Pub Date : 2025-06-27DOI: 10.1109/OJITS.2025.3583787
Mario Rodríguez-Arozamena;Jose Matute;Javier Araluce;Joshué Pérez Rastelli;Asier Zubizarreta
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.
{"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}
Pub Date : 2025-06-27DOI: 10.1109/OJITS.2025.3584024
Chaeriah Bin Ali Wael;El Hadj Dogheche;Nasrullah Armi;Agus Subekti;Iyad Dayoub
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.
{"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}
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.
{"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}
Pub Date : 2025-06-18DOI: 10.1109/OJITS.2025.3580802
Annarita Carianni;Andrea Gemma
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.
{"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}
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.
{"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}
This paper presents a bio-inspired, generative Multi-Modal Sensor Fusion (MSF) framework to effectively detecting novel and dynamic situations in the surroundings of Autonomous Vehicle (AV). The MSF framework fuses both proprioceptive (wheel odometry) and exteroceptive (LiDAR point-clouds) sensory inputs. A novel 3-Dimensional Dynamic Variational Auto-Encoder (3D-DVAE) model is employed to learn attention-focused distributions from point-clouds in an unsupervised manner. By fusing the distributions of both modalities (wheel and lidar), modality-specific experts’ distributions are learned, capturing both proprioceptive and exteroceptive information from the surroundings. Bayesian Filtering is then applied to detect novel situations/dynamics by probabilistically inferring future states. The proposed method is validated using the KITTI dataset across diverse and complex urban environments. Both quantitative and qualitative results demonstrate the effectiveness of the proposed approach in detecting novelties through multi-modal fusion.
{"title":"Novelty Detection in Autonomous Driving: A Generative Multi-Modal Sensor Fusion Approach","authors":"Hafsa Iqbal;Haleema Sadia;Abdulla Al-Kaff;Fernando Garcié","doi":"10.1109/OJITS.2025.3580271","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3580271","url":null,"abstract":"This paper presents a bio-inspired, generative Multi-Modal Sensor Fusion (MSF) framework to effectively detecting novel and dynamic situations in the surroundings of Autonomous Vehicle (AV). The MSF framework fuses both proprioceptive (wheel odometry) and exteroceptive (LiDAR point-clouds) sensory inputs. A novel 3-Dimensional Dynamic Variational Auto-Encoder (3D-DVAE) model is employed to learn attention-focused distributions from point-clouds in an unsupervised manner. By fusing the distributions of both modalities (wheel and lidar), modality-specific experts’ distributions are learned, capturing both proprioceptive and exteroceptive information from the surroundings. Bayesian Filtering is then applied to detect novel situations/dynamics by probabilistically inferring future states. The proposed method is validated using the KITTI dataset across diverse and complex urban environments. Both quantitative and qualitative results demonstrate the effectiveness of the proposed approach in detecting novelties through multi-modal fusion.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"799-812"},"PeriodicalIF":4.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037519","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557713","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 : 2025-06-13DOI: 10.1109/OJITS.2025.3579653
Guido Perboli;Antonio Tota;Filippo Velardocchia
Heavy vehicle rollover plays a pivotal role in road safety scenarios. Numerous researchers addressed the topic, with particular focus on drivers related injuries. Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. Different methodologies were explored, with notable achievements obtained by neural network-based algorithms. At the same time, their heavy requirements in terms of data needs to be addressed to allow practical applications in terms of time and costs. Consequently, exploring the interaction between simulation and experimental data becomes extremely important, motivating the methodology proposed by this paper. In details, an heavy vehicle model was designed in IPG Carmaker®, while experimental data on its physical alter ego were acquired. This led to the generation of a synthetic dataset and the collection of an empirical one. Both were used to define a Long Short-Term Memory architecture, with a dual purpose. First, as typical rollover indicator, estimate the vehicle roll angle. Second, compare the performance of the neural networks, aiming to obtain at least the same order of magnitude in terms of RMSE, MSE and MAE. The goal was to demonstrate that synthetic data can not only be used in combination with real data, but also as substitutes able to address time and cost constraints inevitably linked to the latter, allowing more efficient experiments for overtipping prevention.
{"title":"Long-Short Term Memory Networks and Synthetic Data for Heavy Vehicle Rollover Prevention","authors":"Guido Perboli;Antonio Tota;Filippo Velardocchia","doi":"10.1109/OJITS.2025.3579653","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3579653","url":null,"abstract":"Heavy vehicle rollover plays a pivotal role in road safety scenarios. Numerous researchers addressed the topic, with particular focus on drivers related injuries. Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. Different methodologies were explored, with notable achievements obtained by neural network-based algorithms. At the same time, their heavy requirements in terms of data needs to be addressed to allow practical applications in terms of time and costs. Consequently, exploring the interaction between simulation and experimental data becomes extremely important, motivating the methodology proposed by this paper. In details, an heavy vehicle model was designed in IPG Carmaker®, while experimental data on its physical alter ego were acquired. This led to the generation of a synthetic dataset and the collection of an empirical one. Both were used to define a Long Short-Term Memory architecture, with a dual purpose. First, as typical rollover indicator, estimate the vehicle roll angle. Second, compare the performance of the neural networks, aiming to obtain at least the same order of magnitude in terms of RMSE, MSE and MAE. The goal was to demonstrate that synthetic data can not only be used in combination with real data, but also as substitutes able to address time and cost constraints inevitably linked to the latter, allowing more efficient experiments for overtipping prevention.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"792-798"},"PeriodicalIF":4.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11036550","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536565","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 : 2025-06-11DOI: 10.1109/OJITS.2025.3578872
Mariam Nour;Mohamed H. Zaki;Mohamed Abdel-Aty
Connected and automated vehicle (CAV) technology has the potential to enhance lane change safety in work zones, especially during lane closures. However, the safety implications of vehicle-to-vehicle (V2V) communication under realistic operating conditions remain insufficiently understood. This study investigates the impact of V2V communication on lane change safety in work zone scenarios using a calibrated co-simulation framework that integrates both traffic and communication networks. The framework simulates a range of realistic conditions—including varying market penetration rates (MPRs), communication ranges, and merge strategies (early and late)—and evaluates lane change safety using the time-to-collision (TTC) metric. A data dissemination algorithm is incorporated to coordinate V2V messaging and enable CAVs to initiate safe lane changes. Unlike prior studies that assume ideal communication conditions, this work simulates realistic V2V communication by incorporating metrics such as packet loss and packet delivery ratio to examine their impact on lane change safety. Findings indicate that higher MPRs and extended communication ranges generally enhance safety; however, limitations in communication quality can significantly reduce these benefits—particularly in late merge scenarios, where degraded data exchange decreases safety. Sensitivity analyses further reveal that lane-change timing and communication range are critical factors influencing safety outcomes, emphasizing the need to account for communication reliability when designing and evaluating CAV-based safety interventions.
{"title":"Assessing the Impact of Vehicle-to-Vehicle Communication on Lane Change Safety in Work Zones","authors":"Mariam Nour;Mohamed H. Zaki;Mohamed Abdel-Aty","doi":"10.1109/OJITS.2025.3578872","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3578872","url":null,"abstract":"Connected and automated vehicle (CAV) technology has the potential to enhance lane change safety in work zones, especially during lane closures. However, the safety implications of vehicle-to-vehicle (V2V) communication under realistic operating conditions remain insufficiently understood. This study investigates the impact of V2V communication on lane change safety in work zone scenarios using a calibrated co-simulation framework that integrates both traffic and communication networks. The framework simulates a range of realistic conditions—including varying market penetration rates (MPRs), communication ranges, and merge strategies (early and late)—and evaluates lane change safety using the time-to-collision (TTC) metric. A data dissemination algorithm is incorporated to coordinate V2V messaging and enable CAVs to initiate safe lane changes. Unlike prior studies that assume ideal communication conditions, this work simulates realistic V2V communication by incorporating metrics such as packet loss and packet delivery ratio to examine their impact on lane change safety. Findings indicate that higher MPRs and extended communication ranges generally enhance safety; however, limitations in communication quality can significantly reduce these benefits—particularly in late merge scenarios, where degraded data exchange decreases safety. Sensitivity analyses further reveal that lane-change timing and communication range are critical factors influencing safety outcomes, emphasizing the need to account for communication reliability when designing and evaluating CAV-based safety interventions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"832-847"},"PeriodicalIF":4.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11030855","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623869","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 : 2025-06-02DOI: 10.1109/OJITS.2025.3575808
Yiqiao Li;Andre Y. C. Tok;Stephen G. Ritchie
Inductive loop sensors are widely deployed across the U.S. and can provide vehicle classification data with comparable accuracy to the current axle-based sensor systems when they are enhanced with the inductive signature technology and advanced machine learning models. However, the existing truck population is expected to turnover and be replaced with newer models that may generate distinct inductive signature characteristics. Consequently, legacy inductive signature-based models may not perform optimally in classifying newer trucks operating on the highways over time. To enhance the resilience of the signature-based classification system, this paper investigated a self-learning framework to address the classification system obsolescence through the integration of two complementary sensor technologies: Inductive loop sensors and Light Detection and Ranging (LiDAR) sensors. In this framework, the LiDAR-based Federal Highway Administration (FHWA) classification model served as a data labeling platform to generate class labels for validating and updating the legacy signature-based model. Next, an adaptive transfer learning framework was implemented to improve the performance of a legacy inductive signature-based classification model without compromising computation efficiency. This framework demonstrates the resilience enhancement of the inductive signature-based FHWA classification model with an intelligent system update to accommodate vehicle transition over time while retaining legacy knowledge of the pre-existing population using a methodology that significantly reduces the overall burden of periodic model calibration by utilizing the information stored in the legacy model. The experiment demonstrates that this adaptive self-learning framework achieves an overall correct classification rate of 0.89 on a dataset with distinctively different truck configurations.
{"title":"Adaptive Self-Learning Framework for Resilient Vehicle Classification Through the Integration of Inductive Loops and LiDAR Sensors","authors":"Yiqiao Li;Andre Y. C. Tok;Stephen G. Ritchie","doi":"10.1109/OJITS.2025.3575808","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3575808","url":null,"abstract":"Inductive loop sensors are widely deployed across the U.S. and can provide vehicle classification data with comparable accuracy to the current axle-based sensor systems when they are enhanced with the inductive signature technology and advanced machine learning models. However, the existing truck population is expected to turnover and be replaced with newer models that may generate distinct inductive signature characteristics. Consequently, legacy inductive signature-based models may not perform optimally in classifying newer trucks operating on the highways over time. To enhance the resilience of the signature-based classification system, this paper investigated a self-learning framework to address the classification system obsolescence through the integration of two complementary sensor technologies: Inductive loop sensors and Light Detection and Ranging (LiDAR) sensors. In this framework, the LiDAR-based Federal Highway Administration (FHWA) classification model served as a data labeling platform to generate class labels for validating and updating the legacy signature-based model. Next, an adaptive transfer learning framework was implemented to improve the performance of a legacy inductive signature-based classification model without compromising computation efficiency. This framework demonstrates the resilience enhancement of the inductive signature-based FHWA classification model with an intelligent system update to accommodate vehicle transition over time while retaining legacy knowledge of the pre-existing population using a methodology that significantly reduces the overall burden of periodic model calibration by utilizing the information stored in the legacy model. The experiment demonstrates that this adaptive self-learning framework achieves an overall correct classification rate of 0.89 on a dataset with distinctively different truck configurations.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"768-780"},"PeriodicalIF":4.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367055","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 : 2025-04-29DOI: 10.1109/OJITS.2025.3565209
Kassem Danach;Hassan Harb;Badih Baz;Abbass Nasser
Efficient logistics management is critical in the modern global supply chain, and this study introduces an advanced hyperheuristic approach to the Multi-Echelon Hub and Routing Optimization (MEHRO) problem. The MEHRO problem encompasses optimizing hub locations and vehicle routes while balancing cost efficiency, service quality, and environmental sustainability. A novel mathematical model integrates transportation, hub setup, and inventory costs, strengthened by valid inequalities to enhance computational efficiency. The hyperheuristic framework dynamically selects from a pool of low-level heuristics, adapting strategies to varying problem instances. A real-world case study validates the model’s effectiveness, demonstrating significant cost reductions, improved service levels, and minimized environmental impact compared to traditional methods. This work sets a foundation for scalable and adaptive solutions in logistics and combinatorial optimization, catering to the evolving demands of global supply chain management.
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