To validate the validity of a driving simulation platform, this study constructs a dual dataset using field data and driving simulation experiments on an urban arterial road. Comprehensive validation is conducted from both subjective perception and objective quantification perspectives. Subjectively, questionnaires assessed simulation fidelity, driving workload, and physiological symptoms. Objectively, five core car-following indicators (speed, acceleration, relative speed, headway, and time headway) were analyzed using dynamic time warping and statistical methods. The results demonstrate that the simulated scenarios exhibit high subjective fidelity, reasonable task loads, and controllable motion sickness risks. Objectively, dynamic time warping confirms strong temporal pattern similarity, with waveform consistency proportions across core indicators ranging from 91.67% to 100.0%. Macroscopically, satisfactory relative aggregate similarity is demonstrated, with relative difference between means consistently constrained within a 20% threshold. However, strict absolute behavioral validity is unsupported due to significant statistical differences in sequence means, establishing clear boundary constraints for trend replication. It is speculated that systematic biases are primarily related to differences in risk perception within virtual environments.

As a novel traffic security facility to improve the environment of tunnels, the influence of tunnel sidewall decoration on drivers has been highly controversial. To analyze the impact of the multi-factor coupling of sidewall decoration effects on driving safety, eight combination schemes with different pattern elements and pattern spacings were designed to create a driving simulation environment. Twenty-seven drivers were recruited to obtain fine-grained driving behavior indicators via driving simulation experiments. The velocity following ratio, steering wheel angle, maximum deceleration, and accelerator power were selected to construct an index system. The visual information load of drivers was quantified by the landscape color quantified theory. Based on the analysis of the influence of the singular factor of the pattern element or pattern spacing on driving behavior, a coupling coordination degree model is introduced to quantify the relationship between the complexity of the pattern elements, the pattern spacing, and the coupling coordination degree, and a reasonable combination of their complexities is selected. The results show that the element complexity and pattern spacing of tunnel sidewall decoration have significant effects on driving behavior. Among the schemes considered in this study, the coupling effect of an element complexity of 562.1 and a pattern spacing of 5.5 m was found to be the optimal combination. The coupling coordination degree should be more than 0.8 as the threshold, and the model analysis results indicated that when the pattern spacing was fixed at about 10 m, the ideal element complexity was between 135.6–564.7. This study offers both theoretical and technical support for enhancing traffic safety through tunnel sidewall decoration. By defining optimal thresholds for information density and pattern spacing, it lays a solid foundation for the development of a standardized guideline on decoration content.