IEEE Open Journal of Intelligent Transportation Systems最新文献_第7页

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics 用于客户车队分析的损失感知直方图分选和主成分分析技术

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-15 DOI: 10.1109/OJITS.2024.3366279

Kunxiong Ling;Jan Thiele;Thomas Setzer

{"title":"Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics","authors":"Kunxiong Ling;Jan Thiele;Thomas Setzer","doi":"10.1109/OJITS.2024.3366279","DOIUrl":"10.1109/OJITS.2024.3366279","url":null,"abstract":"We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarser-grained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss. Although information loss with each step is well understood, little guidance exists on the overall information loss when conducting both steps sequentially. We use Monte Carlo simulations to regress information loss on the number of bins and PCs, given few parameters of a dataset related to its scale and correlation structure. A sensitivity study shows that information loss can be approximated well given sufficiently large datasets. Using the number of bins, PCs, and two correlation measures, we derive an empirical loss model with high accuracy. Furthermore, we demonstrate the benefits of estimating information losses and the representativeness of total loss in evaluating the accuracy of k-means clustering for a real-world customer fleet dataset. For preprocessing sensor data which are aggregated from sufficient number of samples, continuously distributed, and can be represented by Beta-distributions, we recommend not to coarsen the histogram binning before PCA.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"160-173"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10437985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV 小视场角多基础设施 RGB-D 摄像机网络的外在校准方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-02 DOI: 10.1109/OJITS.2024.3361842

He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming

{"title":"An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV","authors":"He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming","doi":"10.1109/OJITS.2024.3361842","DOIUrl":"10.1109/OJITS.2024.3361842","url":null,"abstract":"Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras’ extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"617-628"},"PeriodicalIF":4.6,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing Rare Outages in C-V2X With Time-Controlled One-Shot Resource Scheduling 利用受时间控制的一次性资源调度解决 C-V2X 中的罕见中断问题

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-02 DOI: 10.1109/OJITS.2024.3361473

Md Saifuddin;Mahdi Zaman;Yaser P. Fallah;Jayanthi Rao

引用次数: 0

Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment 在车载网络环境中使用机器学习技术和 HTM 算法进行故障预测和恢复

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-18 DOI: 10.1109/OJITS.2023.3347484

Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri

{"title":"Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment","authors":"Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri","doi":"10.1109/OJITS.2023.3347484","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3347484","url":null,"abstract":"The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"132-145"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2024 Editorial IEEE Open Journal of Intelligent Transportation Systems 2024 编辑 IEEE 智能交通系统开放式期刊

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2023.3348988

Jiaqi Ma

引用次数: 0

Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling 通过基于图形的时空建模分析共享单车使用情况

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2024.3350213

Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree

{"title":"Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling","authors":"Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree","doi":"10.1109/OJITS.2024.3350213","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3350213","url":null,"abstract":"Bike sharing schemes can be used both to improve mobility around busy city routes but also to contribute to the fight against climate change. Optimization of the network in terms of station locations and routes is a focus for researchers, where usage can highlight the precise times at which bike availability is high in some areas and low in others. Locations for new stations are important for the expansion of the network, but spatio-temporal pattern analysis is required to accurately identify those locations. In other words, one cannot rely on spatial information nor temporal information in isolation, when making interpretations for the purpose of optimizing or expanding the network. In this research, a solution based on graph networks was developed to model activity in transport networks by exploiting properties and functions specific to graph databases. This generic approach adopts a broad series of analyses, comprising different levels of granularity and complexity, to enable better interpretation of network dynamics at a suitably granular level to help the optimization of transport networks. A large dataset provided by an electric bike company is used to address key research questions in both interpreting activity patterns and supporting network optimization.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"115-131"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY IEEE 智能交通系统学会公开期刊

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2023.3339042

引用次数: 0

IEEE Open Journal of Intelligent Transportation Systems Instructions for Authors IEEE 智能交通系统开放式期刊作者须知

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2023.3339044

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Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts? 耦合驾驶模拟器中的人类并线行为：我们如何解决冲突？

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-04 DOI: 10.1109/OJITS.2024.3349635

Olger Siebinga;Arkady Zgonnikov;David A. Abbink

{"title":"Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?","authors":"Olger Siebinga;Arkady Zgonnikov;David A. Abbink","doi":"10.1109/OJITS.2024.3349635","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3349635","url":null,"abstract":"Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimise their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"103-114"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10380755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles 关于高度自动化客运车辆的建模和用户接受度调查

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2023-12-25 DOI: 10.1109/OJITS.2023.3346477

Ilias E. Panagiotopoulos;George J. Dimitrakopoulos;Gabriele Keraite

{"title":"On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles","authors":"Ilias E. Panagiotopoulos;George J. Dimitrakopoulos;Gabriele Keraite","doi":"10.1109/OJITS.2023.3346477","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3346477","url":null,"abstract":"Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research that explores the predictors influencing consumers’ intention to adopt highly automated passenger vehicles. Through an online questionnaire-based survey conducted among European adults, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to incorporate three additional constructs: perceived reliability/trust, perceived financial cost, and perceived driving enjoyment. The key findings of this study underscore the significance of driving enjoyment, financial cost, social influences, and reliability/trust as influential predictors of consumers’ intention to adopt highly automated passenger vehicles. By considering these factors, automotive stakeholders can gain valuable insights to develop effective strategies and approaches for the successful implementation of highly automated passenger vehicles in the near future. Last, its innovations pave the way for a transformative shift in transportation, enabling the realization of safer, more efficient, and enjoyable travel experiences for individuals and society as a whole.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"70-84"},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10373556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0