IEEE Open Journal of Intelligent Transportation Systems最新文献_第4页

Container Relocation and Retrieval Tradeoffs Minimizing Schedule Deviations and Relocations 集装箱搬迁和取回的权衡尽量减少时间表偏差和搬迁

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-12 DOI: 10.1109/OJITS.2024.3413197

Robert Klar;Anders Andersson;Anna Fredriksson;Vangelis Angelakis

{"title":"Container Relocation and Retrieval Tradeoffs Minimizing Schedule Deviations and Relocations","authors":"Robert Klar;Anders Andersson;Anna Fredriksson;Vangelis Angelakis","doi":"10.1109/OJITS.2024.3413197","DOIUrl":"10.1109/OJITS.2024.3413197","url":null,"abstract":"Ports are striving to improve operational efficiency in the context of constantly growing volumes of trade. In this context, port terminal storage yard operation is key, since complexity and poor coordination lead to containers stacked without consideration of retrieval schedules, resulting in time- and energy-consuming reshuffling operations. This problem, known as the block relocation (and retrieval) problem (BRP), has recently gained considerable attention. Indeed, there are promising solutions to the BRP. However, the literature views the problem in isolation, optimizing one operational parameter for one of the many port stakeholders. This often leads to efficiency losses since port processes involve different stakeholders and port parts. In this work, we explicitly focus on scheduling trucks for pick-up for hinterland distribution. Appointments are often postponed in order to minimize reshuffling operations, leading to losses for the transport forwarders and decreasing the competitiveness of the port. We discuss the trade-off between minimizing container reshuffling operations while maintaining scheduled time windows for container retrieval. We describe the multi-objective optimization problem as a weighted sum of the two objectives. Given the complexity of the problem, we also present a greedy heuristic. Our results indicate that the number of schedule deviations can be reduced without significantly affecting the number of relocations compared to solutions that consider only the latter. Ideally, a weighting of 0.4 and 0.6 should be applied, reflecting schedule deviations and relocations, respectively, to achieve the highest joint optimization potential. This demonstrates that in complex environments, such as ports, with multiple interacting stakeholders and processes, coordination of solutions yields significant benefits.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"360-379"},"PeriodicalIF":4.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944416","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

A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data 基于合成数据的铁路信号数字孪生创建大数据架构

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-11 DOI: 10.1109/OJITS.2024.3412820

Giulio Salierno;Letizia Leonardi;Giacomo Cabri

引用次数: 0

SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing SenseNow：用于车载人群感应的与时间相关的激励方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-07 DOI: 10.1109/OJITS.2024.3411525

Luis G. Jaimes;Harish Chintakunta;Paniz Abedin

{"title":"SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing","authors":"Luis G. Jaimes;Harish Chintakunta;Paniz Abedin","doi":"10.1109/OJITS.2024.3411525","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3411525","url":null,"abstract":"This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"307-321"},"PeriodicalIF":4.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500210","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

Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach 利用噪声多导测量的自适应巡航控制：基于学习的方法

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-04-29 DOI: 10.1109/OJITS.2024.3395149

Ying-Chuan Ni;Victor L. Knoop;Julian F. P. Kooij;Bart van Arem

{"title":"Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach","authors":"Ying-Chuan Ni;Victor L. Knoop;Julian F. P. Kooij;Bart van Arem","doi":"10.1109/OJITS.2024.3395149","DOIUrl":"10.1109/OJITS.2024.3395149","url":null,"abstract":"A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"251-264"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828406","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

Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles 实现车联网和自动驾驶汽车弹性运行的网络攻击监测架构

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-04-22 DOI: 10.1109/OJITS.2024.3391830

Zulqarnain H. Khattak;Brian L. Smith;Michael D. Fontaine

{"title":"Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles","authors":"Zulqarnain H. Khattak;Brian L. Smith;Michael D. Fontaine","doi":"10.1109/OJITS.2024.3391830","DOIUrl":"10.1109/OJITS.2024.3391830","url":null,"abstract":"The deployment of connected and automated vehicles (CAVs) may enhance operations and safety with little human feedback. Automation requires the use of communication and smart devices, thus introducing potential access points for adversaries. This paper develops a prototype real-time monitoring system for a vehicle to infrastructure (V2I) based CAV system that generates cyberattack data for CAV operations under realistic traffic conditions. The monitoring system detects any deviations from the normal operation of CAVs using a long-short term memory (LSTM) neural network proposed by the authors and reverts the system back to a safe state of operation using a set of countermeasures. The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. The monitoring system detected three different emulated cyberattacks with high accuracy. The LSTM showed the highest accuracy of 98% and outperformed the other algorithms. Further, the performance of the monitoring systems was assessed in terms of the impact on traffic stream stability and safety. The results reveal that a fake basic safety message (BSM) attack on even a single CAV causes the traffic stream to become significantly unstable and increase safety risk without the monitoring system. The monitoring system, however, reverts the system to a safe state of operation and reduces the negative impacts of cyberattacks. The monitoring system improves flow stability by an average of 38% as quantified through acceleration variation and volatility. This is comparable to the base case without attacks. The findings have implications for the design of future resilient systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"322-341"},"PeriodicalIF":4.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140637188","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

LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario 基于平坦场景中地面约束的激光雷达与 IMU 紧密耦合定位系统

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-28 DOI: 10.1109/OJITS.2024.3406390

Man Yu;Keyang Gong;Weihua Zhao;Rui Liu

{"title":"LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario","authors":"Man Yu;Keyang Gong;Weihua Zhao;Rui Liu","doi":"10.1109/OJITS.2024.3406390","DOIUrl":"10.1109/OJITS.2024.3406390","url":null,"abstract":"Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"296-306"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195009","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

Editorial Special Section on Intelligent Transportation Systems for Public Transportation 公共交通智能运输系统》编辑专栏

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-27 DOI: 10.1109/OJITS.2024.3377217

Erik Jenelius;Abdulla Al-Kaff

{"title":"Editorial Special Section on Intelligent Transportation Systems for Public Transportation","authors":"Erik Jenelius;Abdulla Al-Kaff","doi":"10.1109/OJITS.2024.3377217","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3377217","url":null,"abstract":"Public transportation serves many important roles in society: When functioning well, it provides accessibility for people to work, healthcare and other essential activities, as well as high-speed mobility for massive volumes of passengers during peak hours. The efficiency of public transportation, in terms of energy consumption, emissions, surface occupancy, etc., makes it a crucial component of sustainable transportation systems in combination with the active mobility modes. New technologies have the potential to enhance the performance, efficiency and attractiveness of public transportation through new vehicle concepts, better resource utilization, and better use of automated data sources. This special Section on “Intelligent Transportation Systems for Public Transportation” was established to provide a collection of studies that advance the state-of-the-art in the field by developing, implementing and evaluating novel technologies and methods. After a rigorous review process, nine scientific papers have been selected to be published. A couple of themes emerge from the combined contributions, highlighting important and active areas of research:","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"205-207"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10482809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310115","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

Editorial Special Section on Coordination, Cooperation, and Control of Autonomous Vehicles in Smart Connected Road Environments 智能互联道路环境中自动驾驶车辆的协调、合作与控制》编辑专栏

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-27 DOI: 10.1109/OJITS.2024.3377216

Alberto Petrillo;Stefania Santini

{"title":"Editorial Special Section on Coordination, Cooperation, and Control of Autonomous Vehicles in Smart Connected Road Environments","authors":"Alberto Petrillo;Stefania Santini","doi":"10.1109/OJITS.2024.3377216","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3377216","url":null,"abstract":"Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"202-204"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310116","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 Optimal Routing Framework for an Integrated Urban Power–Gas–Traffic Network 城市电力-天然气-交通综合网络的优化路由框架

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-27 DOI: 10.1109/OJITS.2024.3380569

Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed

{"title":"An Optimal Routing Framework for an Integrated Urban Power–Gas–Traffic Network","authors":"Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed","doi":"10.1109/OJITS.2024.3380569","DOIUrl":"10.1109/OJITS.2024.3380569","url":null,"abstract":"This paper develops a risk-averse-based framework for optimizing the operation of an integrated power, gas, and traffic (PGT) network with an application to a typical PGT network in downtown Edmonton, the forefront of Canada’s transition to electric vehicles and sustainable urban travel options. The developed non-probabilistic framework provides decision-makers with various secure options to avoid worst-case scenarios and promote social and environmental benefits. The integration of different energy systems allows operators to pursue optimal strategies in critical situations, such as facility outages, maintaining the system within a secure operational range without resorting to expensive workarounds. The proposed algorithm and integrated structure can select optimal travel routes to minimize gas-emission effects and locate charging options to reduce electric vehicle users’ travel time. It can mitigate challenges posed by distributed generator outages and roadway closures. The numerical results from implementing the framework on different case studies and the solar-based PGT network of Edmonton indicate its feasibility and effectiveness.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"223-237"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10481512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140315865","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 the Prediction of the Sideslip Angle Using Dynamic Neural Networks 论利用动态神经网络预测侧滑角度

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-27 DOI: 10.1109/OJITS.2024.3405797

Raffaele Marotta;Salvatore Strano;Mario Terzo;Ciro Tordela

{"title":"On the Prediction of the Sideslip Angle Using Dynamic Neural Networks","authors":"Raffaele Marotta;Salvatore Strano;Mario Terzo;Ciro Tordela","doi":"10.1109/OJITS.2024.3405797","DOIUrl":"10.1109/OJITS.2024.3405797","url":null,"abstract":"With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle’s sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"281-295"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10539180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172633","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