IET Intelligent Transport Systems最新文献_第5页

Enhancing Intersection Safety at Uncontrolled Three-Legged Intersections Through Assessment of Risk 通过风险评估提高非受控三足交叉口的安全性

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-10 DOI: 10.1049/itr2.70062

Khushbu Bhatt, Jiten Shah, Margaret C. Bell, Dilum Dissanayake

{"title":"Enhancing Intersection Safety at Uncontrolled Three-Legged Intersections Through Assessment of Risk","authors":"Khushbu Bhatt, Jiten Shah, Margaret C. Bell, Dilum Dissanayake","doi":"10.1049/itr2.70062","DOIUrl":"10.1049/itr2.70062","url":null,"abstract":"The accepted gap—the time or distance a driver deems sufficient to enter or cross an intersection—is a key indicator of traffic risk, particularly at uncontrolled three-legged intersections. Smaller accepted gaps are linked to higher risk due to an increased chance of vehicle conflicts. This study investigates the relationship between accepted gaps and risk and proposes a method to quantify the level of risk and severity (LORS) to guide targeted safety interventions. Data on vehicle speed, accepted gap and critical gap were collected from six rural intersections in India. Using a binary logit regression model and clustering techniques, the LORS was estimated and validated against actual accident data, yielding a predictive accuracy of up to 83%.The significance of this study lies in its novel data-driven approach to safety assessment using parameters easily measured in the field. Designed for heterogeneous traffic conditions, the method provides traffic engineers and planners with a practical tool to assess intersection safety, recommend specific remedial measures and prioritise interventions based on risk and severity levels. With potential for automation and scalability, this research contributes to the development of safer road systems, particularly in low-resource settings where conventional crash data is limited or unavailable.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589907","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}

引用次数: 0

Safe Passage Strategy With Swarm Intelligence for CAVs in Urban Road Heterogeneous Traffic Flow Using Standard Alliance Game 基于标准联盟博弈的城市道路异构交通流中自动驾驶汽车群体智能安全通行策略

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-08 DOI: 10.1049/itr2.70056

Jixiang Wang, Siqi Chen, Jing Wei, Haiyang Yu, Yilong Ren

{"title":"Safe Passage Strategy With Swarm Intelligence for CAVs in Urban Road Heterogeneous Traffic Flow Using Standard Alliance Game","authors":"Jixiang Wang, Siqi Chen, Jing Wei, Haiyang Yu, Yilong Ren","doi":"10.1049/itr2.70056","DOIUrl":"10.1049/itr2.70056","url":null,"abstract":"This study introduces an innovative approach to distributed cooperative gaming for CAVs in urban road traffic scenarios, aimed at ensuring safe passage. This method treats every connected vehicle in the heterogeneous traffic flow as a player in the game. The individual payoffs for these players are clearly defined by quantifying factors such as travel safety risk, fairness and efficiency. Furthermore, three protocols are developed from the perspectives of enhancing individual payoff and improving alliance stability. These protocols enable CAVs to achieve logical control under conflicting interference from CHVs. By utilising alliance cooperative gaming, CAVs can collectively determine their strategies, avoiding the pitfalls of individual decision-making that could result in mutually detrimental outcomes. The proposed alliance solution method addresses the multi-vehicle simultaneous conflict problem by employing a structured, step-by-step approach that involves conflict decoupling and classification. The following important findings are derived from simulation analysis: the CAV achieves swarm intelligence robust control in a heterogeneous traffic environment through a standard alliance game, which not only effectively ensures safe passage, but also increases the passage efficiency of heterogeneous traffic flow by at the very least 10%, and the suggested approach works better in situations with low densities and high CAV penetration rates.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573962","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}

引用次数: 0

A Self-Correction Transformer Network for Traffic Flow Prediction Under Dynamic Spatio-Temporal Distributions 动态时空分布下的自校正变压器网络交通流预测

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-07 DOI: 10.1049/itr2.70044

Jingru Sun, Ziyu Qiu, Yichuang Sun, Oluyomi Simpson

{"title":"A Self-Correction Transformer Network for Traffic Flow Prediction Under Dynamic Spatio-Temporal Distributions","authors":"Jingru Sun, Ziyu Qiu, Yichuang Sun, Oluyomi Simpson","doi":"10.1049/itr2.70044","DOIUrl":"10.1049/itr2.70044","url":null,"abstract":"Precise and timely traffic flow prediction plays a critical role in developing intelligent transportation systems and has attracted considerable attention in recent decades. The traffic flow has a non-stationary character in both time and space, when the drift phenomenon appears, the traffic flow undergoes significant and sudden changes, bringing the challenge to the prediction. This paper proposed a self-supervised learning-based adaptive spatiotemporal self-correction transformer traffic flow prediction network (SCTNet). SCTNet can feel the drift with self-supervised learning, compute distribution features of the test data, obtain the distribution difference signal, feed it into the model as network correction information, and then adjust the spatiotemporal dependence of traffic flow adaptively to enhance prediction accuracy. The self-supervised learning method can adjust the model quickly and smoothly, and be utilized in most existing traffic flow prediction models. The experiments demonstrate that compared to existing models, the proposed self-supervised learning SCTNet has achieved state-of-the-art performance and exhibited strong adaptability to the dynamically changing spatiotemporal distributions of traffic data.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144573605","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}

引用次数: 0

Analysing Traffic Accidents in Terms of Driver Violation Behaviour Types: Machine Learning and Sensitivity Analysis Approaches 基于驾驶员违规行为类型的交通事故分析：机器学习和敏感性分析方法

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-04 DOI: 10.1049/itr2.70057

Emre Kuşkapan, Muhammed Yasin Çodur, Dilum Dissanayake

{"title":"Analysing Traffic Accidents in Terms of Driver Violation Behaviour Types: Machine Learning and Sensitivity Analysis Approaches","authors":"Emre Kuşkapan, Muhammed Yasin Çodur, Dilum Dissanayake","doi":"10.1049/itr2.70057","DOIUrl":"10.1049/itr2.70057","url":null,"abstract":"Traffic accidents have become a major concern for governments, organizations and individuals worldwide due to the material and moral losses they cause. It is possible to reduce this concern by taking into account the research conducted by relevant institutions and organizations in this field. The main objective of this study is to categorize traffic accidents according to driver violation types and analyse them using machine learning algorithms and feature sensitivity to identify the most influential variables in each category. For this purpose, traffic accident reports that occurred in Erzurum province in the last 1 year were used to categorize and classify driver violation behaviour types. Five different machine learning algorithms, namely k-nearest neighbour, support vector machines, naive Bayes, multilayer perception and random forest, were used to examine the success performance of the classification. Among these, 91% successful classification was obtained with the random forest algorithm. Based on the classification obtained from this algorithm, sensitivity analysis was used to reveal the variables that most affect each violation category. The results of the analysis revealed that driver age and vehicle type were the most influential variables for many types of violations. Thanks to this study, the problems were clearly identified by going into the details of driver violation behaviours. At the end of the study, measures to reduce driver violation behaviours were proposed. If the recommendations that can reduce driver behaviour are taken into consideration by transportation authorities and policy makers, traffic accidents can be significantly reduced.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550999","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}

引用次数: 0

Decision Making Improvement Based on the Ahead Traffic Flow During the Lane Change Manoeuvre Via Model Predictive Control 基于模型预测控制的前方交通流变道机动决策改进

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-03 DOI: 10.1049/itr2.70054

Mohsen Rafat, Shahram Azadi, Mozhgan Faramarzi, Ali Analooee

{"title":"Decision Making Improvement Based on the Ahead Traffic Flow During the Lane Change Manoeuvre Via Model Predictive Control","authors":"Mohsen Rafat, Shahram Azadi, Mozhgan Faramarzi, Ali Analooee","doi":"10.1049/itr2.70054","DOIUrl":"10.1049/itr2.70054","url":null,"abstract":"This paper proposes a novel decision-making framework that combines the influence of ahead traffic flow with the driver's personal decisions, thereby addressing the impact of transient traffic flow on lane-change decision-making. The presented algorithm can design safe trajectories without any collisions at any time of the manoeuvre considering the effects of ahead traffic flow on future decisions of the surrounding vehicles and sudden independent decisions of the surrounding vehicles during the lane change manoeuvre. In order to combine the microscopic and macroscopic models of the traffic environment around the ego vehicle, the ahead traffic flow is modelled and it is combined with the independent movements of the front vehicle in the target lane that is due to the driver's personal decisions. Using the model-based predictive control, the effects of these changes are investigated during the lane change manoeuvre. The algorithm successfully completed all lane change manoeuvres with collision avoidance considering the changes in surrounding vehicles caused by the ahead traffic flow. The performance of the proposed algorithm is simulated in complicated lane change manoeuvre regarding transient changes in the traffic flow and it is validated in IPG Automotive (IPG CarMaker) dynamic environment considering surrounding vehicles. The results indicate the desired performance of the proposed algorithm regarding macroscopic and microscopic changes around the ego vehicle even during the lane change manoeuvre.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536901","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}

引用次数: 0

The Taxi-Sharing: User Equilibrium, System Optimum and Pricing Scheme Design 出租车共享：用户均衡、系统最优与定价方案设计

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-02 DOI: 10.1049/itr2.70043

Zixuan Peng, Peng Jia

引用次数: 0

Electric Vehicle Routing With Recharging Stations: Trade-Offs in Last-Mile Delivery 带有充电站的电动汽车路线：最后一英里交付的权衡

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-07-01 DOI: 10.1049/itr2.70052

Sinem Bozkurt Keser, İnci Sarıçiçek, Ahmet Yazıcı

{"title":"Electric Vehicle Routing With Recharging Stations: Trade-Offs in Last-Mile Delivery","authors":"Sinem Bozkurt Keser, İnci Sarıçiçek, Ahmet Yazıcı","doi":"10.1049/itr2.70052","DOIUrl":"10.1049/itr2.70052","url":null,"abstract":"Last-mile logistics increasingly adopt electric vehicles to address environmental concerns and reduce operational costs. Unlike classical vehicle routing problems, it is essential to consider charging stations in the route planning for electric vehicles. This study aims to investigate the effect of different charging strategies on last-mile delivery optimisation. The adaptive large neighbourhood search (ALNS) algorithm is proposed to solve large-scale problems. The results of the proposed algorithm are compared with the results of the mathematical model in small-scale problems, and the algorithm's performance is proven. The proposed algorithm contributes to electric vehicle route planning by providing effective results in solving large-scale problems. The test problems are solved with three different charging strategies: full charging, partial charging, and partial charging between 20–80% state of charge (SoC). Solutions have been obtained for the objective functions of the minimising total distance, the minimizing total time, and the minimising total energy consumption. The results of the experiments show that the average charging time is the lowest when the total travel time is minimised, the highest values are reached when the total distance is minimised, and more balanced results are provided when the energy consumption is minimised. These findings help logistics companies to determine the most appropriate charging strategy in terms of operational efficiency and cost optimisation.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524962","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}

引用次数: 0

FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway 电气化铁路受电弓接触网系统的物理信息动力学预测模型

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-06-25 DOI: 10.1049/itr2.70059

Wenping Chu, Hui Wang, Yang Song, Zhigang Liu

{"title":"FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway","authors":"Wenping Chu, Hui Wang, Yang Song, Zhigang Liu","doi":"10.1049/itr2.70059","DOIUrl":"10.1049/itr2.70059","url":null,"abstract":"In electric railways, the interaction performance between the pantograph and catenary is crucial for maintaining a stable current supply. Establishing high-fidelity numerical models using the finite element method is generally desirable, yet it involves considerable computational complexity and time demands. In this paper, we propose a novel dynamic prediction model that integrates physical information and data-driven approaches to solve the pantograph-catenary interaction, called FENet. Specifically, there are two significant aspects: (1) A deep learning framework is developed for efficient simulation. The network utilises the temporal convolutional network to extract short-term local features. Simultaneously, the attention-based long short-term memory is leveraged to capture the long-term dependencies in the interaction sequence. FENet establishes the dynamic relationship between the system state and excitation variables, achieving fast and accurate simulation. (2) We integrate multiple physics-informed loss terms to handle implicit constraints within motion equations, which leverages physical principles to guide the learning process. Additionally, a dynamic weighting mechanism adaptively balances the contributions of various terms in the physics-based loss function. Experimental results reveal that FENet exhibits effectiveness and robustness against different external excitations and achieves long-term dynamic response prediction with negligible computational effort. Moreover, it shows promising potential for real-time simulation and feedback in pantograph hardware-in-the-loop test rigs.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144482197","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}

引用次数: 0

Multi-Feature Learning-Based Automatic Recognition of Non-Normative Seafarer Behaviours to Promote Maritime Traffic Safety 基于多特征学习的海员不规范行为自动识别促进海上交通安全

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-06-23 DOI: 10.1049/itr2.70039

Mengwei Bao, Chenjie Zhao, Nian Liu, Ryan Wen Liu

{"title":"Multi-Feature Learning-Based Automatic Recognition of Non-Normative Seafarer Behaviours to Promote Maritime Traffic Safety","authors":"Mengwei Bao, Chenjie Zhao, Nian Liu, Ryan Wen Liu","doi":"10.1049/itr2.70039","DOIUrl":"10.1049/itr2.70039","url":null,"abstract":"In maritime navigation, seafarers' non-normative behaviours can significantly increase the likelihood of maritime accidents and lead to substantial losses. While monitoring equipment and computer vision technology are extensively employed in intelligent transportation systems (ITSs), behaviour detection within ship bridge situations is still rather scarce. We have constructed a dataset concentrating on non-normative behaviours within the ship's bridge environments, tackling the data scarcity problem in this domain. We initially extract essential information for later behaviour analysis by integrating an attention module with an object detection network, owing to the complexity of scenes in video surveillance. Meanwhile, we propose a behaviour recognition network utilizing multi-feature learning (termed MFLNet) to precisely assess seafarer activities in critical areas. In particular, MFLNet adaptively synthesizes seafarer appearance and posture through a compression and incentive module, enhancing recognition accuracy and mitigating sample imbalance issues. Extensive qualitative and quantitative experiments indicate that the MFLNet attains superior speed and accuracy for recognizing non-normative seafarer behaviours.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339468","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}

引用次数: 0

Graph Wavelet Neural Controlled Differential Equations Method for Speed Prediction Under Traffic Accidents 交通事故下图小波神经控制微分方程速度预测方法

IF 2.5 4区工程技术

IET Intelligent Transport Systems Pub Date : 2025-06-12 DOI: 10.1049/itr2.70047

Zihao Wei, Ke Zhang, Shen Li, Meng Li

{"title":"Graph Wavelet Neural Controlled Differential Equations Method for Speed Prediction Under Traffic Accidents","authors":"Zihao Wei, Ke Zhang, Shen Li, Meng Li","doi":"10.1049/itr2.70047","DOIUrl":"10.1049/itr2.70047","url":null,"abstract":"Accurate speed prediction is a crucial component of intelligent transportation systems, as it enhances traffic management and operational efficiency. While the majority of existing research concentrates on speed prediction under normal traffic conditions, the occurrence of traffic accidents significantly disrupts typical urban traffic patterns, leading to reduced predictive accuracy. Considering that the disruption caused by accidents is localized and severe, and that the dynamic behavior of traffic flow can be effectively modeled through differential equations, we propose a novel traffic speed prediction model, graph wavelet neural controlled differential equations (GW-NCDE). The GW-NCDE model leverages graph wavelet transforms to effectively capture the spatial characteristics of the road network under accident conditions and employs a dual-layer neural controlled differential equation structure for enhanced predictive performance. Experiments conducted on a real-world dataset from Wangjing, Beijing, demonstrate that our model outperforms several existing benchmark methods. Particularly in accident scenarios, compared to the best-performing benchmark, the short-term prediction error of our model is reduced by more than 10%. These results underscore the model's robustness and superior predictive capability in complex and dynamic urban traffic environments.","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264678","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}

引用次数: 0