Nie;Yuyan Tan

{"title":"Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service","authors":"Ruxin Wang;Lei Nie;Yuyan Tan","doi":"10.1109/TITS.2025.3574789","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574789","url":null,"abstract":"In the process of railway operation planning, it is essential to take into account both railway capacity and origin to destination (OD) passenger demand. Stop plan plays a vital role in generating a train timetable with maximum railway capacity and ensuring high-quality service to transport passengers. Therefore, we are addressing the challenge of optimizing both the stop plan and timetable for a group of trains on a railway line, focusing on railway capacity estimation and passenger demand satisfaction. To provide realistic and precise passenger distribution, the preferences of different categories of passengers are given due regard. A classic time-space network describes the integrated problem, based on which a mathematical model is formulated to minimize train occupancy time on the high-speed railway line and maximize passenger kilometers at the same time. A decomposition approach based on Lagrangian relaxation (LR) is suggested to address the problem, which decomposes the integrated scheduling problem into two sub-problems: a train timetabling sub-problem, and a stop planning and passenger distributing sub-problem by dualizing constraints linking the two. A heuristic approach based on genetic algorithms is designed to obtain feasible solutions. The proposed model and approach are shown to generate good solutions efficiently. A series of real-world instances are conducted on the Beijing-Shanghai high-speed railway line in China, and the experimental outcomes show the benefits of optimizing the stop plan. Other related analyses are discussed by comparing results with different total number of stops, heterogeneous and homogeneous cases.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9445-9460"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536509","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