Pub Date : 2024-07-22DOI: 10.1109/OJITS.2024.3430843
Praveen Abbaraju;Subrata Kumar Kundu
Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.
尽管电动汽车（EV）在续航能力和充电时间方面存在限制，但它正日益受到广泛关注和欢迎。因此，为了确保电动汽车的可靠性，提高客户满意度，有必要监控和跟踪其电池状况。本文介绍了一种新颖的联合与集合学习（FEL）算法，用于精确估算电池健康状况（SoH）。FEL 算法利用了来自不同利益相关者和地理因素（如交通和天气数据）的真实世界数据。长短期记忆（LSTM）模型已作为 SoH 估算的基础模型实施，利用以数据为中心的联合学习策略，作为边缘场景对每次行程进行持续更新。采用堆叠集合学习算法，将来自不同数据源的数据结合起来，对基础模型进行再训练。使用 NASA 电池数据集对所提出的 FEL 算法的有效性进行了评估，结果表明，经过 30 次迭代后，SoH 估计有了显著改善，平均误差为 3.24%。对比分析（包括有和无利益相关者数据集合的 LSTM 模型）显示，准确率提高了 75%。所提出的与模型无关的 FEL 算法通过利益相关者之间的高效数据共享，显示了其在精确 SoH 估算方面的有效性，并可为实现以数据为中心的互联电动汽车智能解决方案带来显著效益。
{"title":"A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles","authors":"Praveen Abbaraju;Subrata Kumar Kundu","doi":"10.1109/OJITS.2024.3430843","DOIUrl":"10.1109/OJITS.2024.3430843","url":null,"abstract":"Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"445-453"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769896","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}
Pub Date : 2024-07-22DOI: 10.1109/OJITS.2024.3432176
Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos
The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.
{"title":"FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications","authors":"Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos","doi":"10.1109/OJITS.2024.3432176","DOIUrl":"10.1109/OJITS.2024.3432176","url":null,"abstract":"The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"433-444"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769897","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}
Pub Date : 2024-07-15DOI: 10.1109/OJITS.2024.3427790
Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca
Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.
未来的交通环境可能会出现人类驾驶车辆与互联和自动驾驶车辆（CAVs）并存的情况。为了评估 CAV 的影响，尤其是在大规模应用中的影响，可以使用中间混合多尺度模型。通过在实施交通控制策略的区域采用分解模型，在受控制基础设施间接影响的其他区域采用宏观模型，这些模型很容易适应交通控制策略。本文的重点是之前在文献中建立的模型--H-CA&CTM（混合蜂窝自动机-CA-蜂窝传输模型-CTM），重点是可在混合交通流模型中实施的微观模型。研究有两个主要目的1) 使用两种不同的方法研究 CA 模型在不同单元长度下的校准问题：在封闭的环形布局中模拟所有车辆，以及使用从各自跟随者处获得的数据模拟每辆车；2) 在校准过程中使用车辆轨迹数据，以便对各种方法进行综合比较。考虑了两种详细的方法：1. 测量的领跑者-模拟的跟随者互动方法。2.模拟领先者--模拟跟随者交互方法。本文的主要发现是，使用模拟领跑者方法获得的校准参数在不同的单元长度上显示出更大的规律性。
{"title":"Parameters Estimation of a Microscopic Traffic Flow Sub-Model Within a Multiscale Approach Using Experimental Data","authors":"Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca","doi":"10.1109/OJITS.2024.3427790","DOIUrl":"10.1109/OJITS.2024.3427790","url":null,"abstract":"Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"409-421"},"PeriodicalIF":4.6,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10597615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718537","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}
Pub Date : 2024-07-08DOI: 10.1109/OJITS.2024.3425165
Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl
This paper presents an analysis and implementation of a robust autonomous driving system for an electric passenger shuttle in shared spaces. We present results of a risk assessment for our vehicle scenario and develop a flexible architecture that integrates safety features and optimises open-source software, facilitating research and operational functionality. Identifying limitations of the Robot Operating System (ROS) framework, we incorporate our own control measures for autonomous, unsupervised operation with enhanced intelligence. The study emphasizes algorithm selection based on application requirements to ensure optimal performance. We discuss system improvements, including monitoring node implementation and localization algorithm selection. Future work should explore transitioning to a real-time operating system (RTOS) and establishing standardized software engineering practices for consistent reliability. Our findings contribute to effective autonomous shuttle systems in shared spaces, promoting safer and more reliable transportation solutions.
{"title":"Managing Risk in the Design of Modular Systems for an Autonomous Shuttle","authors":"Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl","doi":"10.1109/OJITS.2024.3425165","DOIUrl":"10.1109/OJITS.2024.3425165","url":null,"abstract":"This paper presents an analysis and implementation of a robust autonomous driving system for an electric passenger shuttle in shared spaces. We present results of a risk assessment for our vehicle scenario and develop a flexible architecture that integrates safety features and optimises open-source software, facilitating research and operational functionality. Identifying limitations of the Robot Operating System (ROS) framework, we incorporate our own control measures for autonomous, unsupervised operation with enhanced intelligence. The study emphasizes algorithm selection based on application requirements to ensure optimal performance. We discuss system improvements, including monitoring node implementation and localization algorithm selection. Future work should explore transitioning to a real-time operating system (RTOS) and establishing standardized software engineering practices for consistent reliability. Our findings contribute to effective autonomous shuttle systems in shared spaces, promoting safer and more reliable transportation solutions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"555-565"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567510","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}
Pub Date : 2024-07-05DOI: 10.1109/OJITS.2024.3424587
Luman Zhao;Guoyuan Li;Houxiang Zhang
In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.
{"title":"Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance","authors":"Luman Zhao;Guoyuan Li;Houxiang Zhang","doi":"10.1109/OJITS.2024.3424587","DOIUrl":"10.1109/OJITS.2024.3424587","url":null,"abstract":"In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"422-432"},"PeriodicalIF":4.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567511","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}
Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.
{"title":"Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing","authors":"Salman Bari;Xiagong Wang;Ahmad Schoha Haidari;Dirk Wollherr","doi":"10.1109/OJITS.2024.3418956","DOIUrl":"10.1109/OJITS.2024.3418956","url":null,"abstract":"Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"380-392"},"PeriodicalIF":4.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508700","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}
Pub Date : 2024-06-24DOI: 10.1109/OJITS.2024.3418552
Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu
From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the need to evaluate the intelligence level of such testees are of equal importance. In this paper, we propose a possible solution to these challenges: Explainable Evaluation for visual intelligence. Specifically, we focus on the problem setting where the internal mechanisms of AI algorithms are sophisticated, heterogeneous or unreachable. In this case, a latent attribute dictionary learning method with constrained by mapping consistency is proposed to explain the performance variation patterns of visual perception intelligence under different test samples. By jointly iteratively solving the learning of latent concept representation for test samples and the regression of latent concept-generalization performance, the mapping relationship between deep representation, semantic attribute annotation, and generalization performance of test samples is established to predict the degree of influence of semantic attributes on visual perception generalization performance. The optimal solution of proposed method could be reached via an alternating optimization process. Through quantitative experiments, we find that global mapping consistency constraints can make the learned latent concept representation strictly consistent with deep representation, thereby improving the accuracy of semantic attribute-perception performance correlation calculation.
{"title":"Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models","authors":"Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu","doi":"10.1109/OJITS.2024.3418552","DOIUrl":"10.1109/OJITS.2024.3418552","url":null,"abstract":"From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the need to evaluate the intelligence level of such testees are of equal importance. In this paper, we propose a possible solution to these challenges: Explainable Evaluation for visual intelligence. Specifically, we focus on the problem setting where the internal mechanisms of AI algorithms are sophisticated, heterogeneous or unreachable. In this case, a latent attribute dictionary learning method with constrained by mapping consistency is proposed to explain the performance variation patterns of visual perception intelligence under different test samples. By jointly iteratively solving the learning of latent concept representation for test samples and the regression of latent concept-generalization performance, the mapping relationship between deep representation, semantic attribute annotation, and generalization performance of test samples is established to predict the degree of influence of semantic attributes on visual perception generalization performance. The optimal solution of proposed method could be reached via an alternating optimization process. Through quantitative experiments, we find that global mapping consistency constraints can make the learned latent concept representation strictly consistent with deep representation, thereby improving the accuracy of semantic attribute-perception performance correlation calculation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"393-408"},"PeriodicalIF":4.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508701","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}
Pub Date : 2024-06-12DOI: 10.1109/OJITS.2024.3413197
Robert Klar;Anders Andersson;Anna Fredriksson;Vangelis Angelakis
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.
{"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}
Pub Date : 2024-06-11DOI: 10.1109/OJITS.2024.3412820
Giulio Salierno;Letizia Leonardi;Giacomo Cabri
Industry 5.0 has introduced new possibilities for defining key features of the factories of the future. This trend has transformed traditional industrial production by exploiting Digital Twin (DT) models as virtual representations of physical manufacturing assets. In the railway industry, Digital Twin models offer significant benefits by enabling anticipation of developments in rail systems and subsystems, providing insight into the future performance of physical assets, and allowing testing and prototyping solutions prior to implementation. This paper presents our approach for creating a Digital Twin model in the railway domain. We particularly emphasize the critical role of Big Data in supporting decision-making for railway companies and the importance of data in creating virtual representations of physical objects in railway systems. Our results show that the Digital Twin model of railway switch points, based on synthetic data, accurately represents the behavior of physical railway switches in terms of data points.
{"title":"A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data","authors":"Giulio Salierno;Letizia Leonardi;Giacomo Cabri","doi":"10.1109/OJITS.2024.3412820","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3412820","url":null,"abstract":"Industry 5.0 has introduced new possibilities for defining key features of the factories of the future. This trend has transformed traditional industrial production by exploiting Digital Twin (DT) models as virtual representations of physical manufacturing assets. In the railway industry, Digital Twin models offer significant benefits by enabling anticipation of developments in rail systems and subsystems, providing insight into the future performance of physical assets, and allowing testing and prototyping solutions prior to implementation. This paper presents our approach for creating a Digital Twin model in the railway domain. We particularly emphasize the critical role of Big Data in supporting decision-making for railway companies and the importance of data in creating virtual representations of physical objects in railway systems. Our results show that the Digital Twin model of railway switch points, based on synthetic data, accurately represents the behavior of physical railway switches in terms of data points.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"1-18"},"PeriodicalIF":4.6,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10554659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725634","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}
Pub Date : 2024-06-07DOI: 10.1109/OJITS.2024.3411525
Luis G. Jaimes;Harish Chintakunta;Paniz Abedin
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.
{"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}
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