IET Intelligent Transport Systems最新文献

High and medium technology exports play a crucial role in supporting economic growth, fostering international competition and potentially reducing carbon dioxide emissions through the adoption of advanced technologies. However, the environmental effects of such exports, particularly in the transportation sector, remain underexplored. This study addresses this gap by examining how transportation technologies, high and medium technology exports, trade freedom, and trade globalisation affect CO₂ emissions from transportation. The analysis covers the ten countries with the highest transportation-related emissions over the period 1995–2020, employing augmented mean group (AMG) and common correlated effects (CCE) estimators. The results reveal heterogeneous effects across countries. Transportation technologies are found to increase emissions in Japan but reduce them in South Korea, the United States and Mexico. High and medium technology exports raise transportation emissions in China, France, Germany, the USA and the overall panel. Trade globalisation increases emissions in France, whereas it reduces them in Germany. These findings suggest that advancing transportation technologies, aligning trade openness with environmental goals and shifting exports toward higher technology products can support the reduction of transportation-related carbon emissions. Such measures are vital for progress toward the Sustainable Development Goals.

The increasing penetration of electric vehicles (EVs) poses challenges to voltage stability and power quality in distribution networks, especially under three-phase unbalanced load conditions. This study aims to develop a practical and effective method for mitigating three-phase unbalance and providing reactive power compensation (RPC) in vehicle-to-grid (V2G) applications. The scope of the work focuses on residential distribution networks where V2G charging piles are deployed, considering both balanced and unbalanced operating scenarios. The main contributions are threefold: (1) a realistic V2G AC–DC control scheme based on conventional d–q control is adopted to ensure compatibility with existing charging hardware; (2) a novel three-phase four-wire inverter topology and control strategy is proposed to suppress neutral point voltage shift and absorb zero-sequence current under unbalanced conditions; and (3) an OPF-based RPC control method is integrated to regulate node voltage and improve voltage unbalance factor (VUF) without affecting user charging requirements. Simulation studies and a real residential case in demonstrate that the proposed approach can maintain node voltage within ±5% of nominal value, reduce VUF to below 2% and provide up to 2176 kVAr of reactive power support, confirming its practical feasibility and effectiveness.

Sustainable UAV adoption requires aligning the identification and priorities of user needs with the objective to mitigate flight noise. To link the two, we identify UAV user needs and estimate their baseline importance weights, then guide and re-estimate these weights through a video-based information intervention, enabling manufacturers to adopt the guided weights in low-noise product design while meeting user demand. This study has two objectives that can be jointly operationalised in product design: (1) to identify UAV user demands and estimate their baseline weights via a two-stage quality function deployment (QFD) and fuzzy best–worst method (F-BWM) and (2) to guide the relative weighting of these demands through a video-based information framework that encourages users to prioritise low-noise related attributes when purchasing UAVs and to estimate the post-guidance weights. The baseline analysis produced individual weights for six user demands and ranked ‘environmental and green design’ and ‘technical performance’ as the top two; although ‘environmental and green design’ was already highly weighted, the video intervention further increased its weight from 27.5% to 28.7%. The methodology provides guidance for manufacturers to optimise UAV design and reduce noise, promoting the sustainable development of the low-altitude economy and the environment.

Highway collisions are influenced by a variety of factors, including dynamic traffic conditions and road geometry. A comprehensive understanding of how these factors specifically affect crash risk is essential for enhancing traffic safety. While previous studies have examined the relationship between traffic conditions and collision risk, as well as the influence of road geometry, limited attention has been given to analyses that consider both dimensions simultaneously. The configuration of road sections plays a critical role in vehicle behaviour and, consequently, in collision risk. This study introduces a section-based crash risk analysis framework to investigate the interplay between traffic states and crash likelihood, with a particular focus on merging and diverging areas. Traffic states were classified using upstream and downstream detector speeds. Specifically, we analyse the impact of speed differences between upstream and downstream traffic, along with the influence of ramp flow on collision risk across various geometric configurations. Crash risk was quantified using crash occurrence (CR) and the potential crash occurrence rate (PCR). The relationships between traffic states and crash risk were modelled using polynomial and segmented regression. The results reveal that diverging sections exhibit the highest collision risk, especially under conditions of pronounced speed disparity, regardless of whether traffic is free-flowing or congested. Moreover, the findings indicate a sharp increase in crash risk when the ramp-to-mainline flow ratio exceeds a critical threshold. These insights underscore the necessity of targeted traffic management strategies and optimized road design to mitigate high-risk scenarios. They also emphasize the importance of future research that integrates both geometric and dynamic traffic characteristics in modelling collision risk.

Heavy-haul trains play a crucial role in long-distance bulk transportation, yet their enormous mass and kilometer-scale length lead to complex longitudinal interactions and high coupler forces, which threaten operational safety. Conventional mechanism-based models, while accurate, are computationally expensive and unsuitable for real-time prediction. To address this limitation, this study develops a data-driven prediction framework that combines physics-based modelling and deep learning. A detailed longitudinal dynamics model of a 20,000-ton train operating on the Shuohuang Railway is constructed, incorporating traction, electrical braking, and resistance characteristics to compute coupler forces under varying gradients and curvature conditions. Based on this model, a QP-based optimization algorithm and a high-fidelity simulation platform are used to generate multi-strategy operating datasets that balance energy efficiency, punctuality, and ride comfort. The resulting data are processed using normalization and sliding-window segmentation to form supervised learning samples. A multi-resolution dual-stream LSTM (MRDS-LSTM) and its attention-enhanced variant (MRDS-LSTM–Attn) are then proposed to capture both short-term fluctuations and long-term temporal trends. Compared with RNN, GRU, LSTM, Bi-LSTM, NLSTM, CNN-LSTM, CNN-NLSTM, CapNet-NLSTM, Transformer, and Informer baselines, the proposed model achieves the highest prediction accuracy with MRDS-LSTM-Attn achieves an MAPE of 2.57%, and $�R^2$ of 0.9888. The results demonstrate that the proposed framework effectively bridges physical modelling and data-driven prediction, achieving up to 706$�\times$ faster inference than traditional solvers. It provides a practical foundation for intelligent heavy-haul train operation, supporting real-time coupler force monitoring, predictive safety control, and future extensions to pneumatic braking and field data validation.

To enhance the service scope and quality of urban public transport systems, this study investigates the optimal design problem of feeder-bus networks related to urban rail transit considering time windows (FBNDP-TW). To ensure an acceptable passenger travel time, we differentially set the travel time window for each origin-destination (OD) pair based on the ideal travel time. Considering logical constraints, capacity constraints and time window constraints, we construct an FBNDP-TW optimisation model to minimise passengers’ generalised travel cost and bus operators’ operating cost. To solve this model, a genetic algorithm is developed with a diverse multi-neighbourhood crossover operation that includes ‘direct’, ‘forward’ and ‘adjacent’ rules. This crossover operation mechanism can efficiently make the feeder-bus network quickly meet time window constraints to guarantee its quality. Finally, the proposed model and algorithm are evaluated using a standard example network. The results confirm that they can effectively ensure the travel time of each OD. Although integrating time window constraints slightly raises network cost, it significantly reduces the maximum OD detour ratio and ensures the travel time of all ODs within the acceptable range.

To address the issues of false positives and missed detections of multi-scale cracks and small targets in complex environments, this paper proposes an enhanced YOLOv10 instance segmentation network named YOLO-RCS (YOLOv10s road crack segmentation), specifically designed for segmenting surface cracks. YOLO-RCS utilizes the DCNv4 module to enhance feature extraction in the backbone network, improving the accurate localization of surface crack segmentation. Additionally, we introduce a novel C3FB structure (an efficient fusion of the C3 module and FocalNextBlock structure) to replace the C2f module in YOLOv10's neck network, aiming to reduce the number of parameters while enhancing model accuracy. Finally, we improve the original loss function to the WIOU loss function, which increases the model's precision and mean average precision (mAP) for segmenting surface cracks. Experimental results show that our model achieves an mAP50 of 90.0% on the surface crack segmentation dataset Crackseg9k, a 5.0% improvement over the original algorithm, with a precision of 91.6%, demonstrating excellent segmentation performance. Compared to some mainstream object detection algorithms, our proposed method also exhibits certain advantages.

Computer-based systems (CBSs) are complex and critical, with risks to human lives and the environment. Ensuring their safety requires rigorous methods. Unlike traditional approaches that model system mission specifications in a single step before introducing safety requirements, this paper proposes a layered strategy for modelling both mission and safety requirements. This strategy ensures alignment between safety and mission requirements at each layer and formally proves their sufficiency with respect to system-level safety constraints (SLSCs), thereby achieving synchronised assurance of functionality and safety. Mission requirements are specified in Event-B, while System-Theoretic Process Analysis (STPA) derives safety requirements (SRs) to address system-level hazards. These SRs and SLSCs are integrated into the Event-B model to ensure consistency and verify compliance. By iteratively applying this pattern at each STAMP refinement step, a layered CBS is developed with safety as a core feature. Key contributions include stepwise STAMP refinement aligned with the system architecture hierarchy, coordinated development of Event-B models and STPA analysis using common STAMP models, and managing abstraction levels to ensure compliance between SRs and SLSCs while addressing formal verification complexity. A case study of a computer-based interlocking system demonstrates the approach's practical application.

Low-light road segmentation is a challenging dense prediction task, which is very important for the safety monitoring of intelligent sea ports at night and the autonomous vehicles of shipping logistics. Most current research focuses on scenes with sufficient light. At the same time, there are few datasets for low-light scenes, making research on low-light perception very difficult and seriously restricting the shipping logistics industry's ability to operate safely at night. Directly applying segmentation methods developed for well-lit scenes to low-light road segmentation is unsatisfactory. To solve this problem, we propose a new approach by building an image enhancement module and an edge detection module, and integrating them into existing well-lit segmentation models as a plugin to meet the road segmentation requirements for low-light scenes. Specifically, to compensate for the lack of low-light image detail, we design an image enhancement module that achieves end-to-end pixel-level image enhancement by connecting four image processing filters in series and using convolutional neural network to predict hyperparameters. Additionally, to address the problem that road edges become blurred and difficult to extract in low-light images, we design an edge detection module to maximize its ability to extract road edges by selecting differential pixel pairs using different strategies and efficient combinations. We conduct comprehensive experiments on our newly released dataset, LoRD, demonstrating that our method significantly outperforms previous state-of-the-art models with relatively few parameters and computational cost. Our method achieves new SOTA performance in terms of accuracy and computational efficiency, achieving 93.29$�\%$ at 78.44 FPS in DDRNet-23-slim. The source code and dataset will be publicly available.

This paper introduces a robust Fuzzy Sliding-Mode Controller (FSMC) optimised through a Genetic Algorithm (GA) for a nonlinear full-vehicle semi-active suspension system, explicitly accounting for real-world uncertainties. Specifically, the study considers three types of uncertainties, including variations in sprung mass, temperature variations, and vehicle obsolescence. The proposed control method has been combined with two double-input-single-output fuzzy controllers to enhance both ride comfort and road-holding performance. A seven-degree-of-freedom nonlinear full vehicle model, including a semi-active suspension system with a nonlinear spring and linear magnetorheological (MR) damper, is presented. The study aims to investigate the impact of these uncertainties on the semi-active suspension system. A key innovation stems from the GA-based tuning of FSMC parameters, which dynamically adapts the controller's behaviour for optimal performance under varying uncertain conditions. The findings indicate noteworthy enhancements, such as a roughly 7% increase in ride comfort for FSMC 2 compared to FSMC 1 and an even more substantial, up to 12% improvement in road-holding performance in State 1. Similarly, in State 2, FSMC 2 achieved a 10% improvement in ride comfort and up to a 12% enhancement in road-holding performance over FSMC 1.