Transit-oriented development (TOD) offers a planning basis for compact, mixed-use, and vibrant station areas. However, in many rapidly developing Chinese cities, metro-network expansion has not been matched by coordinated station-area commercial renewal, resulting in spatially uneven street-level activity despite improved transit accessibility. Taking the North Ring Road Station area in Changchun, China, as a case, this study examines the associations between TOD-related spatial conditions and observed human activity intensity as a proxy for commercial spatial vitality, and how these spatial associations can inform targeted regeneration strategies. Within an 800 m TOD analytical boundary, commercial point of interest (POI) data, Baidu Heatmap-derived human activity data, building footprints, road-network data, land-use information, and field observations were integrated. Average nearest-neighbor (ANN) analysis, kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR) were applied within a 5D TOD framework. The results reveal a dual-core, corridor-oriented, and spatially uneven commercial structure. Commercial facility agglomeration and functional diversity are positively associated with observed activity intensity, whereas transit distance, road-network configuration, building morphology, and proximity to commercial anchors exhibit spatially heterogeneous associations. These results suggest that transit proximity and commercial concentration alone do not necessarily correspond to spatially continuous activity when pedestrian permeability, interface openness, and functional mixing are limited. The study translates these associations into evidence-informed transport-development responses that coordinate metro-entrance access, pedestrian transfer and first/last-mile connections, commercial functions, and surrounding land uses, thereby providing a micro-scale diagnostic approach to station–street–commercial integration.

Low-speed urban micro-mobility can support sustainable last-mile transportation in dense urban environments, particularly where compact vehicle dimensions, cargo capability, and flexible energy access are required. This study presents the design and experimental evaluation of a Hybrid Solar-Electric Cart (HSEC) as a photovoltaic (PV)-assisted urban micro-mobility platform, with micro-, small-, and medium-sized enterprise (MSME) logistics considered as a principal application scenario. A Design-Build-Test-Evaluate (DBTE) engineering framework was applied to prototype development, system integration, and field validation. The HSEC integrates a reinforced steel chassis, a 2 kW 72 V brushless direct current (BLDC) mid-drive motor, dual 72 V 22 Ah LiFePO$_4$ batteries, a 300 W PV array, battery management system (BMS)-based battery-state monitoring, maximum power point tracking (MPPT)-regulated charging, and automatic battery switching. These functions form a coordinated energy-management architecture that supports source monitoring and propulsion continuity during low-speed operation. Under the reported field conditions, the prototype achieved an average operating range of approximately 40 km per battery, remained functional under combined rider-and-cargo loads of up to 180 kg, completed low-speed turning maneuvers with an approximate radius of 5.6 m, and charged one battery from 30% to 90% state of charge (SOC) in approximately 3.6 h under tropical solar conditions. The findings demonstrate the engineering feasibility of integrating PV charging, dual-battery energy management, and compact cargo mobility within a low-speed transportation platform. The proposed architecture may support last-mile logistics, neighborhood transport, designated vendor routes, campuses, tourism areas, and other low-speed mixed-use environments, subject to further durability, safety, ergonomic, environmental, and techno-economic validation.

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.

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.