This study investigated the impact of subway car interior design on passenger evacuation and boarding/alighting efficiency. The usability of pedestrian agent models was verified through real-life experiments. A seven-factor orthogonal simulation experiment was designed, using key geometric features of the subway car interior as variables. The results of the computer simulation showed that the impact of subway car interior design factors on evacuation and boarding/alighting time was not entirely consistent, with seat layout and door width being the most important factors affecting passenger movement. In the evacuation scenario, only the connectivity of the subway car has no effect on evacuation time, while in the boarding and alighting scenario, seat layout, car type, door width, and foyer width all significantly affect boarding and alighting time. Multivariate regression models were established to predict evacuation and boarding/alighting times through design features, which can explain 86.7% and 58.9% of the time variation, respectively. The research results were used to guide subway car design, and the proposed new scheme demonstrated better performance.

Enhancing transparency through interface design is an effective method for improving driving safety while reducing driver workloads, potentially fostering human–machine collaboration. However, to ensure system usability and safety, operator psychological factors and operational performance must be well balanced. This study investigates how the introduction of transparency design into urban rail transit driving tasks influences drivers’ situational awareness (SA), trust in automation (TiA), sense of agency (SoA), workload, operational performance, and visual behavior. Three transparency driver–machine interface (DMI) information conditions were evaluated: DMI1, which provided continuous feedback on vehicle operating status and actions; DMI1+2, which added inferential explanations; and DMI1+2+3, which further incorporated proactive predictions. Results from simulated driving experiments with 32 participants indicated that an appropriate level of transparency significantly enhanced TiA and SoA, thereby yielding the greatest acceptance. High transparency significantly aided in predictable takeover tasks but affected gains in TiA and SoA, increased workload, and disrupted perception-level SA. Compared with previous research findings, this study indicates the presence of a disparity in transparency needs for low-workload tasks. Therefore, caution should be exercised when introducing high-transparency designs in urban rail transit driving tasks. Nonetheless, an appropriate transparency interface design can enhance the driving experience.