IEEE Transactions on Intelligent Transportation Systems最新文献_第2页

Arterial Ecosignal Coordination Based on Vehicle Trajectory Estimation Using Kinematic Analytical Formula 基于运动解析公式的车辆轨迹估计的动脉生态信号协调

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-04-01 DOI: 10.1109/TITS.2025.3553881

Yongjie Lin;Shanshan Li;Kai Lu;Binbin Jing

{"title":"Arterial Ecosignal Coordination Based on Vehicle Trajectory Estimation Using Kinematic Analytical Formula","authors":"Yongjie Lin;Shanshan Li;Kai Lu;Binbin Jing","doi":"10.1109/TITS.2025.3553881","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553881","url":null,"abstract":"Existing arterial signal coordination predominantly adopts physical traffic performance indicators or alternative progression bandwidth as optimization objectives, which lack a constraint mechanism between traffic efficiency and the environmental impacts associated with signal parameters. This study proposes a novel arterial ecosignal coordination approach to minimize gas emissions, energy consumption, and passenger delays by leveraging vehicle trajectory data. Initially, a vehicle kinematic analytical model incorporating driving behavior is developed to extract second-by-second individual vehicle trajectories. Through the analysis of micro-trajectory data and comparison with the evaluation metrics from VISSIM simulator, the model is validated to accurately simulate vehicle operating conditions. Subsequently, an exact ecological cost unit, encompassing gas emissions, fuel consumption, and delays for all vehicles by explicitly accounting for both electric and conventional fuel vehicles, is calculated based on the trajectory data. This cost unit is then selected as the objective function of the optimization problem, which is formulated as a bi-level model. Numerical experiments on a five-intersection arterial segment demonstrate that the proposed ecosignal coordination can significantly enhance environmental benefits at the cost of a small amount of delay growth compared to existing methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6186-6200"},"PeriodicalIF":7.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908419","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

Deciding Truck Platooning Operational Rules With Stochastic Approximation for Vehicle Safe Overtaking on Two-Lane Undivided Highways 用随机逼近法确定两车道不分段公路车辆安全超车的队列操作规则

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-31 DOI: 10.1109/TITS.2025.3553841

Yu Song

{"title":"Deciding Truck Platooning Operational Rules With Stochastic Approximation for Vehicle Safe Overtaking on Two-Lane Undivided Highways","authors":"Yu Song","doi":"10.1109/TITS.2025.3553841","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553841","url":null,"abstract":"Truck platooning is a promising technology to improve freight transport efficiency. However, this technology has primarily been explored on multi-lane divided highways, neglecting potential applications on two-lane undivided highways prevalent in rural areas. This paper addresses the challenges of deploying truck platooning on such highways, focusing on overtaking safety. We formulate safe overtaking via stochastic approximation as a feasibility problem. An algorithm is proposed to determine operational rules for truck platooning, considering factors including traffic flow, road segment geometry, and vehicle dynamics. Values for truck platooning operational parameters such as speed, inter-truck following gap, and number of trucks in platoon are decided which ensure overall overtaking safety. The algorithm is iterative and depends on first order information, avoiding possible complex computations of stochastic objective with respect to truck platooning operational parameters. The algorithm is general and adapts to different roadway and traffic conditions and weights on platooning parameters. We show that computation of the algorithm is linear in sample size used in approximation. Simulations show that the algorithm-generated platooning rules offer an overtaking safety guarantee of up to 93% on a straight and flat road segment with sparse traffic.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6201-6215"},"PeriodicalIF":7.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908422","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

Stochastic Behavior Modeling and Optimal Bidirectional Charging Station Deployment in EV Energy Network 电动汽车能源网络随机行为建模与最优双向充电站部署

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-31 DOI: 10.1109/TITS.2025.3553513

Yao Tang;Wei Liu;Kwok Tong Chau;Yunhe Hou;Jian Guo

{"title":"Stochastic Behavior Modeling and Optimal Bidirectional Charging Station Deployment in EV Energy Network","authors":"Yao Tang;Wei Liu;Kwok Tong Chau;Yunhe Hou;Jian Guo","doi":"10.1109/TITS.2025.3553513","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553513","url":null,"abstract":"Electric vehicle energy network (EVEN) enables the transmission of renewable energy from rural to urban area by the flexibility of EVs via energy exchange. In this paper, (dis)charging behavior modelling and bidirectional charging station (BCS) deployment optimization are addressed, since they are crucial in EVEN for EV accommodation, renewable energy utilization, drivers’ profitability estimation, operators’ cost assessment, and financial policy establishment. A novel stochastic Markov (dis)charging behavior model is proposed to calculate the spatiotemporal load pattern considering the realistic factors such as personal features, state of charge (SoC), electricity price, and BCS locations. Unlike most works ignoring energy trading, six scenarios are explored: (S1) no trade; (S2) trade in main battery. (S3) trade in extra battery. (S4) trade in extra ultracapacitor; (S5) trade in both main and extra battery; (S6) trade in both main battery and ultracapacitor. Also, a multi-objective BCS deployment strategy is newly designed, aiming at minimizing installation cost and driver’s electricity bill, while quality of service (QoS) and voltage stability are ensured. An improved hybrid algorithm is developed, which combines hill climbing for enhanced exploitation and particle swarm optimization for better evolvement based on genetic algorithm framework. The simulation validates the fitting ability of charging model, the effectiveness of parameter selection algorithm and the deployment approach. Comparing 6 scenarios, benefits of energy trading in EVEN is confirmed and the superiority of ultracapacitor for trading is demonstrated. The feasibility of financial policies is also studied, and certain guidance is provided for drivers to improve their cost.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"6231-6247"},"PeriodicalIF":7.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908401","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

Intention-Aware Denoising Diffusion Model for Trajectory Prediction 弹道预测的意图感知去噪扩散模型

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-31 DOI: 10.1109/TITS.2025.3553125

Chen Liu;Shibo He;Haoyu Liu;Jiming Chen

{"title":"Intention-Aware Denoising Diffusion Model for Trajectory Prediction","authors":"Chen Liu;Shibo He;Haoyu Liu;Jiming Chen","doi":"10.1109/TITS.2025.3553125","DOIUrl":"https://doi.org/10.1109/TITS.2025.3553125","url":null,"abstract":"Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. Considering the inherent uncertainty of the task, numerous studies have utilized generative models to produce multiple plausible future trajectories for each agent. However, most of them suffer from limited representation ability or unstable training issues. To overcome these limitations, we propose utilizing the diffusion model to generate the distribution of future trajectories. Two cruxes are to be settled to realize such an idea. First, the diversity of intention is intertwined with the uncertain surroundings, making the true distribution hard to parameterize. Second, the diffusion process is time-consuming during the inference phase, rendering it unrealistic to implement in a real-time driving system. We propose an Intention-aware denoising Diffusion Model (IDM), which addresses the above two problems. We decouple the original uncertainty into intention uncertainty and action uncertainty and model them with two dependent diffusion processes. To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps. To validate our approach, we conduct experiments on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset. Our methods achieve state-of-the-art results, with a minFDE of 13.83 pixels on the SDD dataset and 0.36 meters on ETH/UCY datasets. Compared with the original diffusion model, IDM reduces inference time by two-thirds. Interestingly, our experiments further reveal that introducing intention information is beneficial in modeling the diffusion process of fewer steps.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5915-5930"},"PeriodicalIF":7.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913534","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

Corrections to “Traffic Count Estimation at Basis Links Without Path Flow and Historic Data” 修正“在没有路径流量和历史数据的情况下估计基本路段的交通量”

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

引用次数: 0

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 : 2025-03-28 DOI: 10.1109/TITS.2025.3550766

引用次数: 0

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 : 2025-03-28 DOI: 10.1109/TITS.2025.3550767

引用次数: 0

Characterization, Analysis, and Modeling of the Non-Stationary V2I Channel at 28-GHz on Highways 高速公路上28ghz非平稳V2I信道的特性、分析和建模

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3552920

Luis A. López-Valcárcel;Manuel García Sánchez

引用次数: 0

Scanning the Issue 扫描问题

IF 7.9 1区工程技术

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

Simona Sacone

引用次数: 0

Protecting the Copyright of Intelligent Transportation Systems Based on Zernike Moments 基于Zernike矩的智能交通系统版权保护

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-28 DOI: 10.1109/TITS.2025.3552462

Jiale Meng;Zhe-Ming Lu

{"title":"Protecting the Copyright of Intelligent Transportation Systems Based on Zernike Moments","authors":"Jiale Meng;Zhe-Ming Lu","doi":"10.1109/TITS.2025.3552462","DOIUrl":"https://doi.org/10.1109/TITS.2025.3552462","url":null,"abstract":"Intelligent transportation systems are at risk of data misuse. Watermarking data that needs to be opened and shared can mitigate this problem, i.e., embedding signals into generated images, which are imperceptible to humans. Regardless of the shape of the image or any potential attacks it may undergo, the watermark can be detected by algorithms when needed. However, many watermarking schemes fail to resist attacks like cropping and translation, limiting their applicability in the intelligent transportation domain. To tackle these issues, we propose a dual watermarking framework based on Zernike moments for intelligent transportation systems, where a robust watermark and a periodic watermark are embedded in different planes of the cover image. Specifically, we propose a single-circle model (SCM) where Zernike moments are locally computed based on a circle centered at the image center with a radius proportional to image size for embedding the robust watermark. Since SCM is determined by measuring its center and radius, SCM is applicable to images of various sizes and shapes. Then we employ a robust combination of discrete wavelet transform (DWT) and discrete cosine transform (DCT) watermarking algorithms to embed the periodic watermark. For watermark extraction, we propose an efficient adaptive correction mechanism (AC) to recognize attack types and automatically relocate the embedding position of the watermark. By combining the above strategies, our proposed scheme can adaptively resist various attacks (e.g., random cropping, translation), which addresses the shortcomings of most existing watermarking schemes. We test the watermark using over 300 images of different sizes and shapes, and the experimental results prove that our proposed scheme achieves stronger robustness against various distortions with better invisibility, outperforming the state-of-the-art (SOTA) methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 5","pages":"5975-5987"},"PeriodicalIF":7.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913365","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