IET Intelligent Transport Systems最新文献

Off-road path planning and navigation often struggle with complex challenges, such as diverse surface conditions that demand adaptability, stability-sensitive vehicle dynamics on low-adhesion terrain, and the persistent trade-off between real-time performance and path quality. To address these challenges, an improved rapidly-exploring random tree (IRRT) algorithm is developed to adjust the dynamic exploration domain considering the vehicle's design speed and local terrain features, which can affect vehicle's operational stability, thereby increasing path feasibility and environmental adaptability. Furthermore, a nonlinear model predictive controller (NMPC) is deployed in the lower layer of the proposed RRT path planning framework, smoothing the generated path and enhancing ride comfort through terrain-aware adjustments. Both a 100 × 100 meter simulated environment and a real-world 1:10 scale test site, featuring distinct terrain types, i.e., hard roads, natural terrain, and low hills, with obstacles. The results show that the proposed two-layer path planning framework, improved RRT algorithm integrating with NMPC, reduces path length by 6.9% and total turning angle by 12.3% compared to RRT, while maintaining a maximum curvature of 0.134 m⁻¹ (well within the safety limit of 0.2 m⁻¹) and improving ride comfort by 80.4%. On the other hand, although the computation time increases by 272.2%, the resulting gains in path quality and stability justify the trade-off. The proposed method demonstrates a viable solution for off-road vehicle navigation across diverse terrains, effectively balancing path feasibility, ride smoothness, and computational efficiency.

Current approaches to intercity dynamic ride-sharing mainly adopt single-stage scheduling, where new orders are periodically batched and processed. Although effective, this strategy often causes heavy computation and delayed passenger feedback, limiting real-time applicability. To address these issues, we propose a novel two-stage information feedback framework combining coarse and fine scheduling. In the coarse stage, online scheduling (nearest insertion) promptly matches new orders with departed vehicles, while offline scheduling (best insertion) processes non-departed vehicles, thus providing passengers with staged and timely feedback. In the fine stage, assignments are further optimised through large neighbourhood search, with the triggering decision modelled as a Markov decision process and learned by deep Q-learning. This design reduces redundant computation while dynamically balancing feedback timeliness and scheduling efficiency. Unlike traditional methods, our framework is novel in integrating staged passenger feedback, hybrid heuristic optimisation and reinforcement learning-based control. Experiments on two real-world intercity carpooling datasets show that the method significantly reduces runtime and feedback delays while maintaining strong scheduling performance, demonstrating its potential as a practical solution for large-scale dynamic ride-sharing platforms.

Urban traffic prediction is of great significance for traffic management and optimisation. Although research on predicting indicators such as traffic flow and speed is relatively sufficient, research on forecasting congestion ratios in different urban regions remains inadequate. Based on traffic big data, this paper proposes a fusion regional congestion ratio prediction model integrating eXtreme gradient boosting tree (XGBoost) and long short-term memory (LSTM), which integrates multi-source features, including temporal, meteorological, and spatial factors. First, the XGBoost algorithm is used to model the historical congestion ratios and related features of each region, obtaining preliminary prediction results and extracting regional residual sequences; subsequently, the residual sequences are input into the LSTM network for error correction. Finally, the prediction results of the two stages are fused to obtain more refined regional congestion ratio predictions. Experimental results show that during peak hours on weekdays, taking Region 49 as an example, the MAE of the fusion model is 0.062, the mean absolute percentage error is below 30%, and the comprehensive prediction accuracy reaches up to 72%; under complex weather conditions, for the same region, the RMSE values of the fusion model are 0.048, 0.058, and 0.043, respectively, which are 37%–63% lower than those of the XGBoost model used alone. Feature ablation experiments further verify the key role of temporal, meteorological, and spatial features in improving prediction performance, among which spatial features contribute the most to performance optimisation. This study improves the research framework in the field of urban traffic prediction and provides a theoretical basis and methodological support for regional traffic management practices.

This paper proposes a novel Signal-Vehicle Cooperative Control framework (SVCC-HPPO) based on the improved Hierarchical Proximal Policy Optimisation (H-PPO) algorithm to jointly optimise traffic signal timing and Connected and Autonomous Vehicle (CAV) trajectories under mixed vehicular environments with both CAVs and Human-Driven Vehicles (HDVs). A hierarchical hybrid action space is designed to effectively constrain CAV acceleration and signal timing adjustments while explicitly accounting for car-following dynamics near intersections, enabling flexible exploration within physical limits. The hybrid actor-critic architecture facilitates simultaneous optimisation of discrete and continuous actions through parallel actors guided by a global critic, balancing optimization effectiveness with training stability. A multi-objective reward function simultaneously minimises vehicle delay and fuel consumption and maximises ride comfort. The core improvement involves a layered entropy regularisation strategy within the H-PPO algorithm, which separately manages discrete and continuous entropy to enhance exploration efficiency and stability across hybrid action dimensions. Real-world intersections evaluation results demonstrate that SVCC-HPPO significantly outperforms benchmark methods TRANSYT and DRL-based algorithms, achieving reductions of up to 46.3% in delay, 59.5% in queue length, and 52.9% in fuel consumption, alongside a 177.4% improvement in average speed. Performance gains are further enhanced with shorter optimisation intervals and higher CAV penetration rates.

Roadside sensing is an important part of intelligent traffic management systems (ITMSs) for collecting and processing information. In order to better assess and maintain the stability and safety of objects in traffic scenes, all types of basic information are required. This paper proposes a monocular vision-based object parameter measurement and geolocation method to address the problems of high cost and limited information dimension of traditional roadside sensors. Object detection and geometric transformation mapping are combined to achieve efficient estimation of key physical parameters with input of monocular images, and global navigation satellite system (GNSS) information is further incorporated to obtain geolocation of the target. In the method, after the key target is recognized by the neural network-based object detection algorithm, the pixel-level 2D image information is mapped to a series of 3D spaces based on the construction of a geometric model, which leads to further computation of various physical parameters, realizing multi-parameter estimation under one method. The method overcomes the dependence on fixed environments or known references and is highly applicable to non-cooperative scenes. The effectiveness of the method is shown via the experiments in multiple real scenes.

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.