Communications in Transportation Research最新文献

{"title":"Situational awareness using set-based estimation and vehicular communication: An occluded pedestrian-crossing scenario","authors":"Vandana Narri ,&nbsp;Amr Alanwar ,&nbsp;Jonas Mårtensson ,&nbsp;Henrik Pettersson ,&nbsp;Fredrik Nordin ,&nbsp;Karl Henrik Johansson","doi":"10.1016/j.commtr.2025.100190","DOIUrl":"10.1016/j.commtr.2025.100190","url":null,"abstract":"<div><div>The safety of unprotected road-users is crucial in any urban traffic. Occlusions and blind spots in the field-of-view of a vehicle can lead to unsafe situations. In this work, a specific pedestrian-crossing scenario is considered with an occlusion in the ego-vehicle's field-of-view. A novel framework is presented to enhance situational awareness based on vehicle-to-everything (V2X) communication to share perception data between vehicle and roadside units. It leverages set-based estimation utilizing a computationally efficient algorithm, for which the pedestrian is guaranteed to be located in a constrained zonotope. The proposed method has been validated through both simulation and real experiments. The real experiments are carried out on a test track using Scania autonomous vehicles.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100190"},"PeriodicalIF":12.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DFUN-KDF: Efficient and robust decentralized federated framework for UAV networks via knowledge distillation and filtering","authors":"Wenyuan Yang ,&nbsp;Yuhang Liu ,&nbsp;Xinlin Leng ,&nbsp;Hanlin Gu ,&nbsp;Gege Jiang ,&nbsp;Xiaochuan Yu ,&nbsp;Xiaochun Cao","doi":"10.1016/j.commtr.2025.100173","DOIUrl":"10.1016/j.commtr.2025.100173","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are increasingly crucial across various fields. There is a growing interest in using federated learning (FL) methods to enhance the efficiency of UAV operations. Nevertheless, incumbent methods remain encumbered by significant drawbacks, including high energy consumption from extensive parameter exchanges, the imperative for homogeneous networks, and sensitivity to single-point failures. These difficulties are compounded by the unreliable nature of communication channels and the current inability to effectively manage the diversity of UAV models, highlighting the imperative for more resilient and adaptable FL solutions. To address these issues, we propose an efficient and robust decentralized FL framework for heterogeneous UAV networks. Our framework first leverages the knowledge distillation where UAVs transmit embeddings instead of model parameters to reduce the number of transmission parameter. UAVs update their local models using embeddings generated by other UAVs, which also enables UAVs with diverse architectures to participate in training. Moreover, our framework incorporates a filtering mechanism to remove malicious embeddings, ensuring resilience against adversities in UAV networks. Extensive experiments on various datasets validate the effectiveness and practical deployment potential of our framework.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100173"},"PeriodicalIF":12.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating micro and macro traffic control for mixed autonomy traffic","authors":"Tingting Fan ,&nbsp;Jieming Chen ,&nbsp;Edward Chung","doi":"10.1016/j.commtr.2025.100188","DOIUrl":"10.1016/j.commtr.2025.100188","url":null,"abstract":"<div><div>During the transition to fully autonomous traffic systems, managing mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is imperative. Existing macroscopic and microscopic strategies have shown effectiveness in alleviating highway congestion. However, the integration of these strategies for mixed autonomy traffic remains under-explored. This study proposes a hybrid flow and trajectory control (HFTC) strategy that combines a macroscopic control, ramp metering (RM), with a microscopic control, cooperative merging (CM) for CAV trajectory optimization in mixed traffic scenarios. Specifically, the RM control considers CAV-penetration-dependent dynamics to regulate ramp flow, and the CM utilizes a centralized optimization model to enhance CAV merging trajectories. Independently implementing RM or CM proved effective only under heavy or moderate traffic flow, whereas our proposed integrated strategy, HFTC, demonstrated greater adaptability and suitability under various traffic conditions. Additionally, the impacts of CAV penetration rates and traffic flows on performance of different control strategies are thoroughly explored. Simulation results indicate that under low and moderate traffic conditions, microscopic control can be comparable to macroscopic control given sufficient CAV integration, while under heavy traffic flows, macroscopic control cannot be replaced by microscopic control.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100188"},"PeriodicalIF":12.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multiagent social interaction model for autonomous vehicle testing","authors":"Shihan Wang ,&nbsp;Ying Ni ,&nbsp;Chengsheng Miao ,&nbsp;Jian Sun ,&nbsp;Jie Sun","doi":"10.1016/j.commtr.2025.100183","DOIUrl":"10.1016/j.commtr.2025.100183","url":null,"abstract":"<div><div>Social interaction capability (SIC) is essential for autonomous vehicles (AVs) when they interact with surrounding vehicles, as the ability of understanding and reacting to the behaviors of other road users can significantly enhance AVs’ rapid deployment. Virtual simulation testing is a core approach for evaluating AVs, including their SIC, on the basis of traffic simulation models. However, existing simulation models focus mainly on generating accurate vehicle trajectories and do not explicitly model the high-level sociality nature of interaction decisions that guide specific movements. This study aims to address this gap by developing a multiagent simulation model for the social interaction of human driving behavior on the basis of the multiagent imitation learning (MAIL) approach, which is referred to as the Social-MAIL model. Specifically, to quantify the sociality of decisions, we introduce social value orientation into the reward function to quantify cooperation or competition intent and guide the generation of social driving behaviors. Furthermore, to fully depict the complex interaction environment, we develop a heterogeneous policy network with temporal‒spatial attention mechanisms to describe the impact of multiple interactive objects and historical states on driving behavior. Through training and validation on the SinD dataset, we demonstrate that, compared with a set of baseline models, the proposed Social-MAIL model can accurately capture complex and time-varying social intent and reproduce the most realistic vehicle trajectories and macroscopic traffic flow characteristics at intersections. Moreover, we apply the Social-MAIL model for evaluating the SIC of AVs via comparison experiments.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100183"},"PeriodicalIF":12.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach","authors":"Wei Huang ,&nbsp;Dalin Tang ,&nbsp;Xin Qiao ,&nbsp;Guojun Chen","doi":"10.1016/j.commtr.2025.100185","DOIUrl":"10.1016/j.commtr.2025.100185","url":null,"abstract":"<div><div>An effective method of traffic state classification is crucial for managing urban traffic congestion. Existing methods usually assume a given number of state categories, which is not flexible if real applications are required to define different state levels. In this study, a parsimonious statistical model is derived and validated for classifying urban traffic states. The model is developed on the basis of a large-scale empirical travel speed dataset from five cities in China. First, a hybrid clustering method that integrates DBSCAN and natural breaks is used to derive traffic state classification under various numbers of state categories. The classification results are then compiled to conduct the subsequent regression analysis. Second, a two-stage regression approach is proposed to investigate the correlation between the number of state categories and the classification criteria (i.e., state thresholds that separate one state level from another). In the first stage, a significant linear relationship between the classification criteria of adjacent traffic states is derived (<span><math><mrow><mover><msup><mi>R</mi><mn>2</mn></msup><mo>¯</mo></mover></mrow></math></span> ​= ​0.80, <em>P</em> ​&lt; ​0.001). In the second stage, a significant correlation between the slope, intercept, and number of state categories is derived (<span><math><mrow><mover><msup><mi>R</mi><mn>2</mn></msup><mo>¯</mo></mover></mrow></math></span> ​= ​0.95, <em>P</em> ​&lt; ​0.001). On the basis of the two-stage regression analysis, a novel parsimonious statistical model is developed. Third, the developed model is evaluated with three performance indicators, namely, the mean squared error (MSE), mean absolute error (MAE), and mean relative error (MRE). The claffication accuracy is further validated via a case study on the speed data of Foshan Avenue North road. We suggest that the model can be used to assist flexible decision-making support with different levels of detail.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100185"},"PeriodicalIF":12.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next leap in the sustainable transport revolution: Identifying gaps and proposing solutions for hydrogen mobility","authors":"Fangjie Liu ,&nbsp;Muhammad Shafique ,&nbsp;Xiaowei Luo","doi":"10.1016/j.commtr.2025.100180","DOIUrl":"10.1016/j.commtr.2025.100180","url":null,"abstract":"<div><div>Amid escalating global climate concerns, the reliance of the transportation sector on high-carbon fossil fuels urgently demands sustainable alternatives. Hydrogen has emerged as a potent solution because of its zero-emission usage, but its overall impact hinges on its full life cycle, which this review comprehensively examines. This article delves into the environmental, economic, and safety dimensions of hydrogen as an alternative fuel by systematically reviewing the life cycle assessment (LCA) literature across the production, storage, delivery, and usage phases, with a focus on electrolysis and natural gas reforming methods, among others. A key insight from this study is the critical importance of considering the entire delivery system holistically rather than isolating the delivery phase. Many studies have overlooked two important aspects: first, the distribution of hydrogen as a product itself is often underemphasized; second, the integration of storage and delivery (the “storage-delivery nexus”) is crucial since separating them can lead to misleading conclusions about cost and emissions. For example, while certain delivery methods may appear cost-effective, their associated storage processes (such as hydrogenation and dehydrogenation in liquid organic hydrogen carrier systems) can have significant emission impacts. To address these gaps, this study introduces a novel “surface-level” LCA framework to enhance the assessment of the environmental impacts of hydrogen, promoting a more integrated understanding of the storage-delivery system. This framework aims to provide more accurate insights into hydrogen's life cycle, thereby facilitating better-informed policy-making and technological advancements. This study underscores the imperative for robust policy support, public engagement, and continuous innovation to overcome these barriers, advocating for strategic initiatives that bolster the sustainability and adoption of hydrogen mobility, particularly in hydrogen fuel cell vehicles (HFCVs).</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100180"},"PeriodicalIF":12.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic simulation optimization considering driving styles","authors":"Yunyang Shi ,&nbsp;Tong Wu ,&nbsp;Tan Guo ,&nbsp;Jinbiao Huo ,&nbsp;Ziyuan Gu ,&nbsp;Yifan Dai ,&nbsp;Zhiyuan Liu","doi":"10.1016/j.commtr.2025.100181","DOIUrl":"10.1016/j.commtr.2025.100181","url":null,"abstract":"<div><div>Parameter calibration is essential for ensuring the accuracy of microscopic traffic simulations. The expected speed is a critical parameter that characterizes behaviors of vehicles in most simulation models, which is influenced by road traffic conditions and the driving characteristics of different drivers. Most existing parameter calibration methods typically concentrate on micro-level parameters such as time headway and lane change motivation, while overlooking the calibration of vehicle expected speeds in consideration of driver behavior habits. This study combines data from highway electronic toll collection (ETC), gantries, and 100-m mileage average speed data, and proposes a method for calibrating vehicle expected speed that considers driving style clustering. The Gaussian mixture model (GMM) algorithm is used to develop driver models with three distinct driving styles: aggressive, moderate, and conservative. To ensure driving diversity and enhance parameter calibration efficiency, we rebuild vehicle driving models and representative parameters based on the classification results. Moreover, the Bayesian optimization algorithm is modified in conjunction with a microscopic traffic simulation model to perform automatic calibration of expected speeds. Experiments conducted on the Shanghai–Hangzhou–Ningbo highway demonstrate that the proposed method significantly reduces the mean absolute percentage error (MAPE) from 20.2% (using default parameters) to 3.1%. Additionally, in the model robustness test, the MAPE reaches 5.01%, indicating a certain level of stability and scalability. This method proposes a tailored calibration method accounting for the heterogeneous driving behaviors of micro-traffic simulation models, achieving satisfactory calibration results for simulation models in highway scenarios.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100181"},"PeriodicalIF":12.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MetaSSC: Enhancing 3D semantic scene completion for autonomous driving through meta-learning and long-sequence modeling","authors":"Yansong Qu ,&nbsp;Zixuan Xu ,&nbsp;Zilin Huang ,&nbsp;Zihao Sheng ,&nbsp;Sikai Chen ,&nbsp;Tiantian Chen","doi":"10.1016/j.commtr.2025.100184","DOIUrl":"10.1016/j.commtr.2025.100184","url":null,"abstract":"<div><div>Semantic scene completion (SSC) plays a pivotal role in achieving comprehensive perceptions of autonomous driving systems. However, existing methods often neglect the high deployment costs of SSC in real-world applications, and traditional architectures such as three-dimensional (3D) convolutional neural networks (3D CNNs) and self-attention mechanisms struggle to efficiently capture long-range dependencies within 3D voxel grids, limiting their effectiveness. To address these challenges, we propose MetaSSC, a novel meta-learning-based framework for SSC that leverages deformable convolution, large-kernel attention, and the Mamba (D-LKA-M) model. Our approach begins with a voxel-based semantic segmentation (SS) pretraining task, which is designed to explore the semantics and geometry of incomplete regions while acquiring transferable meta-knowledge. Using simulated cooperative perception datasets, we supervise the training of a single vehicle's perception via the aggregated sensor data from multiple nearby connected autonomous vehicles (CAVs), generating richer and more comprehensive labels. This meta-knowledge is then adapted to the target domain through a dual-phase training strategy—without adding extra model parameters—ensuring efficient deployment. To further enhance the model's ability to capture long-sequence relationships in 3D voxel grids, we integrate Mamba blocks with deformable convolution and large-kernel attention into the backbone network. Extensive experiments show that MetaSSC achieves state-of-the-art performance, surpassing competing models by a significant margin while also reducing deployment costs.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100184"},"PeriodicalIF":12.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed-loop coordination of connected and automated vehicles in structured road networks","authors":"Shijie Cong ,&nbsp;Mingyu Zheng ,&nbsp;Wei Zhang ,&nbsp;Yiming Bie ,&nbsp;Emilian Szczepański","doi":"10.1016/j.commtr.2025.100182","DOIUrl":"10.1016/j.commtr.2025.100182","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100182"},"PeriodicalIF":12.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey of research on autonomous driving testing with large models","authors":"Songyan Liu ,&nbsp;Shijie Cong ,&nbsp;Lan Yang","doi":"10.1016/j.commtr.2025.100179","DOIUrl":"10.1016/j.commtr.2025.100179","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100179"},"PeriodicalIF":12.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}