High‐speed rails have a significantly higher driving safety requirement than other public transport because of more passengers and faster speed. However, the particularity of train driving often leads to railway drivers more susceptible to drowsiness and fatigue, and this fatigue has a distinct personality. Here, the authors analyse the mechanism of individual fatigue generation of different drivers, and use eye movement data collected by non‐contact means as an objective measurement, and combine subjective sleepiness assessment to reveal the existence of individual differences in fatigue in high‐speed railway driving. Furthermore, a novel driver‐specific feature weighted support vector machine (FWSVM) algorithm is proposed to handle the individual differences. In the FWSVM, features are assigned with different weights by the information gain to reflect classification importance and individual effects. The average accuracy of FWSVM is 90.98%, the average sensitivity is 92.01%, and the average specificity is 89.88%, which is better than the classical SVM. Such improvements are attributed to the quantitative evaluation of individual effects by the weighted features. These results can be used as a preliminary study to design a high‐speed rail vehicle interface to prevent driver fatigue.

Purpose: The indoor vibration compaction test (IVCT) was a key step in controlling the compaction quality for high-speed railway graded aggregate (HRGA), which currently had a research gap on the assessment indicators and compaction parameters.Design/methodology/approach: To address these issues, a novel multi-indicator IVCT method was proposed, including physical indicator dry density (ρ_d) and mechanical indicators dynamic stiffness (K_rb) and bearing capacity coefficient (K₂₀). Then, a series of IVCTs on HRGA under different compaction parameters were conducted with an improved vibration compactor, which could monitor the physical-mechanical indicators in real-time. Finally, the optimal vibration compaction parameters, including the moisture content (ω), the diameter-to-maximum particle size ratio (R_d), the thickness-to-maximum particle size ratio (R_h), the vibration frequency (f), the vibration mass (M_c) and the eccentric distance (r_e), were determined based on the evolution characteristics for the physical-mechanical indicators during compaction.Findings: All results indicated that the ρ_d gradually increased and then stabilized, and the K_rb initially increased and then decreased. Moreover, the inflection time of the K_rb was present as the optimal compaction time (T_lp) during compaction. Additionally, optimal compaction was achieved when ω was the water-holding content after mud pumping, R_d was 3.4, R_h was 3.5, f was the resonance frequency, and the ratio between the excitation force and the M_c was 1.8.Originality/value: The findings of this paper were significant for the quality control of HRGA compaction.