IEEE Transactions on Intelligent Transportation Systems最新文献_第10页

Last Mile: A Novel, Hotspot-Based Distributed Path-Sharing Network for Food Deliveries 最后一英里基于热点的新型食品配送分布式路径共享网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3465217

Ashman Mehra;Divyanshu Singh;Vaskar Raychoudhury;Archana Mathur;Snehanshu Saha

{"title":"Last Mile: A Novel, Hotspot-Based Distributed Path-Sharing Network for Food Deliveries","authors":"Ashman Mehra;Divyanshu Singh;Vaskar Raychoudhury;Archana Mathur;Snehanshu Saha","doi":"10.1109/TITS.2024.3465217","DOIUrl":"https://doi.org/10.1109/TITS.2024.3465217","url":null,"abstract":"Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, GrubHub, UberEats, Postmates, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. For this, we model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 15%, the distance traveled by 16%, and 50% higher fleet utilization w.r.t point-to-point delivery systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20574-20587"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736677","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 Combinatory AC and DC Charging Approach for Electric Vehicles 电动汽车交流和直流混合充电方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3464591

Baktharahalli Shantaveerappa Umesh;Vinod Khadkikar;Hatem Zeineldin;Shakti Singh;Hadi Otrok;Rabeb Mizouni;Akshay Rathore

{"title":"A Combinatory AC and DC Charging Approach for Electric Vehicles","authors":"Baktharahalli Shantaveerappa Umesh;Vinod Khadkikar;Hatem Zeineldin;Shakti Singh;Hadi Otrok;Rabeb Mizouni;Akshay Rathore","doi":"10.1109/TITS.2024.3464591","DOIUrl":"https://doi.org/10.1109/TITS.2024.3464591","url":null,"abstract":"Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with \u0000<inline-formula> <tex-math>$3.3~kW$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$19~kW$ </tex-math></inline-formula>\u0000 need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port \u0000<inline-formula> <tex-math>$(N (+),O(-))$ </tex-math></inline-formula>\u0000 is formed using the neutral of the EV motor winding \u0000<inline-formula> <tex-math>$(N)$ </tex-math></inline-formula>\u0000 and negative rail of the drivetrain inverter \u0000<inline-formula> <tex-math>$(O)$ </tex-math></inline-formula>\u0000. Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15467-15476"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579129","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 扫描问题

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3460988

Simona Sacone

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 : 2024-10-04 DOI: 10.1109/TITS.2024.3462229

引用次数: 0

Toward Resilient Electric Vehicle Charging Monitoring Systems: Curriculum Guided Multi-Feature Fusion Transformer 实现弹性电动汽车充电监控系统：课程引导的多特性融合变压器

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3456843

Duo Li;Junqing Tang;Bei Zhou;Peng Cao;Jia Hu;Man-Fai Leung;Yonggang Wang

{"title":"Toward Resilient Electric Vehicle Charging Monitoring Systems: Curriculum Guided Multi-Feature Fusion Transformer","authors":"Duo Li;Junqing Tang;Bei Zhou;Peng Cao;Jia Hu;Man-Fai Leung;Yonggang Wang","doi":"10.1109/TITS.2024.3456843","DOIUrl":"https://doi.org/10.1109/TITS.2024.3456843","url":null,"abstract":"With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in real-time EVCM is the handling of missing data caused by unexpected events, which can impair both real-time monitoring and its downstream applications. To address this vital yet underexplored issue, we propose a curriculum guided multi-feature fusion transformer (CurriFusFormer) learning framework – a novel approach designed to enhance the resilience of EVCM systems against real-time information omissions. Our framework integrates curriculum learning with a multi-feature fusion transformer model, capable of handling various patterns and rates of missing data, ranging from random to block omissions. This innovative approach leverages spatial, temporal, and static features to generate accurate real-time estimations for missing values in diverse scenarios. Extensive experiments on a real-world EVCM dataset demonstrate that CurriFusFormer can perform well with \u0000<inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula>\u0000 ranging from 0.92 to 0.83 given the rising missing rate from 30-90%, outperforming seven popular and state-of-the-art methods, especially in scenarios with high missing rates and complex patterns, such as, at 90% missing rate, kNN (\u0000<inline-formula> <tex-math>$R^{2} =0.65$ </tex-math></inline-formula>\u0000), XGBoost (\u0000<inline-formula> <tex-math>$R^{2} =0.78$ </tex-math></inline-formula>\u0000), BRITS (\u0000<inline-formula> <tex-math>$R^{2} =0.79$ </tex-math></inline-formula>\u0000), TFT (\u0000<inline-formula> <tex-math>$R^{2} =0.80$ </tex-math></inline-formula>\u0000), and GRIN (\u0000<inline-formula> <tex-math>$R^{2} =0.82$ </tex-math></inline-formula>\u0000). All results suggest that the proposed framework could be a promising solution for developing future resilient EVCM networks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21356-21366"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736654","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

Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data 用于时空交通数据的自关注图卷积推算网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3461735

Xiulan Wei;Yong Zhang;Shaofan Wang;Xia Zhao;Yongli Hu;Baocai Yin

{"title":"Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data","authors":"Xiulan Wei;Yong Zhang;Shaofan Wang;Xia Zhao;Yongli Hu;Baocai Yin","doi":"10.1109/TITS.2024.3461735","DOIUrl":"https://doi.org/10.1109/TITS.2024.3461735","url":null,"abstract":"Missing data in time series is a pervasive problem that serves as obstacles for subsequent traffic data analysis. Consequently, extensive research works have been conducted on traffic missing data imputation tasks. The state-of-the-art traffic data imputation models are mostly based on recurrent neural networks. However, these methods belong to autoregressive models which are highly susceptible to error propagation. The attention-based methods are non-autoregressive models that can avoid compounding errors and help achieve better imputation quality. Moreover, the attention-based methods in now widely applied and have achieved remarkable results, whereas their application on traffic data imputation is still limited. Thus, this paper proposes Self-Attention Graph Convolution Imputation Network (SAGCIN) for spatio-temporal traffic data. To ensure the accuracy of data imputation, it is necessary to fully capture the spatio-temporal contextual information of traffic data to impute missing values. To this end, the SAGCIN model incorporates self-attention mechanism with diffusion graph convolution network. The SAGCIN model consists of two spatio-temporal blocks with a spatio-temporal encoder and an imputation decoder. The encoder learns spatio-temporal representations specialized for traffic data imputation tasks. Based on the learned representation, the decoder performs two stages of imputation operator for missing data. A joint-optimization training approach of imputation and reconstruction is introduced for SAGCIN to perform missing value imputation for traffic data. Empirical results demonstrate that SAGCIN model outperforms state-of-the-art methods in imputation tasks on relevant real-world benchmarks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19549-19562"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736229","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

Inter-Intra Cluster Reorganization for Unsupervised Vehicle Re-Identification 用于无监督车辆再识别的内部集群间重组

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3464585

Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu

{"title":"Inter-Intra Cluster Reorganization for Unsupervised Vehicle Re-Identification","authors":"Mingkai Qiu;Yuhuan Lu;Xiying Li;Qiang Lu","doi":"10.1109/TITS.2024.3464585","DOIUrl":"https://doi.org/10.1109/TITS.2024.3464585","url":null,"abstract":"State-of-the-art unsupervised object re-identification (Re-ID) methods conduct model training with pseudo labels generated by clustering techniques. Unfortunately, due to the existence of inter-ID similarity and intra-ID variance problems in vehicle Re-ID, clustering sometimes mixes different similar vehicles together or splits images of the same vehicle in different views into different clusters. To enhance the model’s ID discrimination capability in the presence of such kinds of label noise, we propose an inter-intra cluster reorganization approach (ICR) to reorganize the relationship between instances within and between clusters, which can provide higher-quality contrastive learning guidance based on existing clustering results. In the intra-cluster reorganization, we design a camera-aware maximum reliability sub-cluster organization approach, which reorganizes each cluster into several intersecting sub-clusters of higher quality based on the finer intra-camera clustering results. We further design a novel metric called centroid reliability to measure the reliability of intra-cluster contrastive learning. In the inter-cluster reorganization, we propose an ambiguous cluster discrimination criterion to measure the probability that two clusters belong to the same vehicle. Based on this criterion, we design a focal contrastive loss to adaptively re-organize the contribution of ambiguous clusters in model training to perform better contrastive learning. Extensive experiments on VeRi-776 and VERI-Wild demonstrate that ICR is effective and can achieve state-of-the-art performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20493-20507"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736275","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

Structure Synchronized Dynamic Event-Triggered Control for Marine Ranching AMVs via the Multi-Task Switching Guidance 通过多任务切换制导实现海洋牧场 AMV 的结构同步动态事件触发控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3463181

Guoqing Zhang;Shilin Yin;Chenfeng Huang;Weidong Zhang;Jiqiang Li

{"title":"Structure Synchronized Dynamic Event-Triggered Control for Marine Ranching AMVs via the Multi-Task Switching Guidance","authors":"Guoqing Zhang;Shilin Yin;Chenfeng Huang;Weidong Zhang;Jiqiang Li","doi":"10.1109/TITS.2024.3463181","DOIUrl":"https://doi.org/10.1109/TITS.2024.3463181","url":null,"abstract":"To improve the autonomy of marine ranching operations, this paper addresses the cooperative formation control and multi-task switching problem of ranch autonomous marine vehicles (AMVs) with the structure synchronized dynamic event-triggered mechanism (DETM). In the proposed algorithm, adaptive potential ship (APS) technique is adopted to guarantee the integrity and continuity of the guidance signal. Combined with the guidance principle, a cooperative formation control algorithm is proposed by employing the DETM and neural networks (NNs). The communication burden in the channel from the sensor to the controller and from the controller to actuator has been reduced for the merits of the proposed DETM. Unlike the existing results, the proposed DETM can activate the threshold parameters, adaptive parameters and NNs weight estimators at the triggering times synchronously. This releases the computation burden greatly. Considerable effort has been made to guarantee the semi-globally uniformly ultimately bounded (SGUUB) stability via the Lyapunov theorem. Finally, two simulations consist of the marine ranching path following and comparative example are carried out to evaluate the advantages of the proposed strategy.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20295-20308"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736698","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

Cross-View Location Alignment Enhanced Spatial-Topological Aware Dual Transformer for Travel Time Estimation 用于旅行时间估算的跨视图位置对齐增强型空间-拓扑感知双变换器

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3463501

Hanyuan Zhang;Xinyu Zhang;Qize Jiang;Liang Li;Baihua Zheng;Weiwei Sun

引用次数: 0

FrictionSegNet: Simultaneous Semantic Segmentation and Friction Estimation Using Hierarchical Latent Variable Models FrictionSegNet：利用层次潜变量模型同时进行语义分割和摩擦力估算

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-04 DOI: 10.1109/TITS.2024.3463952

Mohammad Otoofi;Leo Laine;Leon Henderson;William J. B. Midgley;Laura Justham;James Fleming

{"title":"FrictionSegNet: Simultaneous Semantic Segmentation and Friction Estimation Using Hierarchical Latent Variable Models","authors":"Mohammad Otoofi;Leo Laine;Leon Henderson;William J. B. Midgley;Laura Justham;James Fleming","doi":"10.1109/TITS.2024.3463952","DOIUrl":"https://doi.org/10.1109/TITS.2024.3463952","url":null,"abstract":"This paper presents an end-to-end approach, named FrictionSegNet, for jointly estimating tyre-road friction coefficient and identifying road surfaces in real time from on board camera data. FrictionSegNet combines semantic segmentation and friction estimation by learning a shared latent space that encompasses both semantic segmentation and friction coefficient information. An objective function is designed for this task and minimised using *geco to train the model, providing the ability to control the balance between improved predictions and uncertainty measurement. To the best of our knowledge, this study is the first attempt to jointly estimate tyre-road friction and surface type by learning the joint latent space of semantic segmentation and friction coefficient information. The results suggest that it is possible to identify low-friction surfaces, e.g. snow or ice, and estimate upcoming road friction in real time from a camera only. As it is of interest to develop techniques that require less training data, numerical experiments were performed using transfer learning from a dataset consisting of images of various road surfaces. This led to better performance and faster convergence during training. FrictionSegNet achieved per-pixel accuracies of 97% and 95% when identifying snow and ice respectively, and RMS errors of 0.04–0.09 when estimating \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000 values achievable by a truck *abs on gravel, dry and wet asphalt, snow, and ice surfaces.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19785-19795"},"PeriodicalIF":7.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736633","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