IEEE Transactions on Intelligent Transportation Systems最新文献_第8页

Optimal Real-Time Bidding Strategy for EV Aggregators in Wholesale Electricity Markets

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-10 DOI: 10.1109/TITS.2025.3536857

Shihan Huang;Dongkun Han;John Zhen Fu Pang;Yue Chen

{"title":"Optimal Real-Time Bidding Strategy for EV Aggregators in Wholesale Electricity Markets","authors":"Shihan Huang;Dongkun Han;John Zhen Fu Pang;Yue Chen","doi":"10.1109/TITS.2025.3536857","DOIUrl":"https://doi.org/10.1109/TITS.2025.3536857","url":null,"abstract":"With the rapid growth of electric vehicles (EVs), EV aggregators have been playing an increasingly vital role in power systems by not merely providing charging management but also participating in wholesale electricity markets. This work studies the optimal real-time bidding strategy for an EV aggregator. Since the charging process of EVs is time-coupled, it is necessary for EV aggregators to consider future operational conditions (e.g., future EV arrivals) when deciding the current bidding strategy. However, accurately forecasting future operational conditions is challenging under the inherent uncertainties. Hence, there demands a real-time bidding strategy based solely on the up-to-date information, which is the main goal of this work. We start by developing an online optimal EV charging management algorithm for the EV aggregator via Lyapunov optimization. Based on this, an optimal real-time bidding strategy (bidding function and bounds) for the aggregator is derived. Then, an efficient yet practical algorithm is proposed to obtain the bidding strategy. It shows that the cost of the aggregator is nearly offline optimal with the proposed bidding strategy. Moreover, the wholesale electricity market clearing result aligns with the individual aggregator’s optimal charging strategy given the prices. Case studies against several benchmarks are conducted to evaluate the performance of the proposed method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5538-5551"},"PeriodicalIF":7.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726430","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

Identifying Users Transferring Between Transportation Modes: A Stable Matching Approach

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-10 DOI: 10.1109/TITS.2025.3536632

Hongtai Yang;Zheng Liu;Daniel Kondor;Ke Han;Zhengbing He

{"title":"Identifying Users Transferring Between Transportation Modes: A Stable Matching Approach","authors":"Hongtai Yang;Zheng Liu;Daniel Kondor;Ke Han;Zhengbing He","doi":"10.1109/TITS.2025.3536632","DOIUrl":"https://doi.org/10.1109/TITS.2025.3536632","url":null,"abstract":"Travel data containing personal identification information needs to be anonymized before being shared and analyzed for privacy considerations. While this approach protects personal privacy, it makes it difficult for researchers and planners to identify the same traveler from different databases and to construct complete multi-modal trips, which greatly reduces the value of data. To address this challenge, this paper develops TBLink, a method to match individual travelers between different modes. The underlying idea is that if a traveler makes a transfer, the spatiotemporal signatures of the previous and next trips will be similar and must satisfy certain conditions. When this pattern occurs for two travelers in the two datasets repeatedly, we can infer that the two travelers are actually the same person. The matching of travelers is regarded as a stable matching problem, and the Gale-Shapley algorithm is used to solve the problem. TBLink is demonstrated using metro and bus trip data from Chengdu City between January and March 2021. The results show that the precision, recall, and recovery rate of user matching are 80.83%, 92.82%, and 10.56% respectively, and the matching is more reliable as the dataset increases. Sensitivity analysis is performed to study the effect of several model parameters on the matching performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4432-4442"},"PeriodicalIF":7.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735402","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

Predicting Pedestrian Crossing Intentions in Adverse Weather With Self-Attention Models

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-07 DOI: 10.1109/TITS.2024.3524117

Ahmed Elgazwy;Khalid Elgazzar;Alaa Khamis

{"title":"Predicting Pedestrian Crossing Intentions in Adverse Weather With Self-Attention Models","authors":"Ahmed Elgazwy;Khalid Elgazzar;Alaa Khamis","doi":"10.1109/TITS.2024.3524117","DOIUrl":"https://doi.org/10.1109/TITS.2024.3524117","url":null,"abstract":"The enhancement of the vehicle perception model represents a crucial undertaking in the successful integration of assisted and automated vehicle driving. By enhancing the perceptual capabilities of the model to accurately anticipate the actions of vulnerable road users, the overall driving experience can be significantly improved, ensuring higher levels of safety. Existing research efforts focusing on the prediction of pedestrians’ crossing intentions have predominantly relied on vision-based deep learning models. However, these models continue to exhibit shortcomings in terms of robustness when faced with adverse weather conditions and domain adaptation challenges. Furthermore, little attention has been given to evaluating the real-time performance of these models. To address these aforementioned limitations, this study introduces an innovative framework for pedestrian crossing intention prediction. The framework incorporates an image enhancement pipeline, which enables the detection and rectification of various defects that may arise during unfavorable weather conditions. Subsequently, a transformer-based network, featuring a self-attention mechanism, is employed to predict the crossing intentions of target pedestrians. This augmentation enhances the model’s resilience and accuracy in classification tasks. Through evaluation on the Joint Attention in Autonomous Driving (JAAD) dataset, our framework attains state-of-the-art performance while maintaining a notably low inference time. Moreover, a deployment environment is established to assess the real-time performance of the model. The results of this evaluation demonstrate that our approach exhibits the shortest model inference time and the lowest end-to-end prediction time, accounting for the processing duration of the selected inputs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3250-3261"},"PeriodicalIF":7.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535539","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

MPF: A Multi-Noise Perception Framework to Enhance Online Map Matching Algorithms

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-07 DOI: 10.1109/TITS.2025.3535801

Hanwen Hu;Cheng Zeng;Shiyou Qian;Jianhua Zhou;Jian Cao;Yirong Chen;Jie Wang;Han Han

{"title":"MPF: A Multi-Noise Perception Framework to Enhance Online Map Matching Algorithms","authors":"Hanwen Hu;Cheng Zeng;Shiyou Qian;Jianhua Zhou;Jian Cao;Yirong Chen;Jie Wang;Han Han","doi":"10.1109/TITS.2025.3535801","DOIUrl":"https://doi.org/10.1109/TITS.2025.3535801","url":null,"abstract":"Map matching is crucial to facilitating location-based services, and recent advancements in map matching have demonstrated excellent performance with high-quality data. However, the use of low-precision devices often introduces high measurement noise, and the slow update rate of maps may result in errors in digital maps. Consequently, multiple types of noise significantly impact the performance of map matching algorithms. To tackle this issue, this paper presents a novel multi-noise perception framework, named MPF, aiming to enhance the performance and robustness of existing map matching algorithms. The main challenge lies in detecting anomalies during map matching, identifying the root causes, and devising appropriate solutions. Firstly, we propose a matching quality assessment (MQA) method that assesses abnormal variance in matching probability. Secondly, we introduce a multiple noise discrimination (MND) mechanism to effectively differentiate between measurement noise and map errors. Thirdly, we present a missing segment generation (MSG) scheme that dynamically fills in map gaps to prevent significant detours. To validate the effectiveness of MPF, we conduct experiments using real-world taxi trajectories from four cities, covering a total distance of 79,670.6 km. MPF is compare with seven online map matching algorithms and is used to optimize their performance. The experiments show that MPF outperforms the top baselines by 15.6%-26.9% and enhances their performance by 18.7%-38.2%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4633-4646"},"PeriodicalIF":7.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724052","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 Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-06 DOI: 10.1109/TITS.2025.3535915

Alessandro Staffolani;Victor-Alexandru Darvariu;Paolo Bellavista;Mirco Musolesi

{"title":"A Cost-Aware Adaptive Bike Repositioning Agent Using Deep Reinforcement Learning","authors":"Alessandro Staffolani;Victor-Alexandru Darvariu;Paolo Bellavista;Mirco Musolesi","doi":"10.1109/TITS.2025.3535915","DOIUrl":"https://doi.org/10.1109/TITS.2025.3535915","url":null,"abstract":"Bike Sharing Systems (BSS) represent a sustainable and efficient urban transportation solution. A major challenge in BSS is repositioning bikes to avoid shortage events when users encounter empty or full bike lockers. Existing algorithms unrealistically rely on precise demand forecasts and tend to overlook substantial operational costs associated with reallocations. This paper introduces a novel Cost-aware Adaptive Bike Repositioning Agent (CABRA), which harnesses advanced deep reinforcement learning techniques in dock-based BSS. By analyzing demand patterns, CABRA learns adaptive repositioning strategies aimed at reducing shortages and enhancing truck route planning efficiency, significantly lowering operational costs. We perform an extensive experimental evaluation of CABRA utilizing real-world data from Dublin, London, Paris, and New York. The reported results show that CABRA achieves operational efficiency that outperforms or matches very challenging baselines, obtaining a significant cost reduction. Its performance on the largest city comprising 1765 docking stations highlights the efficiency and scalability of the proposed solution even when applied to BSS with a great number of docking stations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4923-4933"},"PeriodicalIF":7.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740246","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

Dynamic Control Authority Allocation in Indirect Shared Control for Steering Assistance

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-05 DOI: 10.1109/TITS.2024.3520107

Yutao Chen;Hongliang Zhang;Haocong Chen;Jie Huang;Bin Wang;Zixiang Xiong;Yuyi Wang;Xiwen Yuan

{"title":"Dynamic Control Authority Allocation in Indirect Shared Control for Steering Assistance","authors":"Yutao Chen;Hongliang Zhang;Haocong Chen;Jie Huang;Bin Wang;Zixiang Xiong;Yuyi Wang;Xiwen Yuan","doi":"10.1109/TITS.2024.3520107","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520107","url":null,"abstract":"The concept of shared control has garnered significant attention within the realm of human-machine hybrid intelligence research. This study introduces a novel approach, specifically a dynamic control authority allocation method, for implementing shared control in autonomous vehicles. Unlike conventional mixed-initiative control techniques that blend human and vehicle inputs with weights determined by predefined index, the proposed method utilizes optimization-based techniques to obtain an optimal dynamic allocation for human and vehicle inputs that satisfies safety constraints. Specifically, a convex quadratic programm (QP) is constructed incorporating control barrier functions (CBF) for safety and control Lyapunov functions (CLF) for satisfying automated control objectives. The cost function of the QP is designed such that human weight increases with the magnitude of human input. A smooth control authority transition is obtained by optimizing over the change rate of the weight instead of the weight itself. The proposed method is verified in lane-changing scenarios with human-in-the-loop (HmIL) and hardware-in-the-loop (HdIL) experiments. Results show that the proposed method outperforms index-based control authority allocation method in terms of agility, safety and comfort.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3458-3470"},"PeriodicalIF":7.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535415","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

DAGCAN: Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-05 DOI: 10.1109/TITS.2025.3531665

Qing Yuan;Junbo Wang;Yu Han;Zhi Liu;Wanquan Liu

{"title":"DAGCAN: Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting","authors":"Qing Yuan;Junbo Wang;Yu Han;Zhi Liu;Wanquan Liu","doi":"10.1109/TITS.2025.3531665","DOIUrl":"https://doi.org/10.1109/TITS.2025.3531665","url":null,"abstract":"It is necessary to establish a spatio-temporal correlation model in the traffic data to predict the state of the transportation system. Existing research has focused on traditional graph neural networks, which use predefined graphs and have shared parameters. But intuitive predefined graphs introduce biases into prediction tasks and the fine-grained spatio-temporal information can not be obtained by the parameter sharing model. In this paper, we consider it is crucial to learn node-specific parameters and adaptive graphs with complete edge information. To show this, we design a model based on graph structure that decouples nodes and edges into two modules. Each module extracts temporal and spatial features simultaneously. The adaptive node optimization module is used to learn the specific parameter patterns of all nodes, and the adaptive edge optimization module aims to mine the interdependencies among different nodes. Then we propose a Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting (DAGCAN), which relies on the above two modules to dynamically capture the fine-grained spatio-temporal relationships in traffic data. Experimental results on four public transportation datasets, demonstrate that our model can further improve the accuracy of traffic prediction.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3513-3526"},"PeriodicalIF":7.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564226","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

Scanning the Issue

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3528298

Simona Sacone

引用次数: 0

IEEE Intelligent Transportation Systems Society Information

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527807

引用次数: 0

Multi-Authority CP-ABE Scheme With Cryptographic Reverse Firewalls for Internet of Vehicles

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3533757

Ye Lin;Hu Xiong;Hui Su;Kuo-Hui Yeh

{"title":"Multi-Authority CP-ABE Scheme With Cryptographic Reverse Firewalls for Internet of Vehicles","authors":"Ye Lin;Hu Xiong;Hui Su;Kuo-Hui Yeh","doi":"10.1109/TITS.2025.3533757","DOIUrl":"https://doi.org/10.1109/TITS.2025.3533757","url":null,"abstract":"Internet of vehicles, featured with widely distributed vehicle nodes and limited computing power, usually have high performance requirements. Because of this feature, efficient and reliable access control has raised a challenge in Internet of vehicles. Ciphertext-policy attribute-based encryption (CP-ABE) could be denoted as an efficient solution for this problem. However, directly applying traditional single-authority CP-ABE schemes may result in single-point performance bottleneck. Besides, the secrets of the whole system may be leaked if any node is attacked. To solve these challenging tasks, we proposed MA-CP-ABE-CRF, a multi-authority CP-ABE scheme with cryptographic reverse firewalls. The system is designed to grant vehicles fine-grained access control by encrypting data under vehicle attributes. Besides, load balancing of authorization in distributed systems is achieved based on the characteristic of multi-authority. Meanwhile, specific nodes are equipped with cryptographic reverse firewalls (CRFs) to prevent information leakage. As the first scheme with the above features for Internet of vehicles, the system achieves adaptive CPA-security and ASA-security. Through rigorous theoretical analysis and experimental comparison, MA-CP-ABE-CRF is proved to be highly efficient and practical.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5348-5359"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726379","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