IET Intelligent Transport Systems最新文献

{"title":"Heterogeneous Driver-Aware Vehicle Trajectory Reconstruction and Fusion for Multiple Long-Range Traffic Detectors","authors":"Li Li,&nbsp;Yu-Tao Liu,&nbsp;Er-Long Tan,&nbsp;Li-Yong Zheng,&nbsp;Run-Min Wang","doi":"10.1049/itr2.70116","DOIUrl":"10.1049/itr2.70116","url":null,"abstract":"<p>Traditional methods of vehicle trajectory reconstruction heavily rely on the data of cross-sectional traffic detectors, but the effectiveness of existing methods is limited by insufficient information of the cross-sectional data. In response to this, this study proposes a novel trajectory reconstruction method based on long-range traffic detectors. It builds upon Newell's car-following model and its derived inverse following model. By taking driver heterogeneity into account through individualised calibration of the key parameter, namely the spatial shift, which is optimised by the whale optimisation algorithm, the accuracy of trajectory reconstruction is enhanced. Furthermore, to connect trajectories of the same vehicle in the same area that are reconstructed by adjacent long-range detectors, a particle filter-based trajectory fusion method is developed. It can fuse overlapped reconstructed trajectories and smoothly connect sectional trajectories into a complete and seamlessly connected one. The performance of the trajectory reconstruction method is evaluated on the NGSIM I-80 dataset, while the trajectory fusion method was tested on both the I-80 and the TRJD TS datasets. Results show that the reconstruction method generates complete vehicle trajectories across various traffic flow conditions, achieving an average of 28.65% reduction in mean absolute error compared to methods that do not account for driver heterogeneity. The mean absolute error of the fused trajectories was reduced by 49.23% and 59.69% on average for two datasets, respectively, compared to reconstructed trajectories using a single detector. The trajectory reconstruction accuracy of the proposed method also outperforms that of a deep convolutional neural network and an improved adaptive smoothing method.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619250","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 Multi-Task ConvoBiLSTM Model With Self-Attention for Concurrent Forecasting of Traffic Accident Risk and Severity","authors":"Auwal Sagir Muhammad,&nbsp;Longbiao Chen,&nbsp;Cheng Wang","doi":"10.1049/itr2.70108","DOIUrl":"10.1049/itr2.70108","url":null,"abstract":"<p>In this study, we introduced a multi-task deep learning framework that concurrently forecasts traffic accident risk and severity by integrating convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) units, and a self-attention mechanism. Unlike conventional single-task approaches, our model leverages shared spatiotemporal representations to capture complex patterns in traffic data, thereby enhancing both predictive accuracy and generalizability. Evaluations on large-scale datasets from New York City and Chicago demonstrate that our approach achieves high accuracy (up to 92% for accident risk and 89% for severity) and remains robust across diverse urban contexts. Moreover, an enhanced SHAP-based interpretability module provides granular insights into the influence of both observable and latent factors, such as driver behaviour or road surface conditions, on prediction outcomes. The self-attention mechanism further mitigates unobserved heterogeneity by highlighting critical time steps and feature interactions. With competitive real-time performance and throughput, our framework offers a practical solution for dynamic traffic safety applications. Future work will focus on extending evaluations to broader urban settings and integrating latent variable models to better quantify unobserved influences, ultimately advancing the development of safer, more efficient transportation systems.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618887","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":"Benchmarking Recovery Methods for Adversarial Traffic Signs in Autonomous Driving","authors":"Doreen Sebastian Sarwatt,&nbsp;Frank Kulwa,&nbsp;Huansheng Ning,&nbsp;Adamu Gaston Philipo,&nbsp;Xuanxia Yao,&nbsp;Jianguo Ding","doi":"10.1049/itr2.70115","DOIUrl":"10.1049/itr2.70115","url":null,"abstract":"&lt;p&gt;Autonomous vehicles (AVs) depend critically on vision-based perception systems, with traffic sign classification (TSC) playing a crucial role in interpreting regulatory and warning signs for safe navigation. However, these systems are highly vulnerable to adversarial attacks, subtle input perturbations that deceive deep learning models while appearing benign to human drivers. While detection has been the primary focus of defense, recovery of adversarial perturbed signs remains significantly underexplored, despite its importance for maintaining real-time decision-making and operational safety. To bridge this gap, we present the first comprehensive benchmarking of state-of-the-art image classification recovery methods adapted to the traffic sign domain. We address three domain-specific challenges for autonomous driving: (1) robustness to real-world conditions (e.g., weather, occlusion), (2) latency compatible with real-time pipelines (100 ms), and (3) preservation of geometric/structural integrity. Our adaptations combine weather-resilient preprocessing, shape-preserving restoration, and latency-aware implementation. Under unified white-box attacks, we evaluate across TSRD, BTSC, and GTSRB using recovery rate (RR), structural similarity (SSIM), and recovery time (RT). To connect latency to function, we introduce the recovery-induced distance (RID), which maps recovery time (RT) to added travel distance. PuVAE, VAE, c-GAN, and CD-GAN achieve subfewmillisecond RT with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;RID&lt;/mi&gt;\u0000 &lt;mo&gt;&lt;&lt;/mo&gt;\u0000 &lt;mn&gt;0.1&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$mathrm{RID}&lt;0.1%$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; of the nominal braking distance; DIR remains within &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;annotation&gt;$sim$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;0.3% at &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;mi&gt;km&lt;/mi&gt;\u0000 &lt;mo&gt;/&lt;/mo&gt;\u0000 &lt;mi&gt;h&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$50,mathrm{km/h}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, CSC is &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;annotation&gt;$sim$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;1.9%–3.3%, and DiffPure incurs 150–165 ms latency, yielding &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;RID&lt;/mi&gt;\u0000 &lt;mo&gt;≈&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$mathrm{RID}approx $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; 8%–10% at &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 ","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619208","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":"Does Deep Learning Architectures Model Human-Like Intelligent Response in Asymmetric Car-Following Behaviour? A Novel Framework for Learning Acceleration–Deceleration Decisions","authors":"Nazmul Haque,&nbsp;Md Asif Raihan,&nbsp;Farhana Mozumder Lima,&nbsp;Md. Hadiuzzaman","doi":"10.1049/itr2.70117","DOIUrl":"10.1049/itr2.70117","url":null,"abstract":"<p>Accurate modelling of acceleration and deceleration decisions is vital for replicating car-following behaviour, as these govern longitudinal control, traffic stability, and safety. This study introduces ArrowNet81, a 200-layer novel convolutional neural network (CNN) architecture designed to model the asymmetric nature of these decisions using multivariate time-series data. The architecture leverages modular Ribs and Linkers to enhance depth while minimizing complexity. A perceived-risk variable derived from nominal time-to-collision (NomTTC) addresses the effect of vehicle heterogeneity in car following behaviour. Trajectories extracted from UAV video footage support neighbourhood vehicle identification via the queens move system (QMS), and a novel generalized data arrangement technique ensures compatibility with deep learning (DL) inputs. To prove its generalizability and superiority, the proposed architecture is trained, tested, and validated in three diverse traffic conditions against different statistical, machine learning (ML), and DL techniques. And the developed models using ArrowNet81 architecture outperform all of them. Future research may adapt ArrowNet81 for classification problems such as mode choice, accident severity, or spatial risk mapping, and integrate it into connected and autonomous vehicles (CAVs) to emulate human-like behaviours. The rib-based architecture also permits the development of lighter variants, facilitating real-time deployment in resource-constrained environments without compromising predictive performance.Accurate modelling of acceleration and deceleration decisions is vital for replicating car-following behaviour. ArrowNet81, a 200-layer novel CNN architecture designed to model the asymmetric nature of these decisions using multivariate time-series data. The rib-based architecture permits the development of lighter variants, facilitating real-time deployment in resource-constrained environments without compromising predictive performance.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618796","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 Linear Model Predictive Control-Based Ramp Metering Strategy for Traffic Flow Analysis in Continuous Multi-Bottleneck Highway Segments","authors":"Yifei Yang,&nbsp;Shunchao Wang,&nbsp;Zhibin Li","doi":"10.1049/itr2.70113","DOIUrl":"10.1049/itr2.70113","url":null,"abstract":"<p>Multi-bottleneck highway segments present significant challenges in traffic management due to the propagation of congestion waves between closely spaced bottlenecks. This study proposes a linear model predictive control (MPC)-based ramp metering strategy designed specifically for continuous multi-bottleneck corridors. The approach incorporates a macroscopic linear traffic flow model that discretizes the roadway into interconnected cells, allowing real-time prediction of traffic states on both mainline and ramp segments. The control problem is formulated as a constrained quadratic programming task aimed at minimizing vehicle accumulation and enhancing overall throughput. A key innovation of this strategy lies in its predictive, multi-input, multi-output architecture, which enables proactive, corridor-wide coordination of ramp inflows based on anticipated traffic conditions and inter-bottleneck interactions. To ensure real-time computational feasibility, a linearized cell transmission model is used and efficiently solved via the CPLEX optimizer. Simulation experiments demonstrate the effectiveness of the proposed method, with reductions in total travel time of 23.7%, 11.6% and 2.4% and corresponding reductions in total delay of 74.6%, 54.1% and 8.9%, compared to no-control, PI-ALINEA and regional MPC ramp metering strategies, respectively. These results highlight the strategy's superiority in improving system-wide traffic efficiency under complex congestion scenarios.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145572503","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":"Interdisciplinary Perspectives on E-Scooters: A Bibliometric and Systematic Analysis of the Current Research","authors":"Gokhan Yurdakul,&nbsp;Nezir Aydin,&nbsp;Oludolapo Akanni Olanrewaju","doi":"10.1049/itr2.70107","DOIUrl":"10.1049/itr2.70107","url":null,"abstract":"<p>Since the integration of private vehicles into modern societies, traffic congestion has emerged as a critical and persistent problem. In recent years, the rise of micro-mobility, including all forms of light electric vehicles such as e-scooters and e-bikes, has introduced both innovative solutions and significant challenges. While many scholars argue that micro-mobility can help alleviate traffic-related issues, ongoing debates highlight potential drawbacks, including regulatory gaps, concerns over battery reliability, and the increased risk of severe injuries in accidents. Within this context, a systematic synthesis of the literature represents an important milestone for the field. This study systematically reviews academic research on electric scooters (e-scooters) published in journals indexed in SCI, SCI-Expanded, and SSCI, providing a comprehensive assessment of the existing body of literature. The review organizes the literature into eight thematic categories: demand forecasting, optimization, marketing, urban micro-mobility policies and user behaviour, health and safety, prior review studies, survey-based research, and other miscellaneous domains. From an initial pool of 1921 peer-reviewed articles, the application of objective filtering criteria produced a final dataset of 313 studies, each of which was rigorously examined and categorized accordingly. Using bibliometric techniques, the study analyses publication trends, the geographical distribution of research, collaboration networks among authors, and citation patterns within the e-scooter literature. The findings reveal that demand forecasting and optimization dominate in terms of publication volume, while health, safety, and legal/regulatory research remain comparatively underexplored. Bibliometric results further demonstrate a sharp increase in scholarly activity over the past 5 years, with the majority of studies concentrated in Europe, North America, and Asia. By identifying gaps in the existing literature and outlining priority areas for future research, this study provides both a synthesized knowledge base and practical insights for policymakers, urban planners, and industry stakeholders. The integrative perspective adopted here not only bridges disciplinary divides but also offers actionable recommendations for advancing e-scooter systems toward greater safety, inclusivity, and sustainability.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145522015","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":"Traffic Flow Prediction Model Based on Transformer and Dynamic Graph Convolutional Networks","authors":"Peiyu Li,&nbsp;Zhao Tian,&nbsp;Chong Shang,&nbsp;Yaning Zhu,&nbsp;Honghui Dong,&nbsp;Yusong Lin","doi":"10.1049/itr2.70111","DOIUrl":"10.1049/itr2.70111","url":null,"abstract":"<p>Traffic congestion is a major challenge in urban life, and traffic flow prediction, as a critical task in intelligent transportation management, can effectively alleviate congestion and enhance traffic control efficiency. Although deep learning models have achieved significant progress in traffic flow prediction, most existing studies focus on capturing dynamic spatiotemporal features, with insufficient attention to the pivotal role of historical data's periodic characteristics in forecasting. To address this, we proposed a Transformer-based Dynamic Graph Convolutional Network (T-DGCN), which significantly improves prediction accuracy by integrating historical periodic features and real-time dynamic variations. T-DGCN employs the self-attention mechanism of Transformer to capture temporal dependencies and combines dynamic graph convolutional networks to extract spatial features, thereby comprehensively modelling spatiotemporal dynamics. Experiments on the real highway datasets from California (PeMSD4 and PeMSD8) show that T-DGCN outperforms state-of-the-art models across multiple time horizons. On the PeMSD4 dataset, it reduces the MAE and RMSE by an average of 12.9% and 5.2% across the 15 min, 30 min, and 60 min prediction horizons, respectively. On the PeMSD8 dataset, it reduces the MAE and RMSE results by approximately 8.8% and 5.7% on average, respectively.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521915","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":"Guest Editorial: Enhancing Safety in Connected Automated Vehicles (CAVs) for Mixed Traffic Environments","authors":"Chao Huang,&nbsp;Igal Bilik,&nbsp;Beatriz López Boada,&nbsp;Yue Huang","doi":"10.1049/itr2.70091","DOIUrl":"10.1049/itr2.70091","url":null,"abstract":"&lt;p&gt;Intelligent transportation systems (ITS) leverage information and communication technologies to enhance the efficiency, safety, and sustainability of road transportation. Connected vehicles play a crucial role in ITS as they enable communication with other vehicles and infrastructure, facilitating more efficient traffic management and safer driving experiences. They can provide real-time traffic information to help drivers avoid congestion and accidents, as well as navigation and route planning recommendations. Additionally, it can communicate with traffic signal equipment to optimise signal timing, reducing wait times and queue lengths. Implementation of connected vehicles in intelligent transportation systems will revolutionise the way we drive. However, many challenges need to be addressed to achieve maximum potential, including privacy and security issues, data processing and storage, the development of standards and regulations across all platforms, and the establishment of new communication protocols and system architectures.&lt;/p&gt;&lt;p&gt;This Special Issue on Enhancing Safety in Connected Automated Vehicles (CAVs) for Mixed Traffic Environments of the IET INTELLIGENT TRANSPORT SYSTEMS presents recent progress in theories, methods, and applications addressing safety-critical challenges in autonomous and intelligent transportation systems. After a rigorous peer-review process, 12 papers were selected from a wide range of international submissions. The accepted papers are grouped into seven principal research themes, each reflecting a key aspect of the Special Issue's scope.&lt;/p&gt;&lt;p&gt;The research in ‘A Lightweight Social Cognitive Risk Potential Field Model for Path Planning with Dedicated Dynamic and Static Traffic Factors’ addresses the issue of dependable risk assessment in autonomous driving by presenting a lightweight social cognitive risk potential field model derived from Coulomb's law. The model combines both static and dynamic traffic variables, including mass, velocity, acceleration, heading angle, and road boundaries, while reducing undetermined variables by 36%–50% relative to existing models. The model demonstrated high reliability and logical classification of factors through parametric and sensitivity analyses. Integrated with a model predictive controller, it proficiently enabled safe path planning in both static and dynamic environments, surpassing traditional risk field models in generating safer and more flexible driving trajectories.&lt;/p&gt;&lt;p&gt;In ‘Safe Passage Strategy with Swarm Intelligence for CAVs in Urban Road Heterogeneous Traffic Flow Using Standard Alliance Game’, the issue of safe navigation for connected automated vehicles within diverse traffic conditions is addressed by proposing a cooperative control strategy based on a standard alliance game. Each vehicle is considered a player, with payoffs reflecting safety, fairness, and efficiency. Three interaction protocols are established to direct decision-making. The methodolog","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521951","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":"Mobility-Aware Lévy-Enhanced Adaptive Squirrel Optimisation Framework With Improved Double Q-Learning for Energy-Efficient Dynamic Wireless Sensor Networks","authors":"Michaelraj Kingston Roberts,&nbsp;Jeevanandham Sivaraj,&nbsp;Sarah M. Alhammad,&nbsp;Doaa Sami Khafaga","doi":"10.1049/itr2.70109","DOIUrl":"10.1049/itr2.70109","url":null,"abstract":"<p>Wireless sensor networks (WSNs) operating in challenging, resource-constrained dynamic environments often struggle to address the persistent issues related to energy efficiency, node mobility, network coverage and performance. To overcome these research challenges, an innovative hybrid optimisation framework is proposed. This proposed framework effectively integrates squirrel search optimisation (SSO) with adaptive Lévy flights for enhancing the balance between the exploration-exploitation process, and enhanced double Q-learning for adaptive energy-aware routing. In addition, a long short-term memory (LSTM)-based mobility-aware node prediction model enables proactive cluster adaptation and a residual energy-based cluster head (CH) selection process to improve reliability, convergence speed and energy usage. To ensure a uniform workload among sensor nodes, the proposed algorithm incorporates adaptive data aggregation and task-aware load distribution, which minimises the possibility of redundant transmissions and enhances the operational lifespan of nodes under varying node densities. Simulation results across diverse scenarios confirm the effectiveness of our hybrid scheme in terms of performance improvements achieved across various performance evaluation metrics, including a 14.4% improvement in residual energy, a 11.7% improvement in coverage retention, a 18.71% improvement in cluster stability, a 13.42% enhancement in load balancing efficiency, a 4.5% improvement in scalability, a 11.51% enhancement in QoS reliability and a 61% reduction in complexity overhead across various node counts. Additionally, the proposed framework maintains superior network stability and outstanding packet delivery reliability under varying node densities when validated with state-of-the-art algorithms. These capabilities make our hybrid framework a reliable solution for diverse WSN applications where adaptability and resource efficiency are critical priorities.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521921","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 Effective Multi-Agent Reinforcement Learning Algorithm for Urban Traffic Light Scheduling","authors":"Chun-Wei Tsai,&nbsp;Yu-Chen Luo,&nbsp;Ming-Hsuan Tsai,&nbsp;Siang-Hong Yang","doi":"10.1049/itr2.70101","DOIUrl":"https://doi.org/10.1049/itr2.70101","url":null,"abstract":"<p>The multi-agent reinforcement learning (MARL) has been used to control traffic lights to mitigate the traffic congestion problem of urban cities. However, an agent in such algorithm can only have local information of the intersection to which it belongs instead of global information of all intersections that typically cannot be effectively and completely shared by all the agents. Hence, an effective algorithm, which aims to share information between agents at different neighbor intersections to further enhance the performance of MARL in solving the traffic light control problem, will be presented in this study. The proposed algorithm is a two-step communication mechanism that enables agents to share current local information with each other, thereby further improving the performance of MARL for traffic light control plans. To evaluate the performance of the proposed algorithm, we compare it with other state-of-the-art message-passing-based algorithms for solving the traffic light control optimization problem on the simulation of urban mobility (SUMO) simulator. The results show that the proposed algorithm is able to provide better results than state-of-the-art message-passing-based algorithms for the grid, Monaco, and Kaohsiung maps.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470043","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}