Digital railway transport systems increasingly integrate online ticketing, real-time passenger information, digital payment, onboard connectivity, and customer support into the passenger journey. Their effectiveness depends not only on the availability of these functions but also on how passengers experience and evaluate their interactions with them. This study investigates the relationship between digital experience and railway passenger satisfaction (RPS) and examines the parallel mediating roles of perceived value and digital trust in the Vietnamese railway context. Survey data were collected from 348 passengers who had recently used at least one digital railway service and were analysed using partial least squares structural equation modelling. Digital experience was positively associated with passenger satisfaction ($\beta$ = 0.452, $p <$ 0.001) and accounted for significant variation in perceived value and digital trust. Both mediating pathways were statistically significant, although the indirect effect through perceived value ($\beta$ = 0.175, $p <$ 0.001) was stronger than that through digital trust ($\beta$ = 0.113, $p$ = 0.001). Together, digital experience, perceived value, and digital trust explained 62.2% of the variance in RPS. These findings indicate that the passenger-side performance of digital railway transport systems rests on the quality of integrated interactions across digital touchpoints, particularly their convenience, reliability, security, and service value. The study provides a passenger-centred framework for evaluating the implementation of digital railway services and identifies the user-related factors that railway operators should consider when planning and prioritising digital system improvements.

Railway timetable planning plays a central role in the coordination and operational performance of transportation systems. Efficient timetable development remains essential for balancing infrastructure constraints, service quality, operational efficiency, and economic objectives in railway operations. The interaction among these dimensions makes timetable planning a complex decision problem for infrastructure managers and transport operators. This study aims to evaluate the relative importance of the principal criteria influencing railway timetable planning and to provide quantitative support for transportation system decision-making. A structured evaluation framework was developed using the fuzzy PIvot Pairwise RElative Criteria Importance Assessment (fuzzy PIPRECIA) method. Forty decision-makers with professional experience in railway operation, infrastructure management, engineering practice, and academia participated in the assessment process. Five main criteria were examined: railway line capacity, railway station capacity, number of passed trains, quality of train operations, and revenues of the planned timetable. The results showed that revenues of the planned timetable received the highest importance weight, followed by quality of train operations, number of passed trains, railway line capacity, and railway station capacity. The findings further showed that operational and economic dimensions exerted greater influence on timetable planning decisions than infrastructure-capacity factors. The results indicate that railway timetable planning should be approached as a system-level coordination problem rather than a capacity allocation exercise alone. This study provides a structured decision-support perspective for evaluating competing planning priorities and offers a practical basis for improving timetable development and operational performance in railway transportation systems.

The increasing complexity of modern urban traffic networks demands intelligent control strategies that can anticipate and adapt to dynamic traffic conditions. Model Predictive Control (MPC) is a framework that optimizes vehicle control by predicting future states and respecting real-time constraints, such as traffic signals at intersections. However, the computational complexity of MPC increases significantly with the number of decision variables and constraints, which is directly proportional to the length of the prediction horizon, creating a critical trade-off between control performance and computational efficiency. To address this challenge, this paper proposes an adaptive-horizon optimal driving (AHOD) bi-level optimization framework that incorporates a novel time-step discretization for real-time trajectory optimization and integrates it into a full traffic signal cycle. Unlike conventional MPC, which employs uniform time discretization leading to exponential growth in decision variables with horizon length, the proposed AHOD framework assigns finer time steps near signal phase transitions and coarser steps in the distant horizon, maintaining a fixed number of optimization nodes regardless of cycle length. The proposed framework comprises two controllers: the upper and lower controllers. The Upper controller employs finer resolution at critical times of signal change and coarser resolution in distant horizons, thereby reducing computational cost while maintaining prediction accuracy. The lower controller applies a practical MPC scheme to generate realtime control actions that are consistent with the long-term constraints of the upper controller. Simulation results demonstrate that the proposed framework achieves up to 17.6% fuel savings compared to traditional human driving and reduces computation time by approximately 61% compared to long-horizon MPC, while maintaining comparable control performance. The proposed framework enables real-time, cycle-aware predictive control for connected and automated vehicles (CAVs), and establishes a practical basis for embedding long-horizon prediction within an MPC-based trajectory-planning framework.

This paper presents a genetic algorithm (GA) tuned Mamdani type fuzzy logic control (FLC) framework for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) using a nonlinear rigid body model. The proposed architecture adopts a cascaded structure in which an outer loop position controller generates attitude and thrust references $(\phi_{\mathrm{ref}},\theta_{\mathrm{ref}},T_{\mathrm{ref}})$, while an inner loop attitude controller generates body torques $(\tau_\phi,\tau_\theta,\tau_\psi)$. Both loops employ a shared Mamdani fuzzy inference system with normalized inputs (tracking error and error-rate) and a normalized control output. The GA automatically tunes scaling gains $(K_e,K_d,K_u)$ across all axes to minimize a robust objective that averages tracking error, control effort, and constraint violations over multiple scenarios with mass uncertainty and wind disturbances. Simulation results on a three dimensional figure eight trajectory indicate that GA tuning can reduce position and attitude errors while respecting actuator saturation and tilt safety limits, demonstrating a practical route to performance enhancement without requiring a high fidelity aerodynamic model. The methodology leverages the interpretability of fuzzy rules and the global search capabilities of evolutionary optimization within a UAV modeling framework consistent with established quadrotor dynamics literature.

Rolling bearings are critical components of marine shafting power transmission systems, and accurate prediction of their vibration signal trends is essential for predictive maintenance. To address the limited adaptability of conventional time-series forecasting models under varying operating conditions and their insufficient ability to capture strong noise and abrupt changes, this study proposes a vibration signal prediction method that integrates particle swarm optimization (PSO) with an improved Informer model. PSO is used to adaptively optimize key Informer hyperparameters for different operating conditions, while a rolling time-window mechanism is introduced to enhance the capture of abrupt signal variations. In addition, a mixture of sparse attention (MoSA) encoder with a collaborative dense-head/sparse-head structure is designed to balance global temporal dependency modeling and local fault feature extraction. Experimental results on the Case Western Reserve University (CWRU) bearing fault dataset show that the proposed model outperforms Long Short-Term Memory (LSTM), Transformer, Informer, iTransformer, and Flowformer in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Erro (RMSE). The model achieves an MSE of 0.2015, which is 25.5% lower than that of the second-best iTransformer model. It also demonstrates robust performance under four different bearing operating states, confirming its adaptability to complex operating conditions. The proposed method provides a promising technical route for the predictive maintenance of rolling bearings in marine shafting systems.

Efficient coordination of heterogeneous mobile resources is essential for delivering large-scale urban services, such as sanitation, infrastructure inspection, or last-mile delivery. This study addresses the problem of scheduling aerial and ground service vehicles—unmanned aerial vehicles (UAVs) and mobile ground crews—to cover spatially distributed demand points under operational constraints. We formulate the task as a multi‑objective optimization problem that simultaneously maximizes service coverage, minimizes total completion time, and optimizes resource utilization while respecting safety, capacity, and time‑window restrictions. A hierarchical solution framework is proposed: global task allocation first assigns demand zones to vehicle types according to their capabilities, and local path planning then generates efficient routes for each agent. A dynamic re‑optimization mechanism adjusts schedules in real time when disturbances occur, such as resource depletion or environmental changes. The method is evaluated on scenarios of increasing scale (51, 113, and 212 demand points) that emulate urban public spaces. Results from ten repeated experiments show that the cooperative strategy achieves coverage rates (CRs) above 97% across all scales, reduces total operation time (TOT) by up to 33% compared with single‑mode operations, and improves resource efficiency by 21.10% and 47.40% Statistical analysis confirms the robustness of the improvements. The framework offers a scalable, resource‑aware solution for coordinating heterogeneous service fleets, with direct applicability to intelligent transportation systems, particularly in demand‑responsive urban services and multimodal fleet management.