IET Intelligent Transport Systems最新文献

{"title":"Anti-Saturation Sliding Mode Control for Virtually Coupled HHTs Under Saturation Constraints","authors":"Jing He,&nbsp;Yu Long,&nbsp;Changfan Zhang","doi":"10.1049/itr2.70008","DOIUrl":"https://doi.org/10.1049/itr2.70008","url":null,"abstract":"<p>Maintaining an appropriate distance between trains is key to the normal operation of multiple trains in the virtual coupling mode. However, owing to physical limitations, the saturation of the control system is prone to occur during actual train operations, which makes it difficult to maintain a safe distance between adjacent trains when the speed changes. An anti-saturation sliding mode control algorithm for multiple virtually coupled trains was proposed to address this issue. First, according to the virtual coupling dynamics model of multiple heavy-haul trains (HHTs), an improved finite-time anti-windup compensator (FAWC) suitable for the train model was designed such that the compensation factor rapidly converged within a finite time. Second, the FAWC was introduced into the controller to suppress the input saturation phenomenon of trains. Then, a finite-time dual anti-saturation sliding mode controller (FDA-SMC) was constructed based on the barrier Lyapunov function in combination with the sliding mode algorithm against input constraints to suppress the impact of input and output saturation on the tracking accuracy for the relative position between adjacent HHTs. The stability of the closed-loop system was verified using the Lyapunov stability theory. Finally, the simulation and experimental results showed that the proposed algorithm demonstrated advantages in terms of anti-saturation performance and maintained a safe distance between adjacent HHTs.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Driver Readiness in Conditionally Automated Vehicles From Eye-Tracking Data and Head Pose","authors":"Mostafa Kazemi,&nbsp;Mahdi Rezaei,&nbsp;Mohsen Azarmi","doi":"10.1049/itr2.70006","DOIUrl":"https://doi.org/10.1049/itr2.70006","url":null,"abstract":"<p>As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE level 3 or partly automated vehicles, the driver needs to be available and ready to intervene when necessary. This makes it essential to evaluate their readiness accurately. This article presents a comprehensive analysis of driver readiness assessment by combining head pose features and eye-tracking data. The study explores the effectiveness of predictive models in evaluating driver readiness, addressing the challenges of dataset limitations and limited ground truth labels. Machine learning techniques, including LSTM architectures, are utilised to model driver readiness based on the spatio-temporal status of the driver's head pose and eye gaze. The experiments in this article revealed that a bidirectional LSTM architecture, combining both feature sets, achieves a mean absolute error of 0.363 on the DMD dataset, demonstrating superior performance in assessing driver readiness. The modular architecture of the proposed model also allows the integration of additional driver-specific features, such as steering wheel activity, enhancing its adaptability and real-world applicability.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143456135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KTnet: Hazy weather object detection based on knowledge transfer","authors":"Haigang Deng,&nbsp;Zhiheng Lu,&nbsp;Chengwei Li,&nbsp;Tong Wang,&nbsp;Changshi Liu,&nbsp;Qian Xiong","doi":"10.1049/itr2.12606","DOIUrl":"https://doi.org/10.1049/itr2.12606","url":null,"abstract":"<p>The current method to address the reduced accuracy of target detection algorithms in hazy weather scenes is mainly to first use image dehazing algorithms to restore hazy images, and then input the restored images into target detection algorithms to obtain detection results. However, the images restored by the image dehazing model deviate from real clear images, and do not completely recover the features required by the target detection algorithm, thus limiting the improvement of the detection accuracy of the target detection model. This paper proposes a hazy weather target detection algorithm based on large convolution kernels and knowledge transfer (KTnet). First, a large convolution attention dehazing module is embedded into the backbone network of faster R-CNN to form a dehazing backbone network. Considering the high-dimensional features of the deep backbone network, a lightweight fusion attention module is designed. A loss function is also designed and the adapter model is employed to devise training methods for knowledge transfer and fine-tuning. Extensive experimental results on various hazy weather target detection datasets show that KTnet has achieved significant effectiveness.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban Travel Chain Estimation Based on Combination of CHMM and LDA Model","authors":"Chenxi Xiao,&nbsp;Jinjun Tang,&nbsp;JaeYoung Jay Lee,&nbsp;Yunyi Liang","doi":"10.1049/itr2.70004","DOIUrl":"https://doi.org/10.1049/itr2.70004","url":null,"abstract":"<p>Understanding travel patterns and predicting travel destinations has gained significant attention in the field of transportation research. This study proposes a methodology that utilizes continuous hidden Markov models (CHMMs) to estimate activity sequences for each travel chain and employs a travel destination prediction model based on a random forest (RF) model. Furthermore, it explores the optimization of the results from HMM using the latent Dirichlet allocation (LDA) model and applies it in predicting travel destinations. In the experiment, the dataset collected from unique travellers in Seoul city, South Korea, is used to validate the proposed model, which includes time stamps of origin and destination, location, travel mode and transfer nodes. Research findings show that during the modelling phase of the continuous hidden Markov model, the Gaussian mixture model categorizes the feature vectors into eight distinct groups. The estimated membership probability indicates involvement in four different activities. It also explains the relationship between derived activities. Finally, given the observed features, the proposed model provides an effective method for estimating the most likely sequence of activities in the travel chain. The results can help conduct further activity-based traffic demand analysis and improve the service quality of the transportation system.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recovering Missing Passenger Flow Data in Subway Stations via an Enhanced Generative Adversarial Network","authors":"Hongru Yu,&nbsp;Yuanli Gu,&nbsp;Mingyuan Li,&nbsp;Shejun Deng,&nbsp;Wenqi Lu,&nbsp;Yuming Heng","doi":"10.1049/itr2.70005","DOIUrl":"https://doi.org/10.1049/itr2.70005","url":null,"abstract":"<p>To address the challenges posed by incomplete data in passenger flow prediction and organizational tasks, this paper proposes ProbSparse self-attention conditional generative adversarial imputation net (ProbSA-CGAIN), a novel imputation model framework built on the enhanced generative adversarial network (GAN). The model leverages conditional GANs for controlled data generation using external conditional information. It adopts a denoising autoencoder structure for reconstructing and estimating missing passenger flow data. The integration of an efficient ProbSparse self-attention mechanism captures spatiotemporal evolution features, reducing computational complexity. Additionally, the model incorporates auxiliary conditional information to enhance data imputation accuracy by learning interdependencies among multiple data variables. Further, the model integrates local positional encoding and multi-layer global temporal encoding, offering diverse perspectives on spatiotemporal information. Experimental evaluations with real passenger flow data demonstrate the model's superiority over advanced baseline models across various missing patterns and rates. Notably, it exhibits high stability in data restoration, particularly for datasets with higher missing rates, affirming its effectiveness in predicting and inferring missing passenger flow data based on auxiliary data and multi-view positional information, ensuring reliable imputation. The experiments also assess the model's proficiency in attributing different spatiotemporal features, confirming its commendable training and restoration efficiency.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143397139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Traffic Routes With Enhanced Double Q-Learning","authors":"Mayur Patil,&nbsp;Pooja Tambolkar,&nbsp;Shawn Midlam-Mohler","doi":"10.1049/itr2.70002","DOIUrl":"https://doi.org/10.1049/itr2.70002","url":null,"abstract":"<p>Traffic management has become a major issue in urban planning due to the increasing number of vehicles on urban roads. In this study, we introduce a novel approach using the Reinforcement Learning (RL) technique to address the vehicle routing problem (VRP). We explored the effectiveness of Double Q-Learning enhanced by Prioritized Experience Replay (DQL-PER) in optimizing vehicle routing to shorten travel times and reduce congestion. Using the Simulation of Urban Mobility (SUMO), this method manipulates traffic flow during peak hours to improve urban mobility. DQL-PER stands out due to its superior performance in managing complex traffic systems characterized by multiple interconnected variables and dynamic conditions inherent in urban traffic networks. Compared to standard Q-learning, DQL-PER reduces overestimation bias and facilitates faster convergence toward optimal solutions. This paper includes a comparison between DQL-PER and other RL methods, namely Q-learning, Double Q-learning (DQL), and deep Q-network (DQN), demonstrating its benefits through simulations and analysis. We also perform a scalability analysis to evaluate the algorithm's performance across network sizes, with node counts <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 <mo>=</mo>\u0000 <mrow>\u0000 <mn>39</mn>\u0000 <mo>,</mo>\u0000 <mn>545</mn>\u0000 <mo>,</mo>\u0000 <mn>1672</mn>\u0000 <mo>,</mo>\u0000 <mn>3236</mn>\u0000 <mo>,</mo>\u0000 <mspace></mspace>\u0000 <mtext>and</mtext>\u0000 <mspace></mspace>\u0000 <mn>9652</mn>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation>$N = {39, 545, 1672, 3236, text{ and } 9652}$</annotation>\u0000 </semantics></math>, showing that DQL-PER performs exhaustively over larger networks, demonstrating its scalability potential. DQL-PER offers a scalable solution with the potential to transform urban transportation systems.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-learning-based vehicle trajectory prediction: A review","authors":"Chenhui Yin,&nbsp;Marco Cecotti,&nbsp;Daniel J. Auger,&nbsp;Abbas Fotouhi,&nbsp;Haobin Jiang","doi":"10.1049/itr2.70001","DOIUrl":"https://doi.org/10.1049/itr2.70001","url":null,"abstract":"<p>Vehicle trajectory prediction enables autonomous vehicles to better reason about fast-changing driving scenarios and thus perform well-informed decision-making tasks. Among different prediction approaches, deep learning-based (DL-based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long-horizon prediction. This work reviews the DL-based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer-based models. Finally, this work discusses future research directions, including considerations for real-time applications.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DDPGAT: Integrating MADDPG and GAT for optimized urban traffic light control","authors":"Meisam Azad-Manjiri,&nbsp;Mohsen Afsharchi,&nbsp;Monireh Abdoos","doi":"10.1049/itr2.70000","DOIUrl":"https://doi.org/10.1049/itr2.70000","url":null,"abstract":"<p>Urban traffic control is a complex and dynamic multi-agent challenge, characterized by the need for efficient coordination and real-time responsiveness in fluctuating traffic conditions. Traditional methods often fall short in adapting to these dynamic environments. This article introduces “DDPGAT”, a novel framework that merges Multi-Agent Deep Deterministic Policy Gradients (MADDPG) with Graph Attention Networks (GATs) for optimized urban traffic control, further enhanced by a unique moral reward component. DDPGAT empowers traffic signal controllers as independent agents using GATs for dynamic road importance assessment. Shared attention scores during training enhance each agent's understanding of local and wider traffic patterns, essential for developing adaptive control policies. A key innovation in DDPGAT is the moral reward function, encouraging decisions that consider neighboring intersections' traffic, thus promoting ethical traffic management. The experiments demonstrate that DDPGAT significantly boosts traffic throughput and reduces congestion, confirming its effectiveness in diverse traffic conditions. The integration of MADDPG, GATs, and a moral reward strategy in DDPGAT presents a sophisticated, robust approach for managing the complexities of urban traffic control, marking a notable progression in intelligent traffic system technologies.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of airport surface potential conflict based on GNN-LSTM","authors":"Ligang Yuan,&nbsp;Daoming Fang,&nbsp;Haiyan Chen,&nbsp;Jing Liu","doi":"10.1049/itr2.12611","DOIUrl":"https://doi.org/10.1049/itr2.12611","url":null,"abstract":"<p>The development of the civil aviation industry has contributed to a steady increase in the number of daily flight operations at airports, which in turn has led to increasingly complex airport ground layouts. To aid airport managers in understanding the operational situation on the airport surface, this paper introduces a predictive model for airport ground conflict situations based on GNN-LSTM. This model identifies potential conflicts, conflict hotspots, and conflict hotspots zones, designating key intersections on taxiways as conflict hotspots according to taxiing rules. A conflict network is constructed, employing GNN with an integrated attention mechanism to extract structural features of the network, while LSTM is utilized to capture temporal features. After tuning the model parameters, predictions are made regarding the overall potential number of potential conflicts on the surface. To validate the effectiveness of the model, experimental analysis is conducted using AirTOp simulation data from Shenzhen Bao'an Airport, comparing GNN-LSTM model with GNN-GRU, LSTM, and GRU models, using RMSE and MAE as loss functions. The results demonstrate that he proposed modelling approach effectively extracts the temporal features of potential conflict and GNN-LSTM model outperforms other models in predicting the overall number of potential conflicts.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boarding stop inference with uncertain relationship between bus vehicles and mobile smart card readers","authors":"Peng Zhou,&nbsp;Yu Shen,&nbsp;Yuxiong Ji,&nbsp;Yuchuan Du","doi":"10.1049/itr2.12615","DOIUrl":"https://doi.org/10.1049/itr2.12615","url":null,"abstract":"<p>Boarding stop inference for bus passengers is essential for the improvement of bus transit services. Previous studies mainly focus on matching the bus trajectories with the bus stop locations, while the relationship between smart card readers—which collect the smart card data—and bus vehicles is usually given. However, uncertainties arise in practical applications regarding the matching of vehicles and card readers. To tackle this challenge, in this study, a data-driven approach is proposed to dig into the spatiotemporal features of passengers' smart card data and bus vehicle operations. A weighted bipartite graph algorithm is developed to match the smart card readers with the bus vehicles automatically. To verify the feasibility and effectiveness of the proposed approach, a case study is conducted on the Bus Anhong Line in Shanghai, China. The inferred results of boarding stops are compared with the data from passenger counting sensors installed in the bus vehicles. The matching accuracy rate achieves 0.9539, which validates the effectiveness of the proposed matching model. In addition, the inferred data are used to present the spatiotemporal patterns of boarding passengers and identify high-demand bus stops.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}