IET Intelligent Transport Systems最新文献

{"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}

{"title":"Adaptive Joint Control of Intersection Traffic Signals and Variable Lanes Using Multi-Agent Learning","authors":"Menglin Wang,&nbsp;Haiyong Wang,&nbsp;Sheng Wei,&nbsp;Dan Zhang","doi":"10.1049/itr2.70032","DOIUrl":"https://doi.org/10.1049/itr2.70032","url":null,"abstract":"<p>To effectively manage varying traffic flows at urban intersections during peak and off-peak hours, especially under conditions of unbalanced directional demand, we propose a learning-based coordination method for traffic signal control and variable-direction lane control (LCSL) to alleviate traffic congestion. The framework integrates a variable-direction lane control module and a traffic signal control module, leveraging mutual interaction and real-time information sharing to enable dynamic, coordinated decision-making. Additionally, we design an adaptive reward function based on lane balancing and traffic demand to enhance the adaptive coordination between agents. The use of a prioritized experience replay (Pr) mechanism further enhances the efficiency of experience utilization, accelerates algorithm convergence, and ensures the adaptive stability of the agents across varying traffic conditions. The experimental findings indicate that the LCSL method effectively decreases the average delay by 33.5% and the queue length by 48.2%, compared to the current state-of-the-art techniques, exhibiting higher stability and efficiency and improving intersection throughput.</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/itr2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143939024","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":"Lane-Changing-Enabled Eco Approach Control for Autonomous Vehicles at a Signalized Intersection in Mixed Traffic Environment","authors":"Jiaqi Liu,&nbsp;Ting Qu,&nbsp;Shiying Dong,&nbsp;Bingzhao Gao","doi":"10.1049/itr2.70036","DOIUrl":"https://doi.org/10.1049/itr2.70036","url":null,"abstract":"<p>In complex urban traffic environments, vehicle-to-everything technology can reduce energy consumption and optimize travel time for vehicles at a signalized intersection. We propose a lane-changing-enabled eco approach control strategy that considers constraints from surrounding vehicles, traffic lights, and queues ahead. Our strategy adopts a hierarchical receding horizon control framework that provides the connected and autonomous vehicle with optimal lane and speed planning for adapting to dynamic traffic environment. In the upper level, we set up virtual traffic light for each lane via a mixed vehicle platoon model, utilizing their signal phase time constraints and constraints of surrounding vehicles to formulate a mixed-integer nonlinear programming problem to obtain optimal lane and speed reference. In the lower level, a car-following model is conducted in time domain and the reference speed is put into the controller to obtain the optimal velocity. Numerical experiment results show that our strategy is superior to regular eco-approach and departure strategy and lane-changing-enabled car-following strategy in terms of energy consumption reduction in the traffic scenarios without lane changing. In the traffic scenarios with lane changing, our strategy improves traffic efficiency by reducing travel time.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919570","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":"Dynamic Spatial-Temporal Propagation Neural Network for Airport Delay Forecasting Via Traffic Flow and Delay Embedding","authors":"Xin Zhang,&nbsp;Haibing Guan,&nbsp;Zhen Yan","doi":"10.1049/itr2.70031","DOIUrl":"https://doi.org/10.1049/itr2.70031","url":null,"abstract":"<p>Research on airport delay forecasting is essential for quickly identifying key bottleneck points within the airport network, which enables controllers to promptly assess traffic conditions and take appropriate actions. However, there are two major challenges that should be sincerely considered: (1) modeling the cascading effects of airport delay is often incomplete in existing prediction methods; (2) there is an underlying relationship between traffic flow and delays, but it is difficult to measure how flow changes impact airport delays. To address these problems, we propose a dual-path deep learning framework called the dynamic spatial-temporal propagation neural network (DSTPNN) to forecast airport delays. Specifically, the spatial-temporal convolutional embedding (STCE) module is designed to capture delay propagation patterns by considering dynamic and static spatial dependencies, together with multi-scale temporal features. To model the evolutionary correlations between traffic flow and delays, the auxiliary temporal embedding (ATE) module is first constructed based on causal and dilated convolution to learn high-level feature representations of traffic flow. Based on the output of both components, we design a traffic situation awareness attention (TSAA) mechanism that incorporates both convolution and cross-attention techniques to mine the potential causal relationships between traffic features (flow and delays) at the airport level. Experiments on a real-world dataset from the Bureau of Transportation Statistics (BTS) indicate that the proposed DSTPNN outperforms the baselines, obtaining relative improvements of at least 1.5% in MAE, 2.3% in RMSE, and 9.7% in <span></span><math>\u0000 <semantics>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 <annotation>${rm R}^2$</annotation>\u0000 </semantics></math>. Furthermore, ablation and analysis experiments demonstrate that each proposed technical block is instrumental in achieving the desired performance enhancements.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919801","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":"From Urban Data to City-Scale Models: A Review of Traffic Simulation Case Studies","authors":"Klavdiya Bochenina,&nbsp;Serio Agriesti,&nbsp;Claudio Roncoli,&nbsp;Laura Ruotsalainen","doi":"10.1049/itr2.70021","DOIUrl":"https://doi.org/10.1049/itr2.70021","url":null,"abstract":"<p>Depending on the availability of urban data and the scope of the study, various approaches has been proposed for large-scale modelling and simulation of vehicular mobility. However, studies ending up with real-world applications often adopt less efficient and simpler solutions for distinct parts of the simulation workflow (e.g., grid search for calibration instead of meta-heuristic approach) creating the discrepancy between state-of-the-art methods and the choices made by practitioners. This paper provides a systematised review on traffic simulation case studies based on a consolidated workflow for the creation and use of data-driven large-scale traffic simulations. We analyse and discuss the implementation of the various steps in the workflow, namely, data preparation, model implementation, model evaluation, refinement, and application. By reviewing more than 60 case studies from 23 countries, we identify trends and best practices in the design and development of city-wide traffic simulations, as well as formulate the current challenges and gaps that need to be addressed. As a result, this study summarises the current state-of-the-art techniques for implementing and applying large-scale traffic models and serves as a practical reference for urban researchers and practitioners who aim to develop new data-driven models for large-scale urban areas.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901007","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":"Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator","authors":"Somporn Sahachaiseree,&nbsp;Takashi Oguchi","doi":"10.1049/itr2.70034","DOIUrl":"https://doi.org/10.1049/itr2.70034","url":null,"abstract":"<p>Reinforcement learning (RL) is a promising machine-learning solution to traffic signal control problems, which have been extensively studied. However, variants of non-linear, deep artificial neural network (ANN) function approximators (FAs) have been predominantly employed in previous studies proposing RL-based controllers, leaving a significant interpretability issue due to their black-box nature. In this work, the use of the linear FA for a value-based RL agent in traffic signal control problems is investigated along with the least-squares <span></span><math>\u0000 <semantics>\u0000 <mi>Q</mi>\u0000 <annotation>$Q$</annotation>\u0000 </semantics></math>-learning method, abbreviated as <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>LSTD</mi>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>${rm LSTD}Q$</annotation>\u0000 </semantics></math>. The interpretable linear FA was found to be adequate for the RL agent to learn an optimal policy. This leads to the proposal to replace a non-linear ANN FA with the linear FA counterpart, resolving the interpretability issue. Moreover, the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>LSTD</mi>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>${rm LSTD}Q$</annotation>\u0000 </semantics></math> learning method shows superior behaviour convergence compared to a gradient descent method. In a low-intensity arrival pattern scenario, the control by the RL agent cuts about half of the average delay resulting from the pretimed control. Owing to the conciseness of the linear FA, a direct interpretation analysis of the converged linear-FA parameters is presented. Lastly, two online relearning tests of the agents under non-stationary arrivals are conducted to demonstrate the online performance of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>LSTD</mi>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>${rm LSTD}Q$</annotation>\u0000 </semantics></math>. In conclusion, the linear-FA specification and the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>LSTD</mi>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>${rm LSTD}Q$</annotation>\u0000 </semantics></math> method are together proposed to be used for its control algorithm interpretability property, superior convergence quality, and lack of hyperparameters.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884222","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 Case Study on Peri-Urban Public Transport Optimisation From an Energy and Environmental Perspective","authors":"André Vasconcelos,&nbsp;Margarida C. Coelho,&nbsp;Jorge M. Bandeira","doi":"10.1049/itr2.70035","DOIUrl":"https://doi.org/10.1049/itr2.70035","url":null,"abstract":"<p>Peri-urban and rural mobility presents unique challenges due to its reliance on fossil fuels and the lack of comprehensive studies on its externalities. Furthermore, the provision of efficient public transport in peri-urban areas is complicated due to fluctuating demand patterns. In this context, this paper explores the optimisation of a bimodal (bus line and a diesel railway line) public transport corridor in the peri-urban area of a European medium-sized city. A four-stage research methodology is employed: initial system characterisation, energy and environmental performance analysis, operational efficiency assessment, and the development and evaluation of alternative transport strategies. The study concludes that the variability in demand necessitates the implementation of innovative, complementary services that can adapt to changing passenger numbers while optimising existing resources. Findings of the case study indicate that aligning bus types with demand variability and integrating electric buses can lead to substantial reductions in CO₂ emissions. The use of minibuses during off-peak hours could achieve a 50% reduction in emissions, while the adoption of a bus rapid transit (BRT) system may result in a 90% decrease compared to conventional diesel trains. The research underlines the potential for innovative service models to utilise existing infrastructure more efficiently.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871563","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":"DCSTNet: A Dual-Channel Spatio-Temporal Information Fusion Network for Map-Free Vehicle Trajectory Prediction","authors":"Yuxuan He,&nbsp;Haibin Xie,&nbsp;Xinglong Zhang","doi":"10.1049/itr2.70030","DOIUrl":"https://doi.org/10.1049/itr2.70030","url":null,"abstract":"<p>The complex interaction between traffic participants brings safety problems for autonomous driving in mixed-traffic environment. Current state-of-the-art (SOTA) vehicle trajectory prediction models suffer significant performance degradation when high-definition (HD) map inputs are excluded, which may compromise the safety of decision-making and planning for autonomous driving systems in real-world traffic environments. To address this challenge, this paper proposes a novel dual-channel interactive modelling framework, termed the DCSTNet (dual-channel spatio-temporal information fusion network), specifically designed for vehicle trajectory prediction without relying on HD map information. Unlike previous trajectory prediction models that model temporal and spatial interactions interlacing or hierarchically, DCSTNet decoupling temporal and spatial interaction modules through a specially designed encoding network. This practice enables the model to more fully extract interaction features without increasing computational complexity when map information is not considered. To verify the validity of the dual-channel spatio-temporal information fusion framework, our study uses the publicly available Argoverse motion forecasting dataset. The comparison of results demonstrates that DCSTNet outperforms many SOTA approaches, including those that use map-based priors. To further validate that decoupling temporal and spatial interaction modelling enhances feature extraction capabilities, we conduct rigorous ablation studies and sensitivity analysis on the dataset to dissect architectural components of the DCST network. To explore the adaptability of the framework, we also develop a map-based variant of DCSTNet and compare its predictions with the map-free version in complex road environments.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852753","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}