Predictive model for pre-movement time of individuals based on dynamic vision algorithms
Based on cellular automata,a dynamic visual algorithm considering visual obstruction was proposed,and a pre-movement time model was established in combination with the dynamic visual algorithm.Different from explaining the distribution of pre-movement time from an inductive perspective of data,the present paper intended to approach the subject from the perspective of the following effect,the reason for the distribution difference of pre-movement time in different layout scenarios was explained at the level of the action mechanism.The rationality of the model was verified by simulating evacuations in different types of building scenarios and comparing the results with real experimental data.Based on the proposed model,the influence of visual transparency,evacuation guiders,and crowd density on the pre-movement time was investigated by conducting controlled variable tests,the variables were whether or not obstructions obstructed the vision,the proportion of evacuation guides,and the density of the crowd,and the layout of the scenes in each group of test remained consistent.The test results show that:(1)The model can reflect the trend of pre-movement time changes of the crowd under different types of evacuation scenarios,and the simulation results of each scenario were close to the real test results,indicating that the model had a certain rationality.(2)The effect of visual transparency was not obvious:increasing the visual transparency reduced the average pre-movement time from 67.53 s to 62.77 s,reducing by 7.05％.(3)The effect of guiders was significant:when the proportion of guiders increases from 0 to 20％,the average pre-movement time was reduced from 53.34 s to 30.17 s,reducing by 43.44％.(4)Increasing the crowd density can help to reduce pre-movement time:in a single room scenario,when the crowd density increases from 0.1 person/m2 to 0.9 person/m2,the average pre-movement time of the crowd is shortened by 9.66 s,reducing by 17.45％.
public safetypedestrian evacuationpre-movement timedynamic visual algorithmcellular automataevacuation simulation
NETL
NSTL
万方数据
维普
