IET Intelligent Transport Systems最新文献

Autonomous driving has emerged as a highly topical subject within the realm of intelligent transportation systems. Automated valet parking (AVP) represents one of the initial mass-production application scenarios. However, motion planning in AVP confronts a series of formidable challenges. These challenges include a constricted movement space, vehicles parked in violation of regulations, and vehicles that intrude suddenly. In response to these issues, this article devises a safety-critical, kinematically executable overtaking planning system for AVP through a contingency path-speed iterative algorithm. A path-speed iterative optimisation framework is adopted, taking into full account both the curvature constraint and the contour constraint. The prediction probability of dynamic obstacles is incorporated into the quadratic optimisation problem, presented in the form of either soft or hard constraints. Furthermore, a contingency path-speed iterative planner is formulated to address the multi-modal predictions and the interframe probability transfer that occur during the overtaking process in parking lots. Numerical simulations (conducted on the Carla simulator with a 10 Hz planning cycle) across four complex AVP scenarios demonstrate that the proposed algorithm outperforms the baseline Baidu Apollo EM Planner. On-road experiments (deployed on a mass-produced MCU) further validate that the algorithm maintains real-time performance (average computation time < 10 ms) and reduces speed oscillation by over 50% compared to the baseline, while ensuring kinematically executable trajectories (max steering wheel angle limited to 389°). These results confirm the proposed algorithm significantly enhances overtaking safety, executability, and efficiency for AVP.

The adoption of positioning, tracking and communication technologies in modern ports enables real-time monitoring of vessel arrivals, container movements and equipment status, while automated technologies help ensure operations adhere more closely to schedules. These capabilities allow ports to implement more intelligent and dynamic planning and scheduling strategies. Building on this technological foundation, this paper investigates a comprehensive operation optimization approach that integrates the berth allocation (BAP) and container transshipment problem at a port terminal within a sea-rail intermodal transportation system. The study focuses on berth and quay crane allocation on the quayside, as well as container storage and train operation scheduling on the landside, with components interconnected through the flow of import intermodal containers. A mathematical programming model is developed and a variable neighbourhood search algorithm is proposed, with its performance compared against GUROBI and other heuristic algorithms. Numerical experiments are conducted to demonstrate the effectiveness of the proposed heuristic approach. Furthermore, the impacts of quayside equipment deployment and rail yard operational capacity are analysed to provide managerial insights for improving container terminal operations.

Traffic congestion significantly affects speed, and thus energy consumption of heavy goods vehicles (HGVs). One of the ways of correlating traffic state with vehicle speed is fundamental diagrams (FDs). This study develops a methodology to collect national-level traffic data for England, integrate it with vehicle data, and use the data to construct FDs by type of road in England. Traffic counts and time-averaged traffic speed are obtained from the National Highways database and Road Traffic dataset, and space-averaged traffic speed data is obtained from HERE Maps. Missing entries are added using the temporal pattern of traffic flow, and outliers in the count data are filtered using spline-regression and unsupervised k-means clustering. Traffic data is classified by road types using information from HERE Maps. FDs are constructed for each type of road and validated using a separate test dataset from the National Highways database. The correlation between macroscopic traffic flow data and microscopic vehicle data is verified by validating the FDs with HGV speed data collected from on-board telematics systems. The results can be used to predict vehicle speed directly from traffic density using universal HGV FDs for England, that is useful for estimating energy consumption.

Ship berthing is a critical and high-risk phase of navigation that requires highly precise path planning in environments with numerous obstacles. This paper presents a two-stage hybrid path planning approach designed to improve both safety and manoeuvrability during berthing operations. The first stage involves constructing an accurate environmental model based on berth characteristics. In the second stage, an enhanced A* algorithm is introduced with a directional consistency penalty to generate globally feasible paths with improved continuity. To further enhance local obstacle avoidan ce and edge-following capabilities, the artificial potential field method is applied. The resulting path is coupled with a ship dynamics model and a dynamic look-ahead strategy combined with PID control is employed to enable closed-loop heading and speed tracking. Simulation results show that the proposed method significantly enhances path smoothness and obstacle clearance. Specifically, the average heading change is reduced to 2.51° and the minimum obstacle distance increases from 15.12 to 39.04 m. This approach offers a practical and effective solution for autonomous berthing in constrained port environments.

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.