IET Intelligent Transport Systems最新文献

{"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":"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":2.5,"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":"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":2.5,"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":"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":2.5,"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}

{"title":"An Ensemble Decision Trees Model to Predict Traffic Pattern for Maritime Traffic Management","authors":"Zhao Liu,&nbsp;Weipeng Zuo,&nbsp;Hua Shi,&nbsp;Wanli Chen,&nbsp;Xiao Lang,&nbsp;Wengang Mao,&nbsp;Mingyang Zhang","doi":"10.1049/itr2.70049","DOIUrl":"10.1049/itr2.70049","url":null,"abstract":"<p>This study presents a traffic pattern prediction model using ensembles of decision trees, leveraging AIS data to classify maritime traffic patterns. The model integrates static information, such as origin and destination, with dynamic data, including ship speed, course and spatial position, to define and extract relevant traffic features. By combining traditional algorithms with a decision tree ensemble model, a stacked predictive framework is constructed and trained on these extracted traffic characteristics. The model is applied and validated using data from the Fujiangsha waters of the Jiangsu section of the Yangtze River. Comparative analysis reveals that this model consistently outperforms traditional algorithms and ensemble models, maintaining stable accuracy above 98% across diverse scenarios. Testing on unseen ship data further confirms the model's predictive reliability, aligning well with actual navigation patterns. The findings suggest that this model has strong potential to (1) forecast navigation routes for improved traffic management, (2) infer ship behaviour based on predicted traffic patterns and (3) support future applications in intelligent ship navigation.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171480","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":"Lateral and Longitudinal Trajectory Optimisation Method for a Mixed Fleet of Connected and Automated Vehicles Passing Through Multiple Continuous Intersections","authors":"Wenjuan E,&nbsp;Yao Li,&nbsp;Xiangwang Hu,&nbsp;Shiwei Ma,&nbsp;Feifan Du,&nbsp;Xiang Wang","doi":"10.1049/itr2.70048","DOIUrl":"10.1049/itr2.70048","url":null,"abstract":"<p>The efficiency of signalised intersections affects the overall performance of urban transportation systems. Despite connected and automated vehicle (CAV) technology revolutionarily enables individual-level cooperative control, most existing studies have limited capability in simultaneously considering lane-changing and car-following behaviours of vehicles while passing through continuous intersections. This study proposes a novel trajectory optimisation method for a mixed fleet of CAVs and human-driven vehicles, including lateral and longitudinal behaviour control. The method constructs a lane-changing trajectory optimisation model based on the lane-changing intention generated by CAVs and formulates safety constraints, lane occupancy state assignment and lane-changing cost function constraints. It also establishes a longitudinal following model based on vehicle role switching protocols to realise different constituent fleets passing through signalised intersections with minimal stops. Simulation results show that the proposed method can reduce the average travel time by up to 26.77%, decrease average travel delay by up to 42.66%, minimise the average number of stops by up to 91% and lower fuel consumption and pollutant emissions by more than 28%. After multiple independent experiments are conducted, the 95% confidence intervals are determined as follows: [101.09 s, 101.53 s] for average travel time, [49.77 s, 50.17 s] for travel delay and [111.22 g, 132.38 g] for fuel consumption. Parking behaviour analysis yields [0.68 s, 0.86 s] for average parking time and [0.16, 0.18] occurrences per vehicle for stop frequency. Sensitivity analyses of signal cycle length and guidance zone length demonstrate that the guidance effect of the proposed control strategy is stable under various settings.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148507","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":"Correction to “Adaptive Joint Control of Intersection Traffic Signals and Variable Lanes Using Multi-Agent Learning”","authors":"","doi":"10.1049/itr2.70046","DOIUrl":"10.1049/itr2.70046","url":null,"abstract":"<p>M. Wang, H. Wang, S. Wei, and D. Zhang, “Adaptive Joint Control of Intersection Traffic Signals and Variable Lanes Using Multi-Agent Learning,” <i>IET Intelligent Transport Systems</i>, 19 (2025): e70032. https://doi.org/10.1049/itr2.70032</p><p>In the above-mentioned article, the authors would like to correct the following errors:</p><p>1. Corresponding Author Correction</p><p>The article incorrectly lists both Menglin Wang and Haiyong Wang as corresponding authors.</p><p>The correct corresponding author is Haiyong Wang.</p><p>2. Affiliation Correction</p><p>The affiliation was incorrectly stated as:</p><p>Department of Electronic and Information Engineering, Lanzhou Jiaotong University, Gansu, China.</p><p>The correct affiliation is:</p><p>School of Electronic and Information Engineering, Lanzhou Jiaotong University, Gansu, China.</p><p>This correction reflects an official institutional update that was not incorporated into the final version.</p><p>We apologize for this error.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108856","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 Virtually Coupled Train Control Model Under Allowable Safe Distance Range Based on Vehicle-Following Approach With Operational Hazard Analysis and the Labelled Transition System","authors":"Naphat Ketphat,&nbsp;Somchai Pathomsiri","doi":"10.1049/itr2.70033","DOIUrl":"10.1049/itr2.70033","url":null,"abstract":"<p>The virtual coupling system has been developed for controlling trains operating as a convoy. To achieve this, an effective approach to virtually merge trains into the same convoy, operating safely in normal situations and in emergencies, is essential. This paper proposes a new virtually coupled train control model based on the vehicle-following approach and operational hazard analysis that can ensure safe operation. Unlike existing models in previous works, the proposed model is generalised and flexible for real operations, allowing for the coupling of different train types with varying acceleration and deceleration capabilities and variable safe separation distance. The comprehensive set of operational states is created by adopting the labelled transition system to determine all interconnected state movements which can control the following train based on the preceding train operation. Suitable acceleration and deceleration equations for initial, virtual coupling, and emergency states are introduced to improve coupling capability and ensure safety in all operational states. Moreover, the minimum safe distance equation is modified to ensure safety and provide riding comfort by preventing fluctuating movement of trains in the convoy. The proposed model was simulated by using MATLAB and applied to a 250 km high-speed train line linking Thailand and Laos. The simulation includes normal train operations, varying acceleration and deceleration capabilities, communication time delays, and emergency scenarios such as unintentional stops, communication loss, and temporary speed restrictions. The simulation results demonstrate that the proposed model can accommodate virtual coupling of any train type, various braking capabilities, and a safe distance range, whereas it enhances capacity and guarantees operational safety. The following trains smoothly operate in coordination with the preceding train to maintain a safe separation distance, thereby preventing collisions between trains.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100602","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":"Fare Structure Optimization of Intercity Railway With Regional Passenger Income Disparities","authors":"Ninghai Li,&nbsp;Baohua Mao,&nbsp;Junsheng Huang,&nbsp;Fang Wen,&nbsp;Gang Xu","doi":"10.1049/itr2.70041","DOIUrl":"10.1049/itr2.70041","url":null,"abstract":"<p>The fare structure of intercity railways is crucial for passenger service and sustainable operation. The Current distance-based fare structure (CFS) and a regional distance-based fare structure (RDFS) are investigated to achieve breakeven under limited subsidies. Passenger choice behaviors are measured by a nested-logit (NL) model with prospect theory, considering regional passenger income disparities. A nonlinear programming model is proposed to determine the fare structure and departure headway, of which the objective function maximizes the social welfare, including operator revenue and passenger turnover. A multiple-population genetic algorithm with an elite strategy is designed to solve the problem. The case study shows that the proposed RDFS can effectively increase operator revenue by 5.88%, while sacrificing only 1.73% of the turnover compared to the current fare structure. Under limited subsidies, the RDFS reduces the required passenger demand by 5.70% for achieving breakeven. RDFS consistently shows the highest social welfare under varying passenger income levels among cities and commuter proportions.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091876","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":"Using Scene-Flow to Improve Predictions of Road Users in Motion With Respect to an Ego-Vehicle","authors":"Nilusha Jayawickrama,&nbsp;Risto Ojala,&nbsp;Kari Tammi","doi":"10.1049/itr2.70010","DOIUrl":"10.1049/itr2.70010","url":null,"abstract":"<p>We addressed the challenge of accurately determining the motion status of vehicles neighbouring an ego-vehicle, across various driving scenarios. The aim was to enhance the prediction accuracy in identifying moving vehicles through the integration of scene-flow analysis into tracking. The research was motivated by the importance, in autonomous driving, of analysing the state exclusively of moving vehicles. We implemented a novel, synergistic, vision-based, and offline approach, named MoVe, combining spatial analysis of predicted scene-flows and temporal tracking, from sensor-fused input data. Regions of moving vehicles (post background refinement) were obtained via instance segmentation, and each instance mapped to the corresponding (original) scene flows. Our method achieved an <span></span><math>\u0000 <semantics>\u0000 <mi>F</mi>\u0000 <annotation>$F$</annotation>\u0000 </semantics></math>1 score of 0.953 and accuracy of 0.959 for binary motion classification (stationary vs. moving). The proposed fusion segmentation model produced an mIoU of 82.29% for cars, outperforming YOLOv7 which relies solely on visual features. Notably, we observed a complementary dynamic between scene-flow analysis and tracking. Scene-flow analysis was generally effective in identifying fast moving vehicles, even under occlusions or truncations caused by other vehicles or infrastructure elements, while tracking usually excelled in identifying comparatively slow moving vehicles. Thus, the study demonstrated the viability of our proposed architecture to improve the detection of moving vehicles around an ego-vehicle. The outcomes further suggested the potential of our work to be utilised for training future deep learning models based on machine vision and attention, such as object-centric learning, which paves the way for enhancing perception, intent estimation, control strategies, and safety in autonomous driving.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091878","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":"Impacts of External Factors on Crash Injury Severity in Urbanised Areas: An Exploratory Analysis","authors":"Zhenyu Mei,&nbsp;Jinrui Gong,&nbsp;Zuchen Que,&nbsp;Jianchao Pan","doi":"10.1049/itr2.70040","DOIUrl":"10.1049/itr2.70040","url":null,"abstract":"<p>The safety of urban roads is closely intertwined with residents' daily travel and has consistently been an important research topic of concern. In addition to the subjective behaviour of drivers, understanding the impact of external environmental factors on the severity of crashes is critical to risk management. As a result, this study employed a Bayesian Optimisation-Light Gradient Boosting Machine (BO-LightGBM) to investigate the effects of land use, weather and road conditions on the severity of urban car crashes on workdays and holidays. Additionally, the Shapley Additive explanation (SHAP) was adopted to explore the non-linear effects of the variables. The dataset was records of car crashes in the main urban area of Hangzhou from 2011 to 2017, a period with rapid urbanisation. The results indicate that the LightGBM model achieves a significant performance boost and outperforms traditional regression models and XGBoost after Bayesian optimisation. Crashes that occur in the office and congested areas on workdays generally result in less severe injuries; the temperature, humidity and visibility show strong correlations with crash severity. The findings also highlight which areas are more likely to produce serious crash injuries and provide insights into urban crash prevention.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143950198","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}