IEEE Open Journal of Intelligent Transportation Systems最新文献

Introducing Intelligent Data Sharing to Vehicular Cooperative Federated Learning 将智能数据共享引入车辆协同联邦学习

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-16 DOI: 10.1109/OJITS.2025.3589612

Levente Alekszejenkó;Péter Antal;Tadeusz Dobrowiecki

{"title":"Introducing Intelligent Data Sharing to Vehicular Cooperative Federated Learning","authors":"Levente Alekszejenkó;Péter Antal;Tadeusz Dobrowiecki","doi":"10.1109/OJITS.2025.3589612","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3589612","url":null,"abstract":"This paper proposes a simple yet unexplored measurement and federated learning system architecture for connected vehicles. The novelty of the introduced system is to combine the real-time data-sharing of crowdsensing with federated learning of global traffic models, providing up-to-date information for decision-making, facilitating faster learning, improving communicational channel usage, and possibly enhancing data privacy. This multi-level cooperative federated learning system generally supports operational, tactical, and strategic planning; therefore, we demonstrate its merits with a case study of parking monitoring in a simulated town as well as average speed prediction in a simulated part of Hannover, Germany. However, real-time data-sharing is essential for decision-making; it might also contain privacy-sensitive information regarding the trajectory of the vehicles. To mitigate the risk of privacy leakage, we experimented with different data selection methods for data exchange, introducing an optimization method inspired by Zeuthen’s negotiation strategy. We also checked the privacy impact of real-time data-sharing on federated learning. Our results indicate only negligible differences in privacy leakage between the proposed data selection methods. On the other hand, real-time data-sharing improves the reaction time of the federated learning system. The Zeuthen-inspired optimization method can efficiently supply valuable information for the communication partners. Moreover, it can enhance privacy protection in federated learning in some cases.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1009-1026"},"PeriodicalIF":5.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082276","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758451","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

Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey 城市交通模拟中的机器学习进展：综合调查

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-14 DOI: 10.1109/OJITS.2025.3589208

Harshit Maheshwari;Li Yang;Richard W. Pazzi

{"title":"Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey","authors":"Harshit Maheshwari;Li Yang;Richard W. Pazzi","doi":"10.1109/OJITS.2025.3589208","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3589208","url":null,"abstract":"Urban traffic simulation is useful in many ways to understand, manage, and predict the growing complexities of traffic dynamics within a city. Traditional simulation models often struggle to capture the intricacies of urban traffic patterns, leading to unrealistic simulations, which negatively affect traffic management and urban planning. In recent years, Machine Learning solutions have emerged to enhance various aspects of urban traffic simulation, which is possible by utilizing vast amounts of data and extracting valuable insights. This survey systematically reviews the state-of-the-art Machine Learning techniques applied to urban traffic simulation. By focusing on the practical application of Machine Learning techniques in various studies, we aim to analyze the current research direction, highlight the effectiveness of existing approaches, identify their limitations, and propose potential strategies to improve the performance and applicability of these techniques in real-world scenarios. Another key contribution of this survey is a proof-of-concept case study, which utilizes a basic Reinforcement Learning algorithm to control traffic lights across multiple intersections. The results from this case study demonstrate a significant improvement in vehicle wait time compared to the static baseline method. The code developed for this case study is publicly available, providing a valuable resource for researchers interested in replicating this work or building upon it. This survey aims to bridge the gap between simulation and reality by providing a comprehensive foundational understanding of the subject, critically evaluating the existing limitations in current methodologies, and suggesting future directions to improve performance, adaptability, and usability.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"1027-1052"},"PeriodicalIF":5.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758429","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

AI-Driven Mapping System for Smart Parking Management Applications Using an INS-GNSS-Solid-State LiDAR-Monocular Camera Fusion Engine Empowered by HD Maps 基于高清地图的ins - gnss固态激光雷达-单目相机融合引擎，用于智能停车管理应用的ai驱动地图系统

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-10 DOI: 10.1109/OJITS.2025.3587274

Kai-Wei Chiang;Syun Tsai;Jou-An Chen;Surachet Srinara;Meng-Lun Tsai;Chih-Yun Hsieh;Jyun-Yang Hung;Chalermchon Satirapod;Naser El-Sheimy

{"title":"AI-Driven Mapping System for Smart Parking Management Applications Using an INS-GNSS-Solid-State LiDAR-Monocular Camera Fusion Engine Empowered by HD Maps","authors":"Kai-Wei Chiang;Syun Tsai;Jou-An Chen;Surachet Srinara;Meng-Lun Tsai;Chih-Yun Hsieh;Jyun-Yang Hung;Chalermchon Satirapod;Naser El-Sheimy","doi":"10.1109/OJITS.2025.3587274","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3587274","url":null,"abstract":"Efficient parking management is crucial in crowded Asian cities to optimize limited road space and parking facilities. The increasing vehicle ownership rate in Taiwan has intensified the demand for street parking, leading to excessive driving in search of available spots and contributing up to 30% of traffic congestion. This paper proposes a low-cost, infrastructure-free outdoor roadside parking management system based on high-definition (HD) map updating. The system fuses data from a solid-state LiDAR (SSL) system, a monocular camera, an inertial navigation system, a GPS, and HD maps followed by deep-learning-based efficient region extraction. The goal was to achieve high accuracy with minimal computational resources and infrastructure costs. The proposed system’s performance for dynamic HD map object updating was evaluated through parking management tests. The system’s costs were low due to the selection of SSL and monocular cameras. Traditional and novel extrinsic calibration methods were compared in various experiments, and a hardware architecture for precise sensor time synchronization was designed. Software algorithms for accurate image–point-cloud projection were developed to update HD map parking layers. By using normal distribution transform matching of the SSL and HD point cloud maps, navigation performance was achieved to 0.4-meter accuracy. When applied to license plate localization in two experimental scenarios, the mean performance error was approximately 0.48 and 0.62 m.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"995-1008"},"PeriodicalIF":5.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075854","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725186","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

xFedCAV: Cyberattacks on Leader and Followers in Automated Vehicles With Cooperative Platoons Using Federated Agents xFedCAV：使用联邦代理的协作排自动驾驶车辆对领导者和追随者的网络攻击

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-04 DOI: 10.1109/OJITS.2025.3581617

Guanyu Lin;Sean Qian;Zulqarnain H. Khattak

{"title":"xFedCAV: Cyberattacks on Leader and Followers in Automated Vehicles With Cooperative Platoons Using Federated Agents","authors":"Guanyu Lin;Sean Qian;Zulqarnain H. Khattak","doi":"10.1109/OJITS.2025.3581617","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3581617","url":null,"abstract":"The increasing prevalence of connected and autonomous vehicles (CAVs) in smart cities requires robust cyberattack and anomaly detection systems to ensure safety and resilience. Cyberattacks on leader and follower in cooperative driving can result in differing impacts, however, their impacts on security and resilience of cooperative driving are largely unknown. Traditional anomaly detection methods, which aggregate data centrally, compromise driver privacy and are insufficient to address real-world challenges due to limitations of being compromised by adversarial attacks. To overcome these limitations, we propose Explainable Fine-Grained Cyberattacks and Anomaly Detection with Federated Agents for connected autonomous vehicles (xFedCAV). Our framework leverages federated learning to enhance privacy and security, using Shapley Additive exPlanations (SHAP) for interpretable detection. Unlike existing methods, xFedCAV focuses on fine-grained detection by simulating cyberattacks on individual vehicles rather than the entire fleet, allowing for more precise identification and response. Experimental results, conducted on a real-world CAV dataset, demonstrate that xFedCAV not only explains the relationship between vehicle characteristics and detection outputs, but also effectively detects cyberattacks in a decentralized manner. This research offers knowledge about the cybersecurity impacts of the leader and follower within cooperative driving and provides a significant advancement in federated learning applications for CAVs, contributing to the development of safer and more resilient smart city applications for transportation systems.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"898-914"},"PeriodicalIF":4.6,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11071968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646590","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 New Perspective on AI Safety Through Control Theory Methodologies 基于控制理论方法的人工智能安全新视角

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-07-02 DOI: 10.1109/OJITS.2025.3585274

Lars Ullrich;Walter Zimmer;Ross Greer;Knut Graichen;Alois C. Knoll;Mohan M. Trivedi

{"title":"A New Perspective on AI Safety Through Control Theory Methodologies","authors":"Lars Ullrich;Walter Zimmer;Ross Greer;Knut Graichen;Alois C. Knoll;Mohan M. Trivedi","doi":"10.1109/OJITS.2025.3585274","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3585274","url":null,"abstract":"While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged to improve AI safety. Therefore, this article outlines a new perspective on AI safety based on an interdisciplinary interpretation of the underlying data-generation process and the respective abstraction by AI systems in a system theory-inspired and system analysis-driven manner. In this context, the new perspective, also referred to as data control, aims to stimulate AI engineering to take advantage of existing safety analysis and assurance in an interdisciplinary way to drive the paradigm of data control. Following a top-down approach, a generic foundation for safety analysis and assurance is outlined at an abstract level that can be refined for specific AI systems and applications and is prepared for future innovation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"938-966"},"PeriodicalIF":4.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11066173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704947","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

Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review 网联与自动驾驶汽车的容错与回退策略综述

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-27 DOI: 10.1109/OJITS.2025.3583787

Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Joshué Pérez Rastelli;Asier Zubizarreta

{"title":"Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review","authors":"Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Joshué Pérez Rastelli;Asier Zubizarreta","doi":"10.1109/OJITS.2025.3583787","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3583787","url":null,"abstract":"Connected and Automated Vehicles (CAVs) are considered the future of transportation, offering increased safety, efficiency, and convenience. However, their reliance on sophisticated sensors and complex algorithms poses challenges, especially in scenarios with uncertainties, constraints, or failures. Dynamic Driving Task (DDT) fallback and fault tolerance strategies serve as critical mechanisms to ensure safe operation when primary systems fail or face functional insufficiencies. This paper provides an analysis of the fault-related taxonomy established by international standards and a comprehensive review of the DDT fallback and fault tolerance strategies used in CAVs, focusing on their strategy, classification, and implementation methods. Moreover, the challenges and future research directions for the development and improvement of fault tolerance strategies are discussed. The analysis shows that the main trends are to avoid the termination of the CAV operation in case of a failure or functional insufficiency, or at least to be able to guide the vehicle to a safe state. However, there is a tendency towards the possibility of continuing the operation. This review contributes to a deeper understanding of the role of DDT fallback and fault tolerance strategies for CAVs and future trends.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"915-937"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680901","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

Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey 利用3GPP特性和优化技术实现5G NR-V2X资源分配

IF 5.3

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-27 DOI: 10.1109/OJITS.2025.3584024

Chaeriah Bin Ali Wael;El Hadj Dogheche;Nasrullah Armi;Agus Subekti;Iyad Dayoub

{"title":"Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey","authors":"Chaeriah Bin Ali Wael;El Hadj Dogheche;Nasrullah Armi;Agus Subekti;Iyad Dayoub","doi":"10.1109/OJITS.2025.3584024","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3584024","url":null,"abstract":"Cellular Vehicle-to-everything (C-V2X) communication is critical for Intelligent Transportation Systems (ITS), facilitating information exchange among road users and infrastructure. Since its first introduction in rel-15 by 3GPP, 5G NR-V2X features have continued to evolve, aiming to support increasingly advanced V2X services. Addressing diverse service requirements, spectrum scarcity, dynamic vehicular environments, and radio interference necessitates efficient resource allocation strategies for the 5G NR-V2X system. However, dealing with resource allocation problems involving various conflicting objectives and constraints while accomplishing the Quality of Services (QoS) requirements of the V2X system remains a challenging issue. In this direction, this survey examines state-of-the-art resource allocation strategies for 5G NR-V2X, focusing on 3GPP features associated with V2X communication and their implications, along with optimization techniques employed in designing resource allocation strategies. Specifically, we present the benefits and challenges of each 3GPP feature and optimization technique, and their application to communication and computing resource allocation problems. Finally, we discuss issues tied to 3GPP features and optimization techniques, then highlight future research opportunities for efficient 5G NR-V2X resource allocation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"967-994"},"PeriodicalIF":5.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725185","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

Time-Series Forecasting for Peak Hour Traffic Accidents 高峰时段交通意外的时间序列预测

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-26 DOI: 10.1109/OJITS.2025.3583686

Md. Ferdousul Haque Shikder;Yili Tang;Majid Emami Javanmard

{"title":"Time-Series Forecasting for Peak Hour Traffic Accidents","authors":"Md. Ferdousul Haque Shikder;Yili Tang;Majid Emami Javanmard","doi":"10.1109/OJITS.2025.3583686","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3583686","url":null,"abstract":"Globally traffic accidents cause considerable damage, injuries, and deaths, making their analysis a critical research area. Recent advances have developed various predictions with different method streams yet it is unclear what are the similarities and differences of these streams and how they suit the accident analyses in reality. This study develops time-series accident rate predictions at urban intersections to examine the performance of three streams of the models including statistical model (Negative Binomial Model), machine learning techniques (SARIMA-X) and neural network algorithms (Multi Layer Perceptron, MLP) and further analyzes the suitability of the three streams. Pearson correlation and statistical analysis are first performed to identify the relationships among the spatial-temporal variables (e.g., number of lanes). It is found that the Negative Binomial Model performs superior for the average accuracy of the accident predictions. SARIMA-X performs better for study areas with similar magnitudes of historical traffic accidents over time while MLP is more suitable for accident datasets exhibiting varied magnitudes of accident events. The results provide references and practical insights into the potential of leveraging advanced algorithms and techniques to tackle the dynamics of traffic accidents and improve road safety.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"883-897"},"PeriodicalIF":4.6,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11052747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623964","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

Overview of Traffic Flow Forecasting Techniques 交通流量预测技术综述

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-18 DOI: 10.1109/OJITS.2025.3580802

Annarita Carianni;Andrea Gemma

{"title":"Overview of Traffic Flow Forecasting Techniques","authors":"Annarita Carianni;Andrea Gemma","doi":"10.1109/OJITS.2025.3580802","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3580802","url":null,"abstract":"Forecasting traffic conditions is critical for modern mobility management. With urbanization and motorization rates rising globally, accurate traffic flow prediction plays a vital role in mitigating congestion, optimizing traffic strategies, and reducing environmental impacts. This paper provides a comprehensive review of traffic forecasting methods, bridging traditional techniques and innovative approaches driven by computational intelligence and abundant data. The study classifies forecasting methods into four categories: naïve techniques, parametric methods, simulation-based approaches, and nonparametric models such as machine learning and deep learning. Each category is analyzed for its historical development, theoretical foundations, and practical applications, with special emphasis on artificial intelligence’s transformative role in enabling dynamic and accurate predictions. The review evaluates traditional models like ARIMA and Kalman filters, alongside nonparametric techniques such as neural networks, and explores hybrid approaches that integrate multiple forecasting methods. It also assesses the complementary role of traffic simulation, from macroscopic to microscopic scales, in capturing complex traffic dynamics. The methodology synthesizes insights from foundational works and recent influential studies, examining metrics for prediction accuracy and identifying contextual factors shaping method effectiveness. The paper highlights strengths, limitations, and opportunities for advancement across forecasting approaches. Concluding with a forward-looking perspective, the review underscores trends such as spatiotemporal modeling and real-time data integration, which promise smarter, more adaptive traffic management solutions. This survey serves as a valuable resource for researchers, policymakers, and practitioners in navigating the evolving field of traffic flow forecasting.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"848-882"},"PeriodicalIF":4.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11042911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623889","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

Incentive-Based Platoon Formation: Optimizing the Personal Benefit for Drivers 基于激励的组队：优化驾驶员个人利益

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2025-06-17 DOI: 10.1109/OJITS.2025.3580464

Julian Heinovski;Doğanalp Ergenç;Kirsten Thommes;Falko Dressler

{"title":"Incentive-Based Platoon Formation: Optimizing the Personal Benefit for Drivers","authors":"Julian Heinovski;Doğanalp Ergenç;Kirsten Thommes;Falko Dressler","doi":"10.1109/OJITS.2025.3580464","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3580464","url":null,"abstract":"Platooning or cooperative adaptive cruise control (CACC) has been investigated for decades, but debate about its lasting impact is still ongoing. While the benefits of platooning and the formation of platoons are well understood for trucks, they are less clear for passenger cars, which have a higher heterogeneity in trips and drivers’ preferences. Most importantly, it remains unclear how to form platoons of passenger cars in order to optimize the personal benefit for the individual driver. To this end, in this paper, we propose a novel platoon formation algorithm that optimizes the personal benefit for drivers of individual passenger cars. For computing vehicle-to-platoon assignments, the algorithm utilizes a new metric that we propose to evaluate the personal benefits of various driving systems, including platooning. By combining fuel and travel time costs into a single monetary value, drivers can estimate overall trip costs according to a personal monetary value for time spent. This provides an intuitive way for drivers to understand and compare the benefits of driving systems like human driving, adaptive cruise control (ACC), and, of course, platooning. Unlike previous similarity-based methods, our proposed algorithm forms platoons only when beneficial for the driver, rather than solely for platooning. We demonstrate the new metric for the total trip cost in a numerical analysis and explain its interpretation. Results of a large-scale simulation study demonstrate that our proposed platoon formation algorithm outperforms normal ACC as well as previous similarity-based platooning approaches by balancing fuel savings and travel time, independent of traffic and drivers’ time cost.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"813-831"},"PeriodicalIF":4.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597691","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