IEEE Transactions on Intelligent Transportation Systems最新文献_第4页

MD-TLN: A Transformer--LSTM Hybrid Network for Metro Passenger Flow Prediction With Multi-Source Features 基于多源特征的地铁客流预测变压器-LSTM混合网络MD-TLN

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-20 DOI: 10.1109/TITS.2026.3709845

Lilin Chen;Junheng Yang;Zhixin Zhong;Xiaodong Zang;Qiang Luo;Weiying Chen;Yanhang Lin

{"title":"MD-TLN: A Transformer--LSTM Hybrid Network for Metro Passenger Flow Prediction With Multi-Source Features","authors":"Lilin Chen;Junheng Yang;Zhixin Zhong;Xiaodong Zang;Qiang Luo;Weiying Chen;Yanhang Lin","doi":"10.1109/TITS.2026.3709845","DOIUrl":"https://doi.org/10.1109/TITS.2026.3709845","url":null,"abstract":"Accurate metro passenger flow prediction is essential for enhancing daily crowd management and vehicle scheduling, especially amid the rapidly increasing demand for metro travel. However, existing mathematical and deep learning models struggle to capture the complex effects of heterogeneous external factors, resulting in limited modeling capacity and poor generalization in dynamic environments. To address these challenges, this paper proposes a hybrid deep learning framework, termed Multiple Dimension Transformer LSTM Network (MD-TLN), which integrates multi-source heterogeneous features within a unified structure. MD-TLN comprises three channels—historical, spatial, external and—organized into a structured five-dimensional tensor to balance expressiveness and computational efficiency. The model combines a convolutional feature encoder, a multi-patch transformer for temporal dependency modeling, and an attention-based fusion mechanism for dynamic feature weighting. Experiments on data from 263 Guangzhou Metro stations (October 1–31, 2024) show that MD-TLN significantly outperforms state-of-the-art baselines in MAE, MSE, and R<sup>2</sup>. Extensive performance evaluation and ablation studies further validate the accuracy and robustness of the proposed framework, demonstrating its strong potential for real-world metro operations under complex disturbances. The implementation of MD-TLN is available at: <uri>https://github.com/Lilin-Chen/MD-TLN</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13198-13213"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097052","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

Online IMU-Odometer Calibration Using GNSS Measurements for Autonomous Ground Vehicle Localization 基于GNSS测量的自动地面车辆定位在线imu -里程表校准

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1109/TITS.2026.3706684

Baoshan Song;Xiao Xia;Penggao Yan;Yihan Zhong;Weisong Wen;Li-Ta Hsu

{"title":"Online IMU-Odometer Calibration Using GNSS Measurements for Autonomous Ground Vehicle Localization","authors":"Baoshan Song;Xiao Xia;Penggao Yan;Yihan Zhong;Weisong Wen;Li-Ta Hsu","doi":"10.1109/TITS.2026.3706684","DOIUrl":"https://doi.org/10.1109/TITS.2026.3706684","url":null,"abstract":"Accurate calibration of intrinsic (odometer scaling factors) and extrinsic parameters (IMU–odometer translation and rotation) is essential for autonomous ground vehicle localization. Existing GNSS-aided approaches often rely on positioning results or raw measurements without ambiguity resolution, and their observability properties remain underexplored. This paper proposes a tightly coupled online calibration method that fuses IMU, odometer, and raw GNSS measurements (pseudo-range, carrier-phase, and Doppler) within an extendable factor graph optimization (FGO) framework, incorporating outlier mitigation and ambiguity resolution. Observability analysis reveals that two horizontal translation and three rotation parameters are observable under general motion, while vertical translation remains unobservable. Simulation and real-world experiments demonstrate superior calibration and localization performance over the state-of-the-art loosely coupled method. Specifically, the IMU-odometer positioning using our calibrated parameters achieves the absolute maximum error of <inline-formula> <tex-math>$17.75~m$ </tex-math></inline-formula> while the one of LC method is <inline-formula> <tex-math>$61.51~m$ </tex-math></inline-formula>, achieving up to 71.14% improvement. To foster further research, we also release the first open-source dataset that combines IMU, 2D odometer, and raw GNSS measurements from both rover and base stations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12289-12302"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097324","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

Rail3DNet: A Monocular Vision-Based 3-D Rail-Track Detection Network Using Rail-IoU Loss Rail3DNet：基于单目视觉的三维轨道-轨道检测网络

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-08-03 DOI: 10.1109/TITS.2026.3713896

Songyue Yang;Hongbo Li;Zhangyu Wang;Peng Chen;Guizhen Yu;Junjie Zhang

{"title":"Rail3DNet: A Monocular Vision-Based 3-D Rail-Track Detection Network Using Rail-IoU Loss","authors":"Songyue Yang;Hongbo Li;Zhangyu Wang;Peng Chen;Guizhen Yu;Junjie Zhang","doi":"10.1109/TITS.2026.3713896","DOIUrl":"https://doi.org/10.1109/TITS.2026.3713896","url":null,"abstract":"Three-dimensional Rail-track detection is crucial for autonomous driving trains. Disadvantageously, existing methods primarily detect the rail-tracks on the image plane, making it difficult to obtain the three-dimensional coordinates of the rail-tracks. Therefore, this study introduces the first monocular vision-based rail-track detection network (Rail3DNet). An information interaction module was proposed to fully utilize the prior information on the rail-track. Besides, a feature reconstruction method was introduced to reconstruct the extracted features and incorporate three-dimensional coordinate information, thereby enriching the feature information content. Furthermore, a three-dimensional rail intersection over union loss function was designed to align the rail-tracks closer to the ground truth. The experimental results revealed that the proposed method had achieved an mF1 score of 73.58%, which was characteristic of state-of-the-art performance. The effectiveness of the proposed method was also validated on three different rail transit lines. The proposed rail-track detection network can offer accurate train clearances and increased safety in the navigation of autonomous trains.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12911-12924"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097395","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

Joint Super-Resolution Reconstruction for Small Traffic Sign Detection via Multitask Learning 基于多任务学习的小交通标志联合超分辨率重建

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-24 DOI: 10.1109/TITS.2026.3704948

Zhaowen Lv;Shouxing Wang;Li Zheng;Yao Tang;Qiangqiang Yuan

{"title":"Joint Super-Resolution Reconstruction for Small Traffic Sign Detection via Multitask Learning","authors":"Zhaowen Lv;Shouxing Wang;Li Zheng;Yao Tang;Qiangqiang Yuan","doi":"10.1109/TITS.2026.3704948","DOIUrl":"https://doi.org/10.1109/TITS.2026.3704948","url":null,"abstract":"As a crucial part of the Intelligent Transportation Systems (ITS), traffic sign detection is of vital significance for the construction of high-precision maps, the automation of traffic rule monitoring and the development of auxiliary autonomous driving technologies. However, it faces two critical challenges: 1) limited pixel information for long-distance traffic signs. 2) insufficient deep feature representation in general detectors. To this end, this paper proposes SR-YOLOv8s, an end-to-end framework that incorporates super-resolution (SR) reconstruction and traffic sign detection within a unified multi-task learning paradigm. Specifically, an auxiliary SR branch is introduced alongside the detection head to collaboratively guide the shared backbone in extracting high-resolution and high-quality features, complemented by optimized backbone depth to preserve informative representations. The integration of multi-level features followed by SR-based supervision enables the recovery of fine-grained spatial details, thereby improving the detector’s sensitivity to small targets in complex road scenarios. Moreover, the SR branch is designed to be discarded during inference, incurring no additional computational cost. Comprehensive experiments on CCTSDB, DFG, and TT100k demonstrate that SR-YOLOv8s achieves significant improvements of 11.57, 11.75, and 13.18 in mAP50:95 over the baseline YOLOv8s, with only negligible increases in parameters and inference time, offering a highly cost-effective improvement in accuracy and generalization. The code and results will be accessible at <uri>https://github.com/zhaowenlv5-cmyk/SR-YOLOv8</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12303-12321"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097373","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

GCaSNet: Global Context-Aware Selective Detection Network for Rail Surface Defect Detection 铁路表面缺陷检测的全局上下文感知选择性检测网络

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2025-12-31 DOI: 10.1109/TITS.2025.3612032

Yuansheng Hua;Yun Yang;Wen Sheng;Song Zhu;Jiasong Zhu;Qingquan Li

{"title":"GCaSNet: Global Context-Aware Selective Detection Network for Rail Surface Defect Detection","authors":"Yuansheng Hua;Yun Yang;Wen Sheng;Song Zhu;Jiasong Zhu;Qingquan Li","doi":"10.1109/TITS.2025.3612032","DOIUrl":"https://doi.org/10.1109/TITS.2025.3612032","url":null,"abstract":"The accurate and fast detection of rail surface defects is of great significance for the safe operation of rail transport. Convolutional neural network (CNN) is the dominant choice for rail surface defect detection (RSDD) due to its superior feature learning capability. However, current RSDD still suffers from two challenges: high similarity of defects to the background and limited maintenance skylights. To address these challenges, we propose a novel RSDD network, namely global context-aware selective detection network (GCaSNet). Specifically, a rail surface image is first partitioned into non-overlapping patches and then projected into an embedding space for yielding tokens. Afterwards, they are fed into a global context-aware backbone for encoding short- and long-range feature relations. Eventually, a patch-wise detection head is tailored to localize and identify rail surface defects in selected suspicious patches for fast computation. We evaluate the proposed GCaSNet on Type-I and Type-II rail surface discrete defects data sets, and experimental results demonstrate that GCaSNet improves the mAP by at most 10% over existing convolutional networks and Transformer-based baselines while at low computational costs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13503-13515"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097706","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

Online EV Fast Charging Scheduling With Energy Storage System Using a Profit-Predictable MILP 基于收益可预测MILP的储能系统电动汽车在线快速充电调度

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-08-04 DOI: 10.1109/TITS.2026.3714449

Chun-An Yang;Tzu-Chieh Lin;Shih-Chieh Chen;Yu-Wen Chen;Jian-Jhih Kuo;Ming-Jer Tsai

{"title":"Online EV Fast Charging Scheduling With Energy Storage System Using a Profit-Predictable MILP","authors":"Chun-An Yang;Tzu-Chieh Lin;Shih-Chieh Chen;Yu-Wen Chen;Jian-Jhih Kuo;Ming-Jer Tsai","doi":"10.1109/TITS.2026.3714449","DOIUrl":"https://doi.org/10.1109/TITS.2026.3714449","url":null,"abstract":"Fast charging stations aim to efficiently allocate power to electric vehicles (EVs) through optimized charging scheduling, providing rapid services while meeting increasing demand. However, due to the limited capacity of the grid infrastructure, an energy storage system (ESS) is often integrated to buffer power in advance. Coordinating the charging and discharging schedules of both EVs and ESS remains a challenging task. Many existing EV charging scheduling approaches overlook ESS power management, while others incorporate ESS considerations but neglect the dynamic nature of EV charging. For instance, the maximum charging power of an EV typically decreases as its battery nears full capacity. Moreover, uncertainties such as unknown EV arrival and departure times, unpredictable waiting behaviors, and fluctuating time-of-use (TOU) pricing further complicate the problem. To address these challenges, this paper formulates an online optimization problem and introduces a novel algorithm that predicts future profits to assist decision-making in power allocation. The algorithm leverages carefully designed mixed-integer linear programming (MILP) to emulate a neural network (NN). Simulation results using generated optimal charging data demonstrate that the proposed algorithm significantly outperforms existing baseline methods by up to 50%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12849-12862"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097795","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

ByFeDis: Toward Byzantine-Robust Heterogeneous Federated Learning via Knowledge Distillation in Railway Intelligent Control Systems 铁路智能控制系统中基于知识蒸馏的拜占庭鲁棒异构联邦学习

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-03-18 DOI: 10.1109/TITS.2026.3690685

Shijia Guo;Rong Sun;Jingwei Liu

{"title":"ByFeDis: Toward Byzantine-Robust Heterogeneous Federated Learning via Knowledge Distillation in Railway Intelligent Control Systems","authors":"Shijia Guo;Rong Sun;Jingwei Liu","doi":"10.1109/TITS.2026.3690685","DOIUrl":"https://doi.org/10.1109/TITS.2026.3690685","url":null,"abstract":"Railway intelligent train control system must accommodate a wide range of scenarios due to the extensive variation in its operational environment. Federated learning based on knowledge distillation offers an efficient training paradigm for such systems by supporting model heterogeneity and reducing communication overhead. However, conventional federated knowledge distillation approaches often exhibit limited robustness against untrusted clients and do not facilitate continuous learning. In this paper, we design a Byzantine-robust federated knowledge distillation scheme, named ByFeDis, which mitigates the impact of malicious clients by removing outliers and low-value learning data, thereby improving learning efficiency. Additionally, we enhance the scalability of federated knowledge distillation in an improved scheme ByFeDis*, which is built on ByFeDis and enables continuous training for newly joined clients. Specifically, ByFeDis measures the soft labels uploaded by clients using Euclidean distance to identify poisoned soft labels that disrupt the training process. These abnormal soft labels include not only malicious perturbed samples that significantly deviate from the distribution of the true labels, but also speculative soft labels that are overly close to the true labels. To further enhance privacy protection, we employ fully homomorphic encryption to enable secure aggregation, thereby safeguarding sensitive client information. Theoretical analysis confirms that our scheme effectively preserves privacy while reducing communication complexity compared with existing approaches. Extensive experiments across multiple datasets demonstrate that ByFeDis achieves, on average, a 2% improvement in accuracy over other Byzantine-robust schemes, while ByFeDis* further enhances model accuracy by 3% and reduces premature convergence by up to 10 training rounds in subsequent learning.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"13584-13597"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097844","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

Integrating Lateral Transportation and Collaborative Energy Scheduling: A Bi-Level Programming Approach for Port Waste-Energy Management 整合横向运输与协同能源调度：港口废弃物能源管理的双层规划方法

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-06-08 DOI: 10.1109/TITS.2026.3697772

Junyu Yu;Ji Quan;Xianjia Wang

{"title":"Integrating Lateral Transportation and Collaborative Energy Scheduling: A Bi-Level Programming Approach for Port Waste-Energy Management","authors":"Junyu Yu;Ji Quan;Xianjia Wang","doi":"10.1109/TITS.2026.3697772","DOIUrl":"https://doi.org/10.1109/TITS.2026.3697772","url":null,"abstract":"This study introduces a novel port waste transportation and energy scheduling coupled system, addressing existing research limitations that primarily focus on isolated waste transportation or disposal processes and lack integration of waste-to-energy technology with port energy management systems. The system integrates multiple treatment facilities equipped with waste-to-energy technology and multiple waste collection stations, establishing a dynamic link between solid waste transportation quantities and the energy output of treatment facilities, while considering collaborative transportation of solid waste and collaborative energy scheduling among facilities. To balance the conflicting objectives of port authority and treatment facilities, a bi-level programming model is proposed. After transforming the asymmetric Nash bargaining game and applying Karush-Kuhn-Tucker conditions, the model is solved using commercial solvers. Comparative analysis of different cases under uncertain renewable energy generation and electrical load conditions shows that integrating lateral transportation and collaborative energy scheduling achieves the lowest economic costs and highest environmental benefits. Specifically, compared to the baseline Case 1, Case 4, which integrates both lateral transportation and collaborative energy scheduling, reduces the average cost of the port authority by 0.95%, the average total cost of treatment facilities by 54.39%, the average carbon emissions of the port authority by 3.63%, the average total carbon emissions of treatment facilities by 11.94%, and the average electricity purchased by treatment facilities from the distribution grid by 31.44%. The proposed system offers viable solutions for the sustainable development of port waste management and energy scheduling.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12935-12945"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097946","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

IEEE Transactions on Intelligent Transportation Systems Information for Authors IEEE智能交通系统信息汇刊

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-10-05 DOI: 10.1109/TITS.2026.3737037

引用次数: 0

Federated Graph Learning for EV Charging Demand Forecasting With Personalization Against Cyberattacks 针对网络攻击的个性化电动汽车充电需求预测的联邦图学习

IF 9.1 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2026-10-01 Epub Date: 2026-09-01 DOI: 10.1109/TITS.2026.3712866

Yi Li;Renyou Xie;Chaojie Li;Yi Wang;Zhaoyang Dong

{"title":"Federated Graph Learning for EV Charging Demand Forecasting With Personalization Against Cyberattacks","authors":"Yi Li;Renyou Xie;Chaojie Li;Yi Wang;Zhaoyang Dong","doi":"10.1109/TITS.2026.3712866","DOIUrl":"https://doi.org/10.1109/TITS.2026.3712866","url":null,"abstract":"Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, existing methods either suffer from the data privacy issue and the susceptibility to cyberattacks or fail to consider the spatial correlation among different stations. To address these challenges, a federated graph learning approach involving multiple charging stations is proposed to collaboratively train a more generalized deep learning model for demand forecasting while capturing spatial correlations among various stations and enhancing robustness against potential attacks. Firstly, for better model performance, a spatial-temporal Graph Neural Network (GNN) model is leveraged to characterize the geographic correlation among different charging stations in a federated manner. Secondly, to ensure robust aggregation and deal with the data heterogeneity in a federated setting, a message passing that utilizes a global attention mechanism to aggregate personalized models for each client is proposed. Thirdly, by concerning cyberattacks, a special credit-based function is designed to mitigate potential threats from malicious clients or unwanted attacks. Extensive experiments on three public EV charging datasets are conducted using various deep learning techniques and federated learning methods to demonstrate the prediction accuracy and robustness against cyberattack of the proposed approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 10","pages":"12718-12736"},"PeriodicalIF":9.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097988","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