IET Intelligent Transport Systems最新文献

{"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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":"https://doi.org/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":2.3,"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}

{"title":"Understanding Urban Mobility Responses to Extreme Precipitation Events: A Case Study of Zhengzhou, China","authors":"Qian Ye,&nbsp;Yulin Ma,&nbsp;Shucai Xu,&nbsp;Miguel Ángel Sotelo,&nbsp;Zhixiong Li","doi":"10.1049/itr2.70045","DOIUrl":"https://doi.org/10.1049/itr2.70045","url":null,"abstract":"<p>Cities are increasingly vulnerable to the impacts of extreme weather events, understanding the travel responses to such events can support needs-based emergency resource allocation and long-term resilience planning. Although previous research has advanced our understanding of travel patterns during abnormal conditions, knowledge remains limited regarding how urban travel changes spatially (e.g. by different spatial clusters) during extreme precipitation events. To address this research gap, this study aims to assesses how urban travel, by number of trips, changed in response to extreme precipitation events using time series clustering and discrete choice modelling. The study uses cell phone signalling-based mobility big data before, during, and after the perturbations, with the 2021 Zhengzhou floods as a case study. Moreover, the aggregated responses of urban travel were empirically defined in terms of the magnitude of trip reduction and time-to-recovery, i.e. travel resilience. The study identifies four distinct groups that exhibit comparable response and recovery patterns, which can be subject to the influence of factors associated with the built environment. The study reveals that areas with higher road network density are comparatively more vulnerable to the initial impact of extreme rainfall and flooding. Nevertheless, taking a long-term perspective, higher road network density contributes to faster recovery and more robust travel resilience. Moreover, home-based work trips are less sensitive to red rainfall warnings than home-based other trips and non-home-based trips. This study provides valuable implications for planners and policymakers to resist similar future events.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944569","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":"Fixed-Time Anti-Disturbance Sliding Mode Control Based on a Novel Variable Time Headway Policy for Vehicle Platoon","authors":"Xin Yang,&nbsp;Cheng-Lin Liu","doi":"10.1049/itr2.70037","DOIUrl":"https://doi.org/10.1049/itr2.70037","url":null,"abstract":"<p>This paper primarily studies the control problem of disturbed vehicle platoon using fixed-time control methods. With the proposed variable time headway policy, vehicles can maintain the desired distance to the preceding vehicle during constant speed cruising in advance based on their acceleration. Additionally, a fixed-time disturbance observer is introduced to estimate the disturbances. The introduction of adaptive law and the hyperbolic tangent function, which replaces the sign function, aims to mitigate the chattering issue of the controller. Subsequently, a fixed-time anti-disturbance control strategy is proposed. To address the transient issues caused by non-zero initial spacing errors, another improved approach is suggested. The effectiveness and superiority of the proposed solutions are verified through numerical simulations.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944568","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 Systematic Review on Vision-Based Traffic Density Estimation for Intelligent Transportation Systems","authors":"Muhammad Ardi Putra,&nbsp;Agus Harjoko,&nbsp; Wahyono","doi":"10.1049/itr2.70038","DOIUrl":"https://doi.org/10.1049/itr2.70038","url":null,"abstract":"<p>Traffic congestion is often considered one of the major challenges faced in urban areas. It is important to address this issue due to its significant negative impacts on both society and the environment, including decreased productivity and increased pollution. For this reason, implementing a traffic density estimation system is necessary as it can be further integrated into adaptive traffic control systems that dynamically adjust traffic lights based on real-time congestion levels. Different from existing papers that categorise vision-based traffic density estimation methods into microscopic and macroscopic approaches, this paper contributes a novel taxonomy by introducing hybrid approach, which combines the two to leverage their respective advantages. Furthermore, this review paper offers guidance for future research on this topic. Later in the discussion, the three approaches for estimating traffic density will be broken down into specific methods used, namely image processing techniques, machine learning models, deep learning models, or a combination of them. This paper also provides a coherent discussion of the details of these papers, as well as their advantages and drawbacks. To the best of our knowledge, this is the first review paper that specifically discusses traffic density estimation methods based exclusively on image and video data.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938987","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}