Wei She

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%.

Multi-agent trajectory prediction relies heavily on information exchange, where incomplete state sharing, limited interaction frequency, and communication uncertainty readily induce collaborative instability. Current approaches primarily rely on heuristic interaction rules, failing to explicitly quantify the trade-off between communication overhead and prediction gains. This implicit assumption often leads to suboptimal information exchange and lacks evolutionary stability for communication strategies in dynamic traffic environments. Specifically, we propose the Stability-aware Communication via Weighted Potential Games (SC-WPG) framework. First, we leverage the continuous-time dynamics of Liquid Neural Networks (LNNs) to design a dynamic phase-coupling mechanism that aligns the temporal evolution of agent states, alleviating strategy asynchrony. Second, we establish a liquid state-sharing pool to exchange compact latent representations encoded from local observations, while promoting efficient implicit state fusion through spatio-temporal attention mechanisms. Finally, communication scheduling is formulated as a weighted potential game to explicitly quantify the trade-off between gains and costs. A stability-aware weight adaptation mechanism based on latent-state variation is introduced to regularize communication behavior under dynamic constraints. Experimental results on the NGSIM and HighD datasets show that SC-WPG achieves the lowest average root mean square error (RMSE) of 0.89 and 0.55, respectively. These results correspond to improvements of 10.1% and 6.8% over the strongest competing baselines under the same communication-constrained setting.

In autonomous driving and intelligent transportation systems, the degradation of image quality under low-light conditions severely impacts the reliability of subsequent object detection. Existing methods predominantly employ data-driven deep learning models for image enhancement, often lacking physical interpretability and struggling to maintain robustness in complex lighting-varying traffic scenarios. To address this, this paper proposes a Physically Guided Transformer–CNN Hybrid Network (Physically Guided Transformer–CNN Hybrid Network, PGT-Net) for end-to-end joint optimization of low-light enhancement and object detection. PGT-Net innovatively integrates the atmospheric scattering physical model with deep learning architecture: first, a learnable physical guidance branch estimates the scene’s atmospheric illumination map and transmittance map, providing explicit physical priors for the network; second, a dual-branch enhancement backbone is designed, where the local CNN branch (based on an improved UNet) restores fine textures, while the Global Transformer Branch (based on Swin Transformer) models long-range dependencies to correct global uneven illumination, with features adaptively combined via a Physical Fusion Module to ensure enhancement results align with physical laws while retaining rich visual features; finally, the enhanced images are directly fed into a lightweight detection head (e.g., YOLOv7) for joint training and optimization. Comprehensive experiments on public datasets (ExDark, BDD100K-night, etc.) demonstrate that PGT-Net significantly outperforms mainstream methods (e.g., RetinexNet, KinD, Zero-DCE) in both low-light image enhancement quality (PSNR/SSIM) and object detection accuracy (mAP), while maintaining high inference efficiency. This research offers an interpretable, high-performance solution for visual perception tasks under adverse lighting conditions, holding strong theoretical significance and practical value.