IET Intelligent Transport Systems最新文献

Optimizing regenerative braking in dual-motor electric vehicles (EVs) is critical for extending driving range but presents a complex high-speed control problem. This study proposes a novel, real-time control strategy by training a hybrid deep neural network–decision tree (DNN–DT) model on an optimal dataset generated by offline dynamic programming (DP) considering seven key characteristic variables: road grade, friction coefficient, vehicle load distribution, velocity, braking rate, battery state of charge, and total braking torque. This hybrid methodology combines the high-accuracy, non-linear mapping of DNNs with the interpretability of DTs. The model was validated in a 14-DOF Simulink environment against two reference strategies (fixed-ratio and baseline) across four different scenarios (UDDS, NYCC, WLTP), including interpolation and extrapolation tests. Key experimental results show the hybrid model accurately tracks the DP-optimal torques (average $��R^2�\approx �0.97�$) and consistently outperforms the reference methods, achieving a 1.26% to 5.06% reduction in net SOC loss. This energy saving translates to a practical gain of 90–383 meters per cycle. Crucially, the model's average inference time of 2.3 ms confirms its computational efficiency and feasibility for real-time implementation on a standard vehicle control unit (VCU).

Accurate traffic flow prediction is essential for the effective management of Intelligent Transportation Systems (ITS). However, traditional methods based on static graph structures often fail to address the complex and nonlinear spatiotemporal dependencies in evolving traffic conditions. To address this challenge, we propose a Dynamic Graph Convolutional Recurrent Network with Temporal Self-Attention (DGCRN-TSA), which integrates a temporal attention mechanism to jointly capture dynamic spatial topologies and long-range temporal patterns. The model incorporates a graph generation module that adaptively learns time-varying adjacency matrices from traffic signals and introduces a trend-aware attention module enhanced by residual-guided decomposition for distinguishing between normal and anomalous traffic behaviours. Experiments on real traffic datasets confirm that DGCRN-TSA achieves superior performance in both short- and medium-to-long-term forecasts. Notably, it reduces MAE by 19.4% on PeMS04 and improves MAPE by 12.2% on PeMS08. The model also ensures high prediction accuracy with strong computational efficiency and an inference speed comparable to AGCRN. DGCRN-TSA offers an efficient and reliable solution for dynamic spatiotemporal modelling and large-scale real-time traffic prediction.

The effective management of connected and autonomous electric vehicle (CAEV) fleets is critical for realising their potential to create safer and more efficient urban transportation systems. As CAEVs increasingly share the road with conventional vehicles, a primary challenge is to optimise their operations within this mixed-traffic reality. This study tackles the joint problem of route selection and charging scheduling for multiple CAEVs by developing a multi-objective optimisation model. The framework is designed to minimise a holistic cost function comprising energy consumption, travel time, charging service fees and penalties for violating user-specified time windows. Critically, the model incorporates real-world complexities, including time-of-use electricity pricing, partial charging strategy and the stochastic queuing delays introduced by non-connected vehicles at charging stations. We adapt the non-dominated sorting genetic algorithm III (NSGA-III) to efficiently solve this complex optimisation problem. The proposed model is demonstrated through a case study using an actual road network from Beijing, China. The results indicate that the proposed model can reduce charging service fees by up to 96% and travel time by 33% when compared to conventional scheduling methods. These findings can offer significant insights for policymakers and platform operators aiming to formulate effective CAEV travel and charging policies in heterogeneous traffic environments.

Image restoration is essential for vision-based navigation, surveillance, and remote sensing, yet real-world images are often degraded by haze, fog, rain, snow, clouds, and underwater turbidity. Existing methods are typically tailored to single degradations or narrow domains, limiting their generalisation in complex environments with overlapping effects. We introduce UMD-NOIR, a unified multiscale diffusion model for navigation-orientated image restoration that integrates a Transformer-enhanced UNet into a denoising diffusion probabilistic model. The architecture incorporates multiscale channel and spatial attention together with a gated deconvolutional feed-forward network, enabling robust feature aggregation, spatially aware enhancement, detail recovery, and structural consistency. A hybrid loss combining Huber and perceptual terms further improves sharpness, colour accuracy, and perceptual fidelity. Quantitative evaluations against 13 state-of-the-art diffusion and transformer-based models show that UMD-NOIR achieves an average peak signal-to-noise ratio (PSNR) of 28.07 dB, structural similarity index measure (SSIM) of 0.888, and mean absolute error (MAE) of 0.032 across ground, aerial, and marine degradations. Generalisation is validated on five unseen datasets (DAWN, RESIDE, RICE, LSUI, and CDD-11), demonstrating adaptability to real-world and composite degradations. No-reference assessments further yield NIQE = 3.91 and PIQE = 20.85 on haze images, confirming perceptual realism. Downstream evaluations with YOLOv11 highlight improvements in classification, detection, and segmentation, while ablation studies verify the importance of multiscale processing, attention mechanisms, and hybrid loss in achieving consistent gains.

High-speed railway operations rely on various crew members. Among them, onboard mechanics are responsible for monitoring train systems and handling in-transit faults, bearing long working hours and critical safety responsibilities. However, they have received limited attention in existing crew rostering research, particularly those residing outside the base depot city. Additionally, the crew rostering plan often requires adjustments during execution, due to foreseeable disruptions such as leave requests, training courses, and additional tasks. Yet, these practical issues are often ignored, with adjustments mainly made manually and without a systematic correction approach. To address these gaps, this paper first proposes a hybrid service pattern and a mathematical model to design more reasonable crew routes and provide more convenient task arrangements for onboard mechanics. Following this, a rolling optimisation model is introduced to address foreseeable disruptions during the execution of the rostering plan, aiming to minimise plan modifications while maintaining balanced workloads. The relationship between the two models is sequential, with the crew route information obtained from the first model serving as partial input for the second model. A real-world case study based on data from the Qingdao EMU Depot in China is conducted to validate the effectiveness of the proposed models, with Gurobi used for solving. The computational results show that the crew routes are well assessed and balanced in terms of both operational costs and work convenience. Furthermore, the rolling optimisation model achieves a significantly lower modification-to-disruption ratio than the manual approach, ensuring greater robustness against disruptions. It further improves workload balance, reduces commuting costs, and prevents overwork, offering a practical and efficient solution for real-world planning.

Merging behaviours in work zones pose significant traffic management challenges due to lane closures that create bottlenecks and increase accident risk. In mixed traffic environments involving emergency vehicles (EVs), connected automated vehicles (CAVs), and human-driven vehicles, addressing these challenges becomes more complex. EVs, in particular, require priority movement to minimize response times, but their movement is often hindered by traditional traffic management strategies. While previous merging strategies have shown promising results in managing traffic flow, they fail to account for the unique challenges posed by different vehicle types in work zone environments. In this paper, we propose a new reward weight adjustment method for multi-agent proximal policy optimization based CAVs in work zone for emergency vehicles (MAPPO-WEV). The proposed method improves EV's response times without compromising travel time of other vehicles. MAPPO-WEV introduces a dynamic reward weighting mechanism that adjusts the importance weight of speed, headway, and merging behaviour based on the type and number of vehicles. This approach allows EVs to travel more freely while maintaining safety in mixed traffic conditions. The simulation results of MAPPO-WEV show significant improvements in both travel times and waiting times of EVs by 25% and 33% respectively compared to the Baseline method.

The safety decisions of autonomous driving systems rely on the accurate understanding of 3D scenes, and the existing 3D occupancy prediction (OCC) models are difficult to meet the requirements of in-vehicle deployment due to their high computational complexity and a large number of parameters. Traditional methods (e.g., OccWorld, FlashOcc) rely on full-precision floating-point operations and dense 3D convolution, resulting in hundreds of millions of model parameters. In this paper, we propose a lightweight two-branch binarization network, TBBOcc, to break through the bottleneck of ‘efficiency-accuracy’ trade-off through multi-technology co-optimization. First, we design two-branch binarized feature extraction, using channel compression and hyperbolic tangent relaxation activation function to alleviate the problem of vanishing binarized gradient, which reduces the computation amount while retaining the key geometrical information; second, we improve the EfficientViM module by integrating state space modeling and a two-dimensional normalization strategy, which enhances the ability of global temporal feature modeling; and lastly, we introduce a dynamic temporal fusion mechanism, combining binocular depth estimation with deformable BEV pooling to capture the spatio-temporal evolution laws. Experiments show that TBBOcc achieves 39.1% mean intersection over union (mIoU) on the Occ3D-nuScenes validation set with 32.8 M parameter counts and 164.8 G FLOPs, which reduces the amount of parameters by 26.6%, computation by 33.7%, and improves the accuracy by 3.3% compared with the baseline model FlashOcc. Especially, it performs well in dynamic obstacles (e.g., pedestrians, traffic cones) and complex scenes. In this paper, binarization computation is introduced into the 3D OCC task for the first time, which provides an efficient and reliable technical path for real-time environment sensing for autonomous driving.

To enhance the robustness and interpretability of road-accident prediction under sparse, noisy, and dynamically evolving conditions, this study proposes a Multimodal Grey-Markov and Adversarial Meta-Learning (MGMC-AML) framework. The model integrates grey relational analysis for adaptive modality weighting, dynamic Markov state modelling for temporal-spatial transition learning, and adversarial meta-optimisation for rapid recovery from perturbations. A reinforcement-enhanced dynamic partitioning mechanism is introduced to maintain structural consistency of road-network topology during fluctuating traffic states. Extensive experiments on multi-source traffic and weather datasets demonstrate that the proposed framework achieves superior predictive performance, with a Mean Absolute Error (MAE) of 2.34 incidents per day, a Segmentation Consistency Index (SCI) of 0.85, and a Modular Q value of 0.78, while maintaining an attack-recovery time of 162 s. These results surpass state-of-the-art baselines, including LSTM, GNN, and Transformer models, confirming both its precision and resilience under adversarial or uncertain sensing conditions. The proposed approach offers a unified and reproducible framework that bridges uncertainty modelling, adaptive fusion, and robust optimisation, providing a theoretical and practical foundation for resilient traffic-risk forecasting in intelligent-transportation systems.

Given that constant-parameter model predictive control (MPC)-based automatic emergency braking (AEB) systems are unable to simultaneously balance comfort, safety, and computational efficiency, this paper proposes an adaptive optimization strategy for MPC that adjusts weights and sampling times to address this issue. First, a three-level warning strategy based on a safety distance model is introduced. This strategy assesses the risk level during vehicle operation using an emergency coefficient and adaptively adjusts the sampling time. By analysing the correlation between relative distance, speed, and injury risk, weight adjustment rules based on injury risk are determined. Second, a fuzzy regulator is developed with the emergency coefficient, injury risk, and relative distance as inputs is developed to enable dynamic adjustment of weights and sampling time in response to operational conditions. Finally, an AEB control strategy is designed based on hierarchical control principles: the upper layer uses MPC to achieve multi-objective optimization, while the lower layer employs PID correction to track the desired acceleration. In the test scenarios, joint simulation experiments were conducted using CarSim and MATLAB/Simulink, and the results under four scenarios and operating conditions were analysed and compared. The results show that the proposed control strategy enhances comfort while ensuring AEB safety control, reducing the average braking distance deviation by 11.80% and the average acceleration deviation by 48.91%. These improvements are significant for enhancing AEB performance without hardware modifications.

Traditional methods of vehicle trajectory reconstruction heavily rely on the data of cross-sectional traffic detectors, but the effectiveness of existing methods is limited by insufficient information of the cross-sectional data. In response to this, this study proposes a novel trajectory reconstruction method based on long-range traffic detectors. It builds upon Newell's car-following model and its derived inverse following model. By taking driver heterogeneity into account through individualised calibration of the key parameter, namely the spatial shift, which is optimised by the whale optimisation algorithm, the accuracy of trajectory reconstruction is enhanced. Furthermore, to connect trajectories of the same vehicle in the same area that are reconstructed by adjacent long-range detectors, a particle filter-based trajectory fusion method is developed. It can fuse overlapped reconstructed trajectories and smoothly connect sectional trajectories into a complete and seamlessly connected one. The performance of the trajectory reconstruction method is evaluated on the NGSIM I-80 dataset, while the trajectory fusion method was tested on both the I-80 and the TRJD TS datasets. Results show that the reconstruction method generates complete vehicle trajectories across various traffic flow conditions, achieving an average of 28.65% reduction in mean absolute error compared to methods that do not account for driver heterogeneity. The mean absolute error of the fused trajectories was reduced by 49.23% and 59.69% on average for two datasets, respectively, compared to reconstructed trajectories using a single detector. The trajectory reconstruction accuracy of the proposed method also outperforms that of a deep convolutional neural network and an improved adaptive smoothing method.