IET Intelligent Transport Systems最新文献

{"title":"Analysing Traffic Accidents in Terms of Driver Violation Behaviour Types: Machine Learning and Sensitivity Analysis Approaches","authors":"Emre Kuşkapan,&nbsp;Muhammed Yasin Çodur,&nbsp;Dilum Dissanayake","doi":"10.1049/itr2.70057","DOIUrl":"https://doi.org/10.1049/itr2.70057","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"Decision Making Improvement Based on the Ahead Traffic Flow During the Lane Change Manoeuvre Via Model Predictive Control","authors":"Mohsen Rafat,&nbsp;Shahram Azadi,&nbsp;Mozhgan Faramarzi,&nbsp;Ali Analooee","doi":"10.1049/itr2.70054","DOIUrl":"https://doi.org/10.1049/itr2.70054","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"The Taxi-Sharing: User Equilibrium, System Optimum and Pricing Scheme Design","authors":"Zixuan Peng,&nbsp;Peng Jia","doi":"10.1049/itr2.70043","DOIUrl":"https://doi.org/10.1049/itr2.70043","url":null,"abstract":"<p>Taxi-sharing service involves three stakeholders: passengers, taxi drivers and platform. From the interests of different stakeholders, taxi-sharing equilibrium assignment and taxi-sharing system optimum assignment can be obtained which may be not consistent with each other. This paper addresses two decisions for taxi-sharing service: matching passengers and taxis and pricing with compensation. Nonlinear equations are formulated to describe the equilibrium assignment of taxis to passengers. A price scheme is designed to steer a taxi-sharing equilibrium assignment to a system optimum assignment. The models of taxi-sharing equilibrium assignment, optimum assignment, equilibrium assignment with compensation and the algorithm for finding optimum compensation vectors are validated by case studies based on data of Dalian taxi companies. The findings show that in some cases, a sub-optimal assignment is achieved. After relaxing the equilibrium constraint, most of the cases can get system optimum assignment by compensations.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524923","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":"Electric Vehicle Routing With Recharging Stations: Trade-Offs in Last-Mile Delivery","authors":"Sinem Bozkurt Keser,&nbsp;İnci Sarıçiçek,&nbsp;Ahmet Yazıcı","doi":"10.1049/itr2.70052","DOIUrl":"https://doi.org/10.1049/itr2.70052","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"FENet: A Physics-Informed Dynamics Prediction Model of Pantograph-Catenary Systems in Electric Railway","authors":"Wenping Chu,&nbsp;Hui Wang,&nbsp;Yang Song,&nbsp;Zhigang Liu","doi":"10.1049/itr2.70059","DOIUrl":"https://doi.org/10.1049/itr2.70059","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"Multi-Feature Learning-Based Automatic Recognition of Non-Normative Seafarer Behaviours to Promote Maritime Traffic Safety","authors":"Mengwei Bao,&nbsp;Chenjie Zhao,&nbsp;Nian Liu,&nbsp;Ryan Wen Liu","doi":"10.1049/itr2.70039","DOIUrl":"https://doi.org/10.1049/itr2.70039","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"Graph Wavelet Neural Controlled Differential Equations Method for Speed Prediction Under Traffic Accidents","authors":"Zihao Wei,&nbsp;Ke Zhang,&nbsp;Shen Li,&nbsp;Meng Li","doi":"10.1049/itr2.70047","DOIUrl":"https://doi.org/10.1049/itr2.70047","url":null,"abstract":"<p>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.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"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}

{"title":"Optimisation of Lane-Level Dynamic Traffic Control Strategy Based on Bidirectional Adaptive Gated Graph Convolutional Network and Deep Reinforcement Learning","authors":"Shaowei Sun,&nbsp;Mingzhou Liu","doi":"10.1049/itr2.70055","DOIUrl":"https://doi.org/10.1049/itr2.70055","url":null,"abstract":"<p>This paper innovatively proposes a lane-level dynamic traffic control strategy optimisation method integrating the bidirectional adaptive gated graph convolutional network (Bi-AGGCN) and deep reinforcement learning (DRL). The core innovation lies in three aspects. First, Bi-AGGCN is introduced to precisely capture the spatiotemporal dependencies of traffic flow by simultaneously considering forward and backward information, overcoming the limitations of traditional unidirectional models. Second, an improved deep Q-network (DQN) algorithm is adopted, incorporating a dual network structure, experience pool sampling strategy, and dominance function, which effectively enhances the learning speed and estimation accuracy of the value function. Third, the combination of Bi-AGGCN and DRL enables the framework to automatically adjust traffic signal parameters based on real-time traffic flow, achieving dynamic optimisation of traffic flow. Experimental results indicate that compared with traditional timed signal control (FTSC), fast Q-learning (FQ learning), and modified DQN (M-DQN) algorithms, the proposed Bi-AGGCN + DRL model demonstrates significant advantages. In the experiment, the traffic flow of this model reaches 2600 pcu/h, the delay time is reduced to 90 s, the lane-level control response speed is shortened to 5 s, and the average lane speed is increased to 110 km/h. This verifies the efficiency and feasibility of the model in lane-level traffic control, providing feasible technical support and optimisation directions for the traffic management of actual highways.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144256402","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 Hybrid Optimization Algorithm for Static Rebalance Problem of Bicycle-Sharing System","authors":"Shuhui Liang,&nbsp;Yutong Ye,&nbsp;Jingli Wu","doi":"10.1049/itr2.70050","DOIUrl":"https://doi.org/10.1049/itr2.70050","url":null,"abstract":"<p>Although the bicycle-sharing system plays an important role in easing urban traffic congestion and reducing carbon emissions, it still suffers from the problem of unbalanced bicycle distribution between different stations, which significantly limits the utilization of bicycles. To solve this problem, this paper proposes a novel hybrid optimization algorithm, MixPS, based on the combination of a partheno-genetic algorithm and a simulated annealing algorithm. This algorithm can effectively rebalance the distribution of bicycles. To further improve performance, we design a decimal code to represent a scheduled path and introduce six mutation operators to change the chromosome. The whole evolution is controlled with the simulated annealing process. Moreover, we apply the Metropolis acceptance criterion to effectively reduce the possibility that the population falls into the local optimum. Comprehensive experimental results obtained from both synthetic and real-world data sets show that our proposed MixPS algorithm can achieve better optimization and generate a shorter schedule path scheme in less time. These results confirm the efficiency and effectiveness of our method in solving the static bicycle rebalancing problem with single-vehicle and multiple-access.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206387","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":"TrustChain-VANETs: Blockchain and IPFS Integration for Reliable and Secure Vehicular Communication in Intelligent Transportation Systems (ITS)","authors":"Zia Ullah,&nbsp;Ghassan Husnain,&nbsp;Abid Iqbal,&nbsp;Ibrar Ali Shah,&nbsp;Ali Saeed Alzahrani,&nbsp;Ramasamy Srinivasaga Naidu,&nbsp;Yazeed Yasin Ghadi,&nbsp;Mohammed S. Al-Zahrani","doi":"10.1049/itr2.70051","DOIUrl":"https://doi.org/10.1049/itr2.70051","url":null,"abstract":"<p>Vehicular ad-hoc networks (VANETs) are pivotal in intelligent transportation systems (ITS), enabling enhanced traffic efficiency and safety. However, VANETs within ITS face critical challenges related to trust, privacy, and data reliability. To address these issues, this paper proposes a comprehensive solution that integrates blockchain and InterPlanetary file system (IPFS) technologies for ITS applications. We introduce a blockchain-based trust management system, TrustChain-VANETs, designed to ensure message credibility, privacy, and data reliability in ITS environments. Our model safeguards vehicle privacy while enabling credible messages to be shared through anonymous aggregate vehicular announcements, an essential feature for ITS. Reputation values, stored in the blockchain, allow roadside units (RSUs) to assess message reliability, achieving a 15% higher malicious vehicle detection rate compared to traditional methods at low probabilities of false reporting, crucial for trust in ITS. Additionally, conditional privacy is maintained by tracking malicious entities through public addresses, ensuring accountability in ITS. The system leverages IPFS on RSUs for secure, reliable data storage, with aggregated event data from vehicles stored in IPFS and vehicle reputation values maintained on the blockchain, addressing storage and cost challenges in ITS. This approach reduces transaction costs by 20% and decreases storage overhead by 30%, enhancing the efficiency of ITS data sharing. An incentive mechanism encourages honest data sharing among vehicles, with monetary rewards for aligning with verified event information, transparently recorded on the blockchain. Performance analysis demonstrates that TrustChain-VANETs reduces message verification time by an average of 25% compared to traditional proof-of-work blockchain models, making it suitable for the dynamic and demanding nature of ITS. This innovative framework addresses critical challenges in VANETs, delivering robust, scalable, and efficient solutions for security, privacy, and reliability in ITS.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197523","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}