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

Uncovering Passenger-Seeking Behavior of Vacant Taxis From Trajectory Data via Self-Supervised Deep Spectral Clustering

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3546293

Xinlian Yu;Mingzhuang Hua;Jingxu Chen;Jiankun Peng;Haijun Mao

{"title":"Uncovering Passenger-Seeking Behavior of Vacant Taxis From Trajectory Data via Self-Supervised Deep Spectral Clustering","authors":"Xinlian Yu;Mingzhuang Hua;Jingxu Chen;Jiankun Peng;Haijun Mao","doi":"10.1109/TITS.2025.3546293","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546293","url":null,"abstract":"Knowledge of vacant taxis’ passenger-searching behavior is of great social and economic interest to multiple applications, particularly in planning and operating on-demand mobility services. The inherent stochasticity and dynamic nature in both passenger demand and drivers’ decision-making pose challenges in capturing vacant taxis movements. This study proposes a novel deep clustering framework to comprehensively uncover passenger-searching strategies from extensive trajectory data. Specifically, multiple features from vacant searching trips are extracted and analogously defined as multi-channel images, where each channel corresponds to a specific feature. A novel deep image clustering approach is then proposed, integrating a feature representation module utilizing convolutional neural networks (ConvNets), a self-expression module for affinity learning, a spectral clustering module, and a classification module for self-supervision. An effective training procedure is also presented for the proposed deep clustering framework. Experiments demonstrate the effectiveness of the proposed approach against benchmark methods. Based on the clustering results, common and specific passenger searching strategies are further revealed. Specifically, our findings highlight the importance of individual’s contextual experience in explaining searching behavior and operational efficiency. Moreover, drivers cruising without clear searching strategy often exhibit lower performance, and some drivers may gamble with peers to increase their chances of picking up passengers. These results deliver important justifications for future studies and provide managerial implications to improve on-demand mobility.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5307-5321"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726383","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

Study on Unsafe Behavior Detection of Tower Crane Drivers in Prefabricated Building Construction

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3546232

Fuwang Wang;Hao Wang;Rongrong Fu;Xiaolei Zhang

{"title":"Study on Unsafe Behavior Detection of Tower Crane Drivers in Prefabricated Building Construction","authors":"Fuwang Wang;Hao Wang;Rongrong Fu;Xiaolei Zhang","doi":"10.1109/TITS.2025.3546232","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546232","url":null,"abstract":"Considering the significant safety incidents arising from the fatigue-induced operations of tower crane drivers in prefabricated building construction, this study proposed the utilization of a new portable semi-dry electrode for real-time monitoring of the cognitive fatigue levels in tower crane drivers, aiming to mitigate instances of unsafe operational conduct. This type of semi-dry electrode possesses the merits of facile placement and low impedance, rendering it conducive for application in construction settings. Furthermore, the study applied Multifractal Detrended Fluctuation Analysis (MF-DFA) to analyze the electroencephalogram (EEG) features of tower crane operators. Evaluation and comparison using the traditional wet electrode and the new semi-dry electrode to extract the EEG features show that the performance of the novel semi-dry electrode closely approximates that of the traditional wet electrode, effectively tracking alterations in the cognitive fatigue indicators among tower crane drivers during prefabricated building construction. These findings contribute to the detection of the mental state of tower crane drivers and the identification of physiological characteristics associated with unsafe conduct in the actual process of prefabricated construction, thereby enhancing construction safety.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4406-4417"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735400","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

Enhancing Federated Learning in Connected and Autonomous Vehicles Through Cost Optimization and Advanced Model Selection

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3546088

Xuelian Cai;Pincan Zhao;Sha Liu;Yuchuan Fu;Changle Li;Fei Richard Yu

{"title":"Enhancing Federated Learning in Connected and Autonomous Vehicles Through Cost Optimization and Advanced Model Selection","authors":"Xuelian Cai;Pincan Zhao;Sha Liu;Yuchuan Fu;Changle Li;Fei Richard Yu","doi":"10.1109/TITS.2025.3546088","DOIUrl":"https://doi.org/10.1109/TITS.2025.3546088","url":null,"abstract":"With the rapid evolution of vehicular network technology, the integration of Machine Learning (ML) with Connected and Autonomous Vehicles (CAVs) presents both remarkable opportunities and formidable challenges. This paper addresses the crucial need for efficient ML model training in the context of Federated Learning (FL) within vehicular networks. Recognizing the limitations imposed by the tradeoff between the high energy cost at the local level with the performance problem at the global level, we propose an innovative approach that harmonizes cost optimization with strategic model selection. Our strategy primarily focuses on optimizing energy consumption during model training and updating at the vehicle end, thereby resolving the prevalent issue of limited end-user participation in FL due to high energy demands. Additionally, we introduce an advanced model selection method, prioritizing local model uploads and adaptively allocating bandwidth to clients with more extensive training data. This method enhances the efficiency and reliability of model updates, ensuring robust global model performance. We validate our approach through extensive simulations, demonstrating not only improved learning performance but also a significant reduction in energy consumption among participating clients.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5276-5289"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724050","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

GEOcc: Geometrically Enhanced 3D Occupancy Network With Implicit-Explicit Depth Fusion and Contextual Self-Supervision

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3539627

Xin Tan;Wenbin Wu;Zhiwei Zhang;Chaojie Fan;Yong Peng;Zhizhong Zhang;Yuan Xie;Lizhuang Ma

{"title":"GEOcc: Geometrically Enhanced 3D Occupancy Network With Implicit-Explicit Depth Fusion and Contextual Self-Supervision","authors":"Xin Tan;Wenbin Wu;Zhiwei Zhang;Chaojie Fan;Yong Peng;Zhizhong Zhang;Yuan Xie;Lizhuang Ma","doi":"10.1109/TITS.2025.3539627","DOIUrl":"https://doi.org/10.1109/TITS.2025.3539627","url":null,"abstract":"3D occupancy perception holds a pivotal role in recent vision-centric autonomous driving systems by converting surround-view images into integrated geometric and semantic representations within dense 3D grids. Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the lack of generalizability issues due to sparse LiDAR supervision. To address these issues, this paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception. Our approach is three-fold: 1) Integration of explicit lift-based depth prediction and implicit projection-based transformers for depth modeling, enhancing the density and robustness of view transformation. 2) Utilization of mask-based encoder-decoder architecture for fine-grained semantic predictions; 3) Adoption of context-aware self-training loss functions in the pertaining stage to complement LiDAR supervision, involving the re-rendering of 2D depth maps from 3D occupancy features and leveraging image reconstruction loss to obtain denser depth supervision besides sparse LiDAR ground-truths. Our approach achieves State-of-the-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone compared with current models, marking an improvement of 3.3% due to our proposed contributions. Comprehensive experimentation also demonstrates the consistent superiority of our method over baselines and alternative approaches. Our code is available at <uri>https://github.com/world-executed/GEOcc.git</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5613-5623"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725108","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

Automatic eCall in Powered Two-Wheelers: A Dynamics-Based Approach

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-10 DOI: 10.1109/TITS.2025.3545907

Jessica Leoni;Simone Gelmini;Giulio Panzani;Mara Tanelli;Sergio Matteo Savaresi

{"title":"Automatic eCall in Powered Two-Wheelers: A Dynamics-Based Approach","authors":"Jessica Leoni;Simone Gelmini;Giulio Panzani;Mara Tanelli;Sergio Matteo Savaresi","doi":"10.1109/TITS.2025.3545907","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545907","url":null,"abstract":"Powered two-wheelers exhibit complex dynamics, primarily due to their broader range of possible attitude configurations compared to four-wheelers. This is especially true when considering accident scenarios, in which the dynamics can be affected by both high-frequency events, such as in high-sides, marked by rapid variations of the characteristics signals, and low-frequency events, such as in side slips, which exhibit slower and monotonic change in the same quantities. Therefore, designing an effective automatic eCall system for this type of vehicle is particularly challenging, while at the same time, it can significantly increase safety in dangerous situations. This paper proposed a novel approach for detecting falls of two-wheeled vehicles that avoids missed detections while minimizing the number of false alarms, which would heavily undermine its usability. We show, based on extensive analysis of a wide range of experimental data, why and how the proposed approach allows us to overcome the limitations of the existing proposal, ensuring a consistent detection of the wide range of falls that can happen on two-wheelers, also providing a detailed comparison with the scientific literature.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4365-4379"},"PeriodicalIF":7.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735365","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

Adjustment of Energy-Saving Train Operations Based on Synergies of All Trains in the Same Power Supply Area

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-05 DOI: 10.1109/TITS.2025.3545480

Xiangmeng Jiao;Yonghua Zhou;Hamido Fujita

{"title":"Adjustment of Energy-Saving Train Operations Based on Synergies of All Trains in the Same Power Supply Area","authors":"Xiangmeng Jiao;Yonghua Zhou;Hamido Fujita","doi":"10.1109/TITS.2025.3545480","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545480","url":null,"abstract":"As a major use of electricity, the energy efficiency of urban railways is of great concern. To reduce the operational energy consumption of a metro line, this paper proposes a two-stage optimization model for energy-efficient train operations based on three aspects: train operation strategies at inter-stations, running time allocation within an operating cycle, and utilization of regenerative braking energy of multiple collaborative trains in the same power supply area. The first stage is based on a multi-objective optimization algorithm, which optimizes the position-speed curves at each inter-station to obtain a series of energy-saving running curves and the corresponding Pareto fronts. In the second stage, a single-objective optimization algorithm and parallel computing method are used to solve the integrated optimization model for train operations with the minimum net energy consumption as the objective function, to reduce the net energy consumption in an operating cycle. Finally, numerical experiments are carried out with data on the Beijing Yizhuang line. The results show that the proposed bi-level programming model and solution methods have a good energy-saving effect.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4606-4620"},"PeriodicalIF":7.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726457","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

Cybersecurity in Digital Twins of Electric Vehicle’s LIBs: Unveiling a Robust TTB-GA Attack

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-05 DOI: 10.1109/TITS.2025.3545782

Mitra Pooyandeh;Huaping Liu;Insoo Sohn

{"title":"Cybersecurity in Digital Twins of Electric Vehicle’s LIBs: Unveiling a Robust TTB-GA Attack","authors":"Mitra Pooyandeh;Huaping Liu;Insoo Sohn","doi":"10.1109/TITS.2025.3545782","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545782","url":null,"abstract":"Virtual replicas of physical systems, known as Digital Twins (DT), can offer innovative solutions for optimizing and forecasting battery management systems (BMS). However, their security remains a major concern. A new type of attack called Time Tampering Black-Box Attack Genetic Algorithm (TTB-GA) is introduced in this paper to study security in DT Intelligent Transportation Systems (DT-ITS). TTB-GA exploits the sensitivity of time series data and effectively deceives prediction models by altering input data’s timing within realistic ranges. To enhance the efficiency of locating and querying, customized operators such as mutation and fitness are designed within the GA-based search framework. Our attack achieves a remarkable success rate of 98% for Long short-term memory (LSTM) and 96% for Gated Recurrent Unit (GRU) models, exposing a critical vulnerability in digital twin security. Furthermore, we demonstrate the limitations of a distributed detection scheme combining an Autoencoder, a Convolutional Neural Network (CNN), and an Extended Kalman Filter (EKF), emphasizing the need for a robust and adaptive defenses. By exposing a novel and highly effective attack method (TTB-GA) targeting temporal vulnerabilities in time series data, and emphasizing the limitations of existing defense mechanisms against such attacks, our research significantly contributes to digital twin security.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5360-5381"},"PeriodicalIF":7.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726530","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 Tailored Two-Stage Algorithm for Quay Crane and Automated Guided Vehicle Scheduling Problems

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-05 DOI: 10.1109/TITS.2025.3545433

Zehao Wang;Qingcheng Zeng;Baoli Liu;Chenrui Qu;He Wang

{"title":"A Tailored Two-Stage Algorithm for Quay Crane and Automated Guided Vehicle Scheduling Problems","authors":"Zehao Wang;Qingcheng Zeng;Baoli Liu;Chenrui Qu;He Wang","doi":"10.1109/TITS.2025.3545433","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545433","url":null,"abstract":"The integrated scheduling problem of cranes and automated guided vehicles (AGVs) in automated container terminals is a crucial area of concern for ports. In the terminal with AGV-supports in the yard, AGVs can autonomously place or pick up containers without waiting for yard cranes. Therefore, in such a terminal, meticulous scheduling and coordination between quay cranes (QCs) and AGVs are core for efficient and orderly operations. However, managing the operation of QCs and AGVs is complex as numerous factors affect the operational performance, such as QC interference, vehicle congestion, and limited capacity of handover points. To address the problem, we formulate a mixed-integer linear programming model that explicitly considers the above realistic factors. As the model is computationally inefficient even for small-scale instances, we develop a tailored two-stage algorithm, where the first stage is branch-and-bound for QC operations and the second is column generation for AGV operations. To validate the solution quality, we compare the proposed algorithm with some benchmark methods, and the numerical experiments confirm the effectiveness of the proposed approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5049-5066"},"PeriodicalIF":7.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740260","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

Adaptive Feature-Manipulated Vehicle and Pedestrian Detection in Infrared Images

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-05 DOI: 10.1109/TITS.2025.3545844

Guangchen Chen;Pengcheng Zhang;Yinhui Zhang;Zifen He;Benjie Shi

{"title":"Adaptive Feature-Manipulated Vehicle and Pedestrian Detection in Infrared Images","authors":"Guangchen Chen;Pengcheng Zhang;Yinhui Zhang;Zifen He;Benjie Shi","doi":"10.1109/TITS.2025.3545844","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545844","url":null,"abstract":"Infrared imaging is widely applied in assisted driving systems to enable night vision for sensitive targets such as vehicles and pedestrians. However, the detection accuracy of these targets is limited on lightweight detection networks due to their coarse color and texture characteristics rendered in infrared images. To solve this problem, we propose an adaptive feature-manipulated network (AFMNet) for accurate vehicle and pedestrian detection in infrared images. First, a refined spatial pooling module that uses one-dimensional convolution is proposed to establish local and channel feature mapping under different receptive fields so that different fine-grained features are fused. Second, the shuffle manipulation module is designed which includes slicing and shuffling to manipulate the spatial and channel features so that the information loss problem caused by conventional convolutional downsampling is overcome. Third, the adaptive connection of features at different scales using learnable parameters is proposed, and then the target features are reinforced by location and channel calibration branches. The experimental results show that AFMNet achieves the best performance in terms of average detection accuracy of 86.4%, model size of 5.3MB, and detection speed of 48FPS on GTX 2080Ti.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4579-4591"},"PeriodicalIF":7.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726353","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

Parallel Heterogeneous Networks With Adaptive Routing for Online Video Lane Detection

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1109/TITS.2025.3545621

Zhengyun Cheng;Changhao Wang;Guanwen Zhang;Wei Zhou

{"title":"Parallel Heterogeneous Networks With Adaptive Routing for Online Video Lane Detection","authors":"Zhengyun Cheng;Changhao Wang;Guanwen Zhang;Wei Zhou","doi":"10.1109/TITS.2025.3545621","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545621","url":null,"abstract":"Lane detection plays a critical role in the field of autonomous driving. Most previous lane detection methods focus exclusively on analyzing individual images and overlook the inter-frame dynamics, while car-mounted cameras capture continuous streams amenable to leveraging intra-video context. In challenging scenes with blur, occlusion, or illumination variations, considering visible lanes from previous frames can aid current frame interpretation. To tackle above challenges, we introduce a parallel heterogeneous framework, called PHNet, for video lane detection. First, a novel router automatically analyzes multi-level features to determine the inference route of candidates with different visual cues. Then, a cross-frame attention mechanism leverages relationships between current candidates and positive embeddings from past frame to aggregate contextual cues. Unlike existing offline video lane detection methods, which face limitations in handling long video sequences and real-time video streams due to computational constraints, our proposed method operates as an online framework capable of processing clips of any length. Our method demonstrates robust lane detection through temporally association modeling and efficient online inference. The extensive experiments on public benchmark show that PHNet has superior performance versus state-of-the-art video lane detection methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5225-5235"},"PeriodicalIF":7.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740330","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