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Volume 5, Issue 3, 2026

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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.

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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.

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Intelligent transportation systems increasingly rely on connected and autonomous platforms operating across road, rail, low-altitude, and marine environments. These systems must accommodate nonlinear and time-varying dynamics, environmental uncertainty, communication limitations, safety requirements, and tightly coupled operational constraints. Model predictive control (MPC) is well suited to such conditions because it combines future-state prediction, constrained optimization, and closed-loop correction within a receding-horizon framework. This review examines the theoretical foundations, major formulations, and transportation applications of MPC across four domains: ground vehicles and traffic networks, railway systems, low-altitude unmanned aerial transportation, and marine autonomous systems. The literature was organized according to transportation mode and application, with attention given to prediction models, control objectives, operational constraints, uncertainty treatment, computational requirements, and validation methods. The review showed that ground transportation research placed greater emphasis on vehicle motion control, connected-vehicle coordination, traffic signal optimization, and network regulation. Railway applications focused mainly on train regulation, virtual coupling (VC), scheduling, energy-efficient operation, and maglev control. Low-altitude studies addressed trajectory tracking, obstacle avoidance, multi-unmanned aerial vehicle (UAV) coordination, and learning-based prediction, whereas marine studies concentrated on underactuated motion, environmental disturbances, collision avoidance, navigation rules, and surface–underwater cooperation. Across these domains, model uncertainty, online computational burden, conflicting control objectives, coupled safety constraints, and limited real-world validation remained the principal obstacles to wider deployment. The findings indicate that MPC architectures must be adapted to the dynamics, operational environment, and communication conditions of each transportation mode. This review clarifies the common and domain-specific requirements of MPC in multimodal intelligent transportation and identifies the technical issues that require further theoretical and experimental study.
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