IET Intelligent Transport Systems最新文献

{"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}

{"title":"Design prognostics for 4400 TEU container vessel by multi-variate Gaidai reliability approach","authors":"Yan Zhu,&nbsp;Oleg Gaidai,&nbsp;Jinlu Sheng,&nbsp;Alia Ashraf,&nbsp;Yu Cao,&nbsp;Zirui Liu","doi":"10.1049/itr2.12613","DOIUrl":"https://doi.org/10.1049/itr2.12613","url":null,"abstract":"<p>This case study introduces an innovative multivariate methodology for assessing the lifetime of marine engineering systems, specifically in cargo vessel transportation. The analysis focused on stress data collected onboard a 4400 TEU container vessel during multiple trans-Atlantic voyages. One of the major challenges in marine cargo transport lies in mitigating the risk of container loss due to excessive whipping loads. Accurate prediction of extreme stress levels on vessel deck panels remains difficult, primarily because of the nonlinear and non-stationary nature of wave and ship motion interactions. Higher-order dynamic effects, such as second- and third-order responses, often become significant when ships operate under adverse environmental conditions, amplifying nonlinear influences. Laboratory simulations, constrained by wave characteristics and scale similarity issues, may not always provide reliable results. Consequently, data collected from vessels navigating extreme weather conditions serves as a critical resource for comprehensive container ship risk assessment. The primary goal of this study was to validate and demonstrate the effectiveness of a novel multivariate risk evaluation approach, leveraging onboard measurements of dynamic areal pressure on cargo ship deck panels as the core dataset. The Gaidai methodology for multivariate risk evaluation proved to be a robust tool for assessing failure, hazard, and damage risks in complex, nonlinear vessel deck panel and ship hull stress systems.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114329","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":"A reinforcement learning-based reverse-parking system for autonomous vehicles","authors":"Amjed Al-Mousa,&nbsp;Ahmad Arrabi,&nbsp;Hamza Daoud","doi":"10.1049/itr2.12614","DOIUrl":"https://doi.org/10.1049/itr2.12614","url":null,"abstract":"<p>This work presents the design and implementation of a reinforcement learning-based autonomous parking system where an agent is trained to reverse-park in a selected parking spot. The parking procedure is divided into three stages, and each stage has its corresponding surrogate objective that contributes to the overall parking process. The model solely depends on features extracted from a top-view image of the parking space. It has the advantage of potential deployment in smart parking buildings without refitting non-autonomous cars with modern sensors. The training was conducted offline on a simulation utilizing the proximal policy optimization algorithm. The model was then transferred and tested on a hardware prototype of the parking space. The results of the system were successful as the successful parking rate reached 100% with no collisions with any objects, and the fastest parking time reached 10 s. The testing was conducted on multiple samples and scenarios of the parking setup.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113361","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":"Formulation and solution framework for real-time railway traffic management with demand prediction","authors":"Bianca Pascariu,&nbsp;Johan Victor Flensburg,&nbsp;Paola Pellegrini,&nbsp;Carlos M. Lima Azevedo","doi":"10.1049/itr2.12610","DOIUrl":"https://doi.org/10.1049/itr2.12610","url":null,"abstract":"<p>Recent transport policies increasingly promote shifts towards rail travel aiming at a more sustainable transportation system. This shift is hampered by widespread unexpected perturbations in operations, resulting in perceived poor punctuality and reliability. When prevention of such perturbations is not feasible, traffic management must mitigate their effects, resolving arising conflicts to restore regular train operations and minimize delay. Current practice generally includes the assessment of railway performance in terms of train delays, but the quality of service to passengers is rarely explicitly accounted for. A railway traffic management framework is proposed that accounts for both passenger and train delays. To do so, a predictive optimization framework is proposed, integrating a demand prediction module, a passenger demand assignment module and a traffic management module. The first dynamically predicts future origin-destination passenger flows using linear regression on real-time observed smart card data. Then, the demand assignment module links predicted passengers to specific train paths, given a railway schedule. Finally, the traffic management module optimizes train scheduling and routing in real time, under the combined objective of minimizing train and passenger delays. The methodology is validated and benchmarked against equivalent passenger agnostic traffic management on a case study of the Copenhagen suburban railway network. The results show that it is possible to take into account passenger perspective in railway traffic management, without reducing the railway system efficiency compared to classic approaches.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12610","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113495","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}