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Crowd evacuation simulation using virtual reality (VR) is significant for digital emergency response construction. However, existing evacuation simulation studies suffer from poor adaptation to complex environments, inefficient evacuations, and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation. To more realistically express the crowd evacuation results obtained under the influence of fire environments and the subjective consciousness of pedestrians in subway stations, we designed a dynamic pedestrian evacuation path planning method under multiple constraints, analysed the influences of an ‘environmental role’ and a ‘subjective initiative’ on crowd evacuation, established an improved social force model (ISFM)-based crowd evacuation simulation method in VR, developed a prototype system and conducted experimental analyses. The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity. In simulation experiments without disaster scenarios, the improved model's crowd evacuation efficiency improved by averages of 12.53% and 15.37% over the commercial Pathfinder software and the original social force model, respectively. The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subway fires.

Efficient and intuitive representation of floods can improve people’s perception, which is useful for flood emergency management and decision making. However, the current methods of visualizing flood disaster scenes have the shortcomings of data redundancy and low-efficiency. The interference of the complex background can be avoided through region of interest (ROI) extraction, because the attention will be quickly attracted by a few salient visual objects. First, the characteristics of the flood disaster scene object are analysed, and the method for scene division and data organization is established. Second, the region of interest is extracted according to the time series data of the flood evolution process simulated using cellular automata, and the dynamic identification model of the objects of interest is established. Then, a dynamic scheduling queue model with service interruption is designed to optimize the rendering efficiency of flood scenes and improve the perception efficiency. Finally, a prototype visualization system was developed and the experimental results show that approximately 30% of the redundant data are reduced, and the scene rendering efficiency is increased by approximately 15%. The non-ROI visualization is weakened by using the rules of human visual cognition, which improves the perception efficiency of flood scenes.

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.

As an important technology of digital construction, real 3D models can improve the immersion and realism of virtual reality (VR) scenes. The large amount of data for real 3D scenes requires more effective rendering methods, but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality. In this study, the location of the viewing frustum is predicted by a Kalman filter, and eye-tracking equipment is used to recognize the region of interest (ROI) in the scene. Finally, the real 3D model of interest in the predicted frustum is rendered first. The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance, the prediction accuracy is approximately 87%, the scene rendering efficiency is improved by 8.3%, and the motion sickness is reduced by approximately 54.5%. These studies help promote the use of real 3D models in virtual reality and ROI recognition methods. In future work, we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.

The visualization of flood disasters in virtual reality (VR) scenes is useful for the representation and sharing of disaster knowledge and can effectively improve users’ cognitive efficiency in comprehending disaster information. However, the existing VR methods of visualizing flood disaster scenes have some shortcomings, such as low rendering efficiency and poor user experience. In this paper, a tunnel vision optimization method for VR flood scenes based on Gaussian blur is proposed. The key techniques are studied, such as region of interest (ROI) calculation and tunnel vision optimization considering the characteristics of the human visual system. A prototype system has been developed and used to carry out an experimental case analysis. The experimental results show that the number of triangles drawn in a flood VR scene is reduced by approximately 30%–40% using this method and that the average frame rate is stable at approximately 90 frames per second (fps), significantly improving the efficiency of scene rendering and reducing motion sickness.

Large-scale crowd evacuation simulation in virtual reality is of great significance for emergency response. However, the existing large-scale crowd evacuation simulation methods are challenging to apply to the virtual reality scene. Thus, the efficiency of large-scale crowd rendering in virtual reality needs to be improved. Here we proposed a chain navigation grid for virtual reality large-scale crowd evacuation simulation. The objective of this study was to apply traditional cellular automata crowd evacuation simulation algorithms to virtual reality scenes through a chain navigation grid. Combined with the characteristics of crowd movement, we proposed the vertex raster rendering method to efficiently visualize large-scale crowds in virtual reality scenes. Finally, we constructed two evacuation scenes and carried out experimental analyses. The results show that the chain evacuation navigation grid can transform the discrete crowd movement of cellular automata into continuous crowd movement in virtual reality and can connect multiple cellular automata corresponding to multi-floors. Compared with the evacuation results of Pathfinder software, the chain evacuation navigation can add the factor of environmental familiarity. Therefore, this study improved evacuation simulation by increasing about 6% efficiency in time and computing process. Compared with the traditional crowd evacuation rendering method, the vertex raster rendering method increases the maximum number of people rendering from 2 times to 31 times.

The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing methods have shortcomings, such as a long and costly deposit volume acquisition cycle, lack of knowledge and guidance, complex operations for scene modeling expression, and low scene rendering efficiency. Therefore, this paper focuses on the study of a three-dimensional visualization and optimization method for landslide disaster scenes guided by knowledge, and discusses key technologies such as the rapid acquisition of landslide deposit volume information based on three-dimensional reconstruction, the knowledge-guided dynamic modeling visualization of disaster scenes, and scene optimization considering visual significance. The prototype systems are developed and used in a case experiment and analysis. The experimental results show that the proposed method can quickly obtain the deposit volume, and the results are equivalent to ContextCapture, Metashape, and Pix4Dmapper software. The method realizes the dynamic visualization of the whole disaster process, provides rich information, achieves high readability, and improves the efficiency of scene rendering, with a stable average rendering frame rate of more than 80 frames/second.