IEEE Transactions on Intelligent Transportation Systems最新文献_第9页

Smart Battery Swapping Control for an Electric Motorcycle Fleet With Peak Time Based on Deep Reinforcement Learning 基于深度强化学习的峰值时间电动摩托车队智能电池更换控制

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3469110

YoonShik Park;Seungdon Zu;Chi Xie;Hyunwoo Lee;Taesu Cheong;Qing-Chang Lu;Meng Xu

{"title":"Smart Battery Swapping Control for an Electric Motorcycle Fleet With Peak Time Based on Deep Reinforcement Learning","authors":"YoonShik Park;Seungdon Zu;Chi Xie;Hyunwoo Lee;Taesu Cheong;Qing-Chang Lu;Meng Xu","doi":"10.1109/TITS.2024.3469110","DOIUrl":"https://doi.org/10.1109/TITS.2024.3469110","url":null,"abstract":"This study proposes a deep Q-network (DQN) model for electric motorcycles (EMs) and a multi-agent reinforcement learning (MARL)-based central control system to support battery swapping decision-making in the delivery business. We aim to minimize expected delivery losses, especially in scenarios where delivery requests are randomly and independently generated for each EM, with fluctuating time distributions and limited BSS capacity. Our MARL benefits from a reservation mechanism and a profit-aggregated central system, which greatly reduces the complexity of MARL. Furthermore, to address the inherent non-stationary problems of MARL, we propose a decentralized agent-based MARL framework, named Decentralized Agents, Centralized Learning Deep Q Network. This framework, leveraging a tailored learning algorithm, achieves peak-averse behavior, reducing delivery losses. Additionally, we introduce a hybrid approach that combines the resulting DQN algorithm for determining when to visit the BSS, and a greedy algorithm for deciding which BSS to visit. Computational experiments using real-world delivery data are conducted to evaluate the performance of our algorithm. The results demonstrate that the hybrid approach maximizes the overall profit of the entire EM fleet in a challenging environment with limited BSS capacity.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20175-20189"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736673","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

Spatial-Temporal Traffic Prediction With an Interactive Spatial-Enhanced Graph Convolutional Network Model

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467172

Qin Li;Pai Xu;Xuan Yang;Yuankai Wu;Hongwen He;Deqiang He

{"title":"Spatial-Temporal Traffic Prediction With an Interactive Spatial-Enhanced Graph Convolutional Network Model","authors":"Qin Li;Pai Xu;Xuan Yang;Yuankai Wu;Hongwen He;Deqiang He","doi":"10.1109/TITS.2024.3467172","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467172","url":null,"abstract":"Accurate traffic prediction is crucial for effective traffic control and risk assessment. Traffic data exhibits a distinct nature, characterized by the interplay of swift, sudden short-term variations and enduring, extended long-term trends within specific regions. This intricate intermingling and interaction give rise to diverse spatial propagation patterns. Successful traffic prediction models necessitate mastering multi-scale temporal and dynamic spatial correlations, as well as their intricate interrelationships. In this study, we present a novel spatial-temporal traffic prediction framework named \u0000<underline>I\u0000nteractive \u0000<underline>S\u0000patial-Enhanced \u0000<underline>G\u0000raph \u0000<underline>C\u0000onvolution \u0000<underline>N\u0000etwork (ISGCN). Our key innovation lies in the introduction of a novel dynamic graph convolution module, which not only captures overarching spatial correlations but also unveils the concealed evolution of dynamic spatial correlations over time. By seamlessly integrating the graph convolutional module with temporal sample convolution and interaction blocks, we adeptly bridge multi-scale temporal correlations with the acquired dynamic spatial correlations. Additionally, we harness diverse temporal granularities data to comprehensively capture global temporal correlations. Experiments conducted on four real-world traffic datasets illustrate that ISGCN outperforms diverse types of state-of-the-art baseline models.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20767-20778"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757848","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 Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic 用于高效学习和准确交通预测的动态时空直流网络

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3443887

Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang

{"title":"Dynamic Spatiotemporal Straight-Flow Network for Efficient Learning and Accurate Forecasting in Traffic","authors":"Canyang Guo;Feng-Jang Hwang;Chi-Hua Chen;Ching-Chun Chang;Chin-Chen Chang","doi":"10.1109/TITS.2024.3443887","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443887","url":null,"abstract":"To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"18899-18912"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579171","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

Multimodal-XAD: Explainable Autonomous Driving Based on Multimodal Environment Descriptions 多模态-XAD：基于多模态环境描述的可解释自主驾驶

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467175

Yuchao Feng;Zhen Feng;Wei Hua;Yuxiang Sun

{"title":"Multimodal-XAD: Explainable Autonomous Driving Based on Multimodal Environment Descriptions","authors":"Yuchao Feng;Zhen Feng;Wei Hua;Yuxiang Sun","doi":"10.1109/TITS.2024.3467175","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467175","url":null,"abstract":"In recent years, deep learning-based end-to-end autonomous driving has become increasingly popular. However, deep neural networks are like black boxes. Their outputs are generally not explainable, making them not reliable to be used in real-world environments. To provide a solution to this problem, we propose an explainable deep neural network that jointly predicts driving actions and multimodal environment descriptions of traffic scenes, including bird-eye-view (BEV) maps and natural-language environment descriptions. In this network, both the context information from BEV perception and the local information from semantic perception are considered before producing the driving actions and natural-language environment descriptions. To evaluate our network, we build a new dataset with hand-labelled ground truth for driving actions and multimodal environment descriptions. Experimental results show that the combination of context information and local information enhances the prediction performance of driving action and environment description, thereby improving the safety and explainability of our end-to-end autonomous driving network.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19469-19481"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736638","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

Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving 探索基于多样性的主动学习，用于自动驾驶中的 3D 物体检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3463801

Jinpeng Lin;Zhihao Liang;Shengheng Deng;Lile Cai;Tao Jiang;Tianrui Li;Kui Jia;Xun Xu

{"title":"Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving","authors":"Jinpeng Lin;Zhihao Liang;Shengheng Deng;Lile Cai;Tao Jiang;Tianrui Li;Kui Jia;Xun Xu","doi":"10.1109/TITS.2024.3463801","DOIUrl":"https://doi.org/10.1109/TITS.2024.3463801","url":null,"abstract":"3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV). The success of deep learning based object detectors relies on the availability of large-scale annotated datasets, which is time-consuming and expensive to compile, especially for 3D bounding box annotation. In this work, we investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. Given limited annotation budget, only the most informative frames and objects are automatically selected for human to annotate. Technically, we take the advantage of the multimodal information provided in an AV dataset, and propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We benchmark the proposed method against other AL strategies under realistic annotation cost measurements, where the realistic costs for annotating a frame and a 3D bounding box are both taken into consideration. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15454-15466"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579170","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

An Adaptive Memetic Algorithm for a Cost-Optimal Electric Vehicle-Drone Routing Problem 电动汽车-无人机成本最优路由问题的自适应记忆算法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467219

Setyo Tri Windras Mara;Ruhul Sarker;Daryl Essam;Saber Elsayed

引用次数: 0

FedShip: Federated Over-the-Air Learning for Communication-Efficient and Privacy-Aware Smart Shipping in 6G Communications FedShip：在 6G 通信中，为实现通信效率高、注重隐私的智能航运而进行的联合空中学习

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3468383

Anastasios E. Giannopoulos;Sotirios T. Spantideas;Menelaos Zetas;Nikolaos Nomikos;Panagiotis Trakadas

{"title":"FedShip: Federated Over-the-Air Learning for Communication-Efficient and Privacy-Aware Smart Shipping in 6G Communications","authors":"Anastasios E. Giannopoulos;Sotirios T. Spantideas;Menelaos Zetas;Nikolaos Nomikos;Panagiotis Trakadas","doi":"10.1109/TITS.2024.3468383","DOIUrl":"https://doi.org/10.1109/TITS.2024.3468383","url":null,"abstract":"Maritime and shipping are unambiguously the cornerstones of the global economy and transportation. To improve efficiency, maritime sector activities are focused on the realization of Smart Shipping (SMS), leveraging 6G Communications, Energy Efficiency (EE) and Machine Learning (ML). However, conventional Centralized Machine Learning (CML) cannot be easily applied in the maritime, mainly due to the drawbacks: (i) prohibitive data communication overhead and bandwidth limitations, since CML requires centralization of massive data through transmissions from heterogeneous sources, (ii) excessive energy consumption associated with massive data transfers, (iii) remarkable transmission errors due to harsh propagation conditions, and (iv) data privacy violation, since the data carries sensitive and commercial information. This article proposes a two-fold Federated Learning (FL) scheme (FedShip) to improve the privacy, EE and communication-efficiency of future 6G maritime networks. FedShip uses the Over-the-Air computation (AirComp) principles to exploit the signal superposition property and ensure that local models are accurately and efficiently combined. Using real data regarding the fuel consumption of multiple cargo ships, we compared the FL performance, building multiple timeseries forecasting models, with collaborative ML baselines. AirComp performance was also assessed using simulation data about channel measurements. After optimizing the hyperparameters of the local models, extensive results revealed that: (i) FL shows enhanced fuel prediction accuracy (95.5% relative to the CML), while ensuring data privacy and (ii) AirComp can be adopted to combine the local models with low computation error, offering significant EE and spectrum efficiency improvements, especially when dense 6G scenarios are considered.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19873-19888"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736596","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

SpikeTOD: A Biologically Interpretable Spike-Driven Object Detection in Challenging Traffic Scenarios SpikeTOD：在充满挑战的交通场景中进行生物可解释的尖峰目标检测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3468038

Junfan Wang;Yi Chen;Xiaoyue Ji;Zhekang Dong;Mingyu Gao;Zhiwei He

{"title":"SpikeTOD: A Biologically Interpretable Spike-Driven Object Detection in Challenging Traffic Scenarios","authors":"Junfan Wang;Yi Chen;Xiaoyue Ji;Zhekang Dong;Mingyu Gao;Zhiwei He","doi":"10.1109/TITS.2024.3468038","DOIUrl":"https://doi.org/10.1109/TITS.2024.3468038","url":null,"abstract":"Artificial neural networks (ANN) have shown remarkable performance in intelligent transportation systems (ITS), especially for the traffic object detection. However, as the ITS is applied to a wider range of traffic scenarios, the increasing demand for the trade-off between detection performance and power resources has become inevitable. A biologically interpretable spike-driven traffic object detector for challenging scenarios is proposed in this paper, named SpikeTOD, achieving the trade-off between the accuracy and power consumption. Firstly, the spike neural network (SNN) is employed to realize energy-efficient object detection in traffic scenarios. And a local modulation-based integrate-and-fire (IF) neuron is designed, which provides an efficient way to convert the traffic detection model from ANN to SNN. Secondly, a biology-inspired detail-guided context-aware network (DCNet) is proposed to improve the detection performance. The integration of detail coherence and global priors is leveraged to selectively emphasize object features and improve the detection capabilities within challenging conditions. As far as we know, this is the first application of SNN in traffic object detection tasks. SpikeTOD achieved a mAP@50 of 46.11% on the BDD100K dataset with a power consumption of 4.73E-03J, demonstrating a more efficient trade-off in detection accuracy and power consumption. Notably, SpikeTOD maintained an average missed detection rate of 44.56%, further contributing to its overall efficacy in traffic object detection. Further, we conducted on road test by deploying SpikeTOD on Jetson Xavier NX and Loihi to demonstrate that model achieves a better balance between accuracy and power consumption.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"21297-21314"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736623","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 Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System 基于异构图卷积的地铁系统短期外径流量完成与预测方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467094

Jiexia Ye;Juanjuan Zhao;Furong Zheng;Chengzhong Xu

{"title":"A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow Completion and Prediction in a Metro System","authors":"Jiexia Ye;Juanjuan Zhao;Furong Zheng;Chengzhong Xu","doi":"10.1109/TITS.2024.3467094","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467094","url":null,"abstract":"Short-term OD flow (i.e. the number of passenger traveling between stations) prediction is crucial to traffic management in metro systems. The delayed effect in latest complete OD flow collection and complex spatiotemporal correlations of OD flows in high dimension make it challengeable to predict short-term OD flow. Existing methods need to be improved due to not fully utilizing the real-time passenger mobility data and not sufficiently modeling the implicit correlation of the mobility patterns between stations. In this paper, we propose a Completion based Adaptive Heterogeneous Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in two aspects. The first is to model real-time mobility evolution by establishing the implicit correlation between observed OD flows and the prediction target OD flows in high dimension based on a key data-driven insight: the destination distributions of the passengers departing from a station are correlated with other stations sharing similar attributes (e.g. geographical location, region function). The second is to complete the latest incomplete OD flows by estimating the destination distribution of unfinished trips through considering the real-time mobility evolution and the time cost between stations, which is the base of time series prediction and can improve the model’s dynamic adaptability. Extensive experiments on two real world metro datasets demonstrate the superiority of our model over other competitors with the biggest model performance improvement being nearly 4%. In addition, the data complete framework we propose can be integrated into other models to improve their performance up to 2.1%.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15614-15627"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579180","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 Path and Pick-Up Design for Connectivity-Aware UAV-Enabled Multi-Package Delivery 为具有连接感知能力的无人机多包装投递设计联合路径和拾取装置

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-10-07 DOI: 10.1109/TITS.2024.3467329

Bin Duo;Aoqi Kong;Qingqing Wu;Xiaojun Yuan;Yonghui Li

{"title":"Joint Path and Pick-Up Design for Connectivity-Aware UAV-Enabled Multi-Package Delivery","authors":"Bin Duo;Aoqi Kong;Qingqing Wu;Xiaojun Yuan;Yonghui Li","doi":"10.1109/TITS.2024.3467329","DOIUrl":"https://doi.org/10.1109/TITS.2024.3467329","url":null,"abstract":"This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20017-20031"},"PeriodicalIF":7.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736681","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