Existing studies have mainly focused on vehicle suspension control and coupled analysis of normal conducting maglev systems, while research on parameter optimization and sensitivity analysis of guideway girders for superconducting electrodynamic suspension (EDS) maglev systems on bridges remains limited. In this study, a coupled mechanical simulation model of the maglev vehicle, guideway, and bridge is developed. Considering vertical train loads, temperature rise of trackside coils, and high-speed operating conditions, the displacement, stress, dynamic amplification factor, vehicle vibration response, and levitation gap variation of the guideway girder are analyzed. The results show that increasing the girder height can improve the first-order vertical natural frequency of the structure and reduce the dynamic amplification effect. A girder height of 3.0–3.2 m provides relatively stable dynamic responses at 600 km/h. Under the structural form and calculation conditions considered in this study, a span of 24 m shows favorable dynamic response and engineering applicability. The concrete elastic modulus of the guideway girder is a key parameter affecting the system dynamic response. The levitation gap variation is sensitive to car body mass, and the air spring stiffness should be optimized by balancing vibration reduction performance and gap stability. The results can provide a reference for structural design and parameter optimization of guideway girders for superconducting EDS maglev systems on bridges.
Ride-hailing services, characterized by convenience, flexibility, and on-demand availability, have substantially reshaped urban mobility patterns, posing uncertain impacts on subway ridership. To clarify the competitive and cooperative dynamics between two modes, a latent class choice model (LCCM) is employed using revealed preference survey data (N = 2061) collected from Beijing, Shanghai, and Shenzhen in China. Three distinct commuter segments are identified through latent class analysis, and results indicate that ride-hailing and subway predominantly exhibit a competitive relationship (46.34%). Moreover, user heterogeneity is evident across user groups. Competitive users ride more frequently and commute shorter distances, and carless users tend toward competitive relation, while cooperative behaviors are linked to income and metro accessibility. Interestingly, commonly used spatial proximity assumption in previous studies exerts few influences on empirical modal interactions. Designing user-targeted takeaways for different transportation participants, this work offers valuable insights for improving multimodal travel efficiency and sustainable urban mobility.
Through large-scale new construction, expansion and comprehensive development of suburban railways, the Tokyo metropolitan area has effectively driven the development of the peripheral areas of the metropolitan circle, which has achieved good socioeconomic benefits and is a model for the construction of human settlements. This study adopts an exploratory sequential mixed-methods research design, taking the JR Chuo Express Line, Tokyu Den-en-toshi Line, and TX Express Line as typical cases. It first conducts a historical policy discussion based on textual analysis of the transformation and renovation of Tokyo’s suburban railways and their relationship with urban development, sorting out the characteristics and successful experiences of integrated development along suburban railways in Tokyo. Then, it carries out a data-based discussion on the current situation, summarizing the effects of the lines and their stations on driving population growth and land prices, the TOD (transit-oriented development) model along the lines, and the collaborative strategies between railways, stations, and surrounding cities, so as to analyze the synergistic relationship between the transformation of suburban railways and urban development in Tokyo as well as the effects after the transformation. Research results indicate that during the early construction and development stages of the outer areas of the Tokyo metropolitan area, suburban railways have had a positive effect on the development of population and land prices along the line, as well as the integrated development of stations and cities. Their construction experience can provide reference for the formulation of suburban railway development strategies and integrated development along the lines in similar international metropolitan circles.
We are presenting a crew planning optimization project for the train drivers of an urban rail transportation company. The core is about crew rostering, i.e., defining over a certain period of time (e.g., one year) for each train driver sequences of working days and rest days, and also specifying whether working days shall contain either some early, late, or night shift. But the project was not only about solving just one classical crew rostering problem. Rather, during the last three years, the entire process of crew rostering had been investigated. Apart from having modeled, solved, and implemented two different variants of crew rostering—a cyclic and an acyclic one—there have been designed and used three further optimization models for less typical surrounding sub-processes, as well as a simple crew assignment model. During the design and implementation process of these six optimization models, the focus had been put on their annual and daily usability, respectively. In particular, many practical requirements had been collected and implemented in rather straightforward ways. The result is a family of mathematical optimization models, whose results cover the valid annual crew rosters from the year 2024 on, as well as the daily assignment of specific duties to train drivers from May 2025 on at S-Bahn Berlin GmbH.
Subway Station Construction Site Layout Planning (SSCSLP) in dense urban cores is characterized by extreme spatial constraints. Conventional Constraint-Preserving Search (CPS) paradigms often exhibit significant limitations in such environments. Specifically, the strict rejection of infeasible solutions fragments the search space, frequently causing stagnation in local optima. To address these challenges, a novel Graph-based Dynamic Constraint-Relaxation Multi-Objective Optimization Framework is proposed. An Edge-Attributed Weighted Graph is utilized to capture complex spatial dependencies. Uniquely, the Graph-based Dynamic Constraint-Relaxation NSGA-II (GDCR-NSGA-II) is developed to overcome optimization bottlenecks. A Dynamic Constraint-Relaxation Strategy (DCRS) transforms hard constraints into a continuous penalty landscape. This mechanism establishes an infeasibility-driven search trajectory, guiding the population from the infeasible region toward the global optimum at the feasible boundary. The proposed framework was validated using a case study of Chongqing Rail Transit Line 27. Comparative analysis demonstrated that, when the single best feasible solution identified by the conventional method was strictly used as the benchmark, the proposed framework reduced the average construction cost by approximately 49.4% and improved average safety performance by 63.7%. Consequently, this study provides robust theoretical support for intelligent decision-making in ultra-constrained engineering scenarios.
