IET Intelligent Transport Systems最新文献

{"title":"Optimising Shelter Locations for Bus Evacuation and Relief Supply Under Traffic Congestion","authors":"Seong-Jong Woo,&nbsp;Seungmo Kang","doi":"10.1049/itr2.70020","DOIUrl":"https://doi.org/10.1049/itr2.70020","url":null,"abstract":"<p>Effective disaster management requires shelter location optimisation to enhance evacuation efficiency and ensure timely relief distribution. This study integrates human evacuation and relief logistics while accounting for traffic congestion during large-scale evacuations, thereby proposing a model that prioritises bus-based evacuation to mitigate congestion and expedite movement, particularly for transit-dependent populations. Employing a metaheuristic evolutionary algorithm with a local search process, the model is applied to a flood scenario in Ulsan, South Korea and significantly outperforms alternative methods in optimising shelter placement, transportation routes and relief supply distribution. Comparative analysis indicates that the proposed shelter locations reduce total costs by 9.4% relative to manually selected nearest shelters. Additionally, neglecting network congestion was found to underestimate evacuation time by up to 41%. The proposed approach also reduces relief transportation costs by 4.5%. Sensitivity analysis examines the impact of bus availability and evacuation demand variations. This study is the first to fully incorporate city-wide traffic congestion into shelter location optimisation under multimodal evacuation scenarios.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143690091","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":"Current Status and Future Prospects of Digital Twin Technology Applications in Intelligent Transportation Infrastructure Management","authors":"Chao Gao,&nbsp;Lei Jia,&nbsp;Maopeng Sun,&nbsp;Junshao Luo","doi":"10.1049/itr2.70011","DOIUrl":"https://doi.org/10.1049/itr2.70011","url":null,"abstract":"<p>Digital twin technology has emerged as a promising solution for the digital transformation of transportation infrastructure. This paper presents a comprehensive review of digital twin technology in the transportation industry, analyzing its relationship with key enabling technologies. By examining the development of digital twins across various transportation domains, we summarize the connotation, characteristics, and development trends of digital twins in transportation infrastructure. We propose a conceptual model and a digital system architecture for transportation infrastructure, along with a set of engineering application technical guidelines. Our findings reveal that current digital twin technology still faces challenges in driving the digital transformation of the transportation industry. From a theoretical perspective, the granularity of digital twin models is insufficient, lacking systematic support. In terms of application, the reconstruction of full-cycle digital processes primarily focuses on low-level applications. Future research should focus on theory innovation, data fusion, model integration, and professional applications to promote the development of digital twin technology in transportation infrastructure. Additionally, emphasis should be placed on collaborative design across disciplines and data standardization to build intelligent full-lifecycle management platforms, improve operation and maintenance efficiency, and provide new ideas and methods for sustainable development.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612388","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":"An Efficient Illumination-Invariant Dynamic Facial Expression Recognition for Driving Scenarios","authors":"Ercheng Pei,&nbsp;Man Guo,&nbsp;Abel Díaz Berenguer,&nbsp;Lang He,&nbsp;HaiFeng Chen","doi":"10.1049/itr2.70009","DOIUrl":"https://doi.org/10.1049/itr2.70009","url":null,"abstract":"<p>Facial expression recognition (FER) is significant in many application scenarios, such as driving scenarios with very different lighting conditions between day and night. Existing methods primarily focus on eliminating the negative effects of pose and identity information on FER, but overlook the challenges posed by lighting variations. So, this work proposes an efficient illumination-invariant dynamic FER method. To augment the robustness of FER methods to illumination variance, contrast normalisation is introduced to form a low-level illumination-invariant expression features learningmodule. In addition, to extract dynamic and salient expression features, a two-stage temporal attention mechanism is introduced to form a high-level dynamic salient expression features learning module deciphering dynamic facial expression patterns. Furthermore, additive angular margin loss is incorporated into the training of the proposed model to increase the distances between samples of different categories while reducing the distances between samples belonging to the same category. We conducted comprehensive experiments using the Oulu-CASIA and DFEW datasets. On the Oulu-CASIA VIS and NIR subsets in the normal illumination, the proposed method achieved accuracies of 92.08% and 91.46%, which are 1.01 and 7.06 percentage points higher than the SOTA results from the DCBLSTM and CELDL method, respectively. Based on the Oulu-CASIA NIR subset in the dark illumination, the proposed method achieved an accuracies of 91.25%, which are 4.54 percentage points higher than the SOTA result from the CDLLNet method. On the DFEW dataset, the proposed method achieved a UAR of 60.67% and a WAR of 71.48% with 12M parameters, approaching the SOTA result from the VideoMAE model with 86M parameters. The outcomes of our experiments validate the effectiveness of the proposed dynamic FER method, affirming its ability in addressing the challenges posed by diverse illumination conditions in driving scenarios.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554686","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 Two-Stage Energy-Efficiency Optimization Approach for Conflict-Free Dispatching in Open-Pit Mines","authors":"Zhichao Wang,&nbsp;Jue Yang,&nbsp;Yanbiao Feng,&nbsp;Yiting Kang,&nbsp;Yong Li","doi":"10.1049/itr2.70003","DOIUrl":"https://doi.org/10.1049/itr2.70003","url":null,"abstract":"<p>The objective of this paper is to present a novel energy-efficiency conflict-free dispatching algorithm for autonomous mining fleets. In lieu of halting or decelerating the trucks at intersections when conflicts arise, the algorithm facilitates conflict-free dispatching for trucks to operate with the optimal speed trajectory, thereby achieving minimum fuel consumption and mining cost. This work first develops reference speed trajectories for mining trucks, considering their drivetrain characteristics, load status and geographic information pertaining to the path. Second, the total production determination model is based on the MILP model, which determines the total production of each path while taking the travel time into account with the objective of maximizing fleet production. Next, in the fleet allocation model and conflict-free scheduling model, the objectives are to reduce the fleet make span and fleet queuing time, respectively. Finally, a fleet operation timetable is eventually derived. Therefore, all trucks can operate intact according to the speed trajectory, thus minimizing fleet energy consumption and maximizing production efficiency. To verify the advantages of the model in this work, we selected DISPATCH and a multi-objective dispatching model developed by other researchers for comparison on the basis of the historical production data from an open pit coal mine. The results indicated that the proposed model exhibited the capacity to decrease the fleet size by 22.2%, thereby attaining equivalent production levels to those of a real open-pit coal mining fleet. Moreover, the model proposed in this paper can improve the production by about 36.11% to 49.75% compared to DISPATCH under the optimal speed trajectory, whereas the multi-objective dispatching model's improvement is only 9.84% to 21.89%. It also has significant advantages in terms of fleet productivity and fleet profit.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533568","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":"Prescribed Performance Ship Tracking Control With a Novel Predefined-Time Performance Function","authors":"Han Xue,&nbsp;Xiangtao Wang","doi":"10.1049/itr2.70014","DOIUrl":"https://doi.org/10.1049/itr2.70014","url":null,"abstract":"<p>How to accurately process and achieve good transient performance in a short period of time is a key consideration factor for the system. A hyperbolic sine function is used to construct a novel predefined-time convergent prescribed performance function. This algorithm introduces a set of new predefined standards for time convergence assessment based on gamma functions and Riemann zeta functions. By integrating performance indicators of speed, stability and efficiency into the design of the prescribed performance function, the performance framework ensures the achievement of establishing a comprehensive performance optimization model. The upper limit of the settling time is studied, and sufficient conditions for achieving predetermined time convergence are established, validated through experiments using unmanned surface vessels.</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/itr2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475632","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":"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}