Analysis And Modeling Of Pedestrian Movement Behavior Based On Cellular Automata Model

With the deepening of urbanization,people pay more and more attention to the safety control of people in urban public space.In recent years,with the improvement of public safety awareness,pedestrian evacuation research has become the current research hotspot.In recent years,with the improvement of public safety awareness,pedestrian evacuation research has become the current research hotspot.Compared with human experiment,pedestrian dynamic model has the advantages of low cost and low risk.In this paper,based on the micro cellular automata model,combined with fuzzy logic theory,Logit model and Gaussian mixture model,pedestrian dynamics model is established to provide effective suggestions for pedestrian evacuation.Firstly,a cellular automata model based on fuzzy logic theory is established to simulate pedestrian evacuation from a multiple-exit room.The exit selection behavior of pedestrians plays an important part in the process of evacuation.When pedestrians select the exit,the distance and density are adopted as two important input variables in the fuzzy logic method,and the probability of selecting each exit is defined as the output variable of fuzzy logic method.The output variable of fuzzy logic,exit width and herding behavior are combined to determine the target exit.The competitiveness of each pedestrian is calculated by Logit model to solve the position conflicts among pedestrians.The validation of the model is demonstrated by comparing the simulation data with the real data.The effects of attributes of pedestrians,exits and obstacles on evacuation are studied in simulations.Results show that large public facilities should control the inflows of pedestrians,and the reasonable increase of the exit quantity and exit width are effective for improving the evacuation efficiency.In the design of buildings,obstacles need to be designed reasonably,which should not be too large or too small.At the same time,obstacles should be kept at a certain distance from the exit,so as to ease the exit congestion and improve the evacuation efficiency.Secondly,a pedestrian distribution prediction model based on fuzzy logic theory is established to study the relationship between platform facilities and pedestrian distribution.Collect the actual flow and speed data of pedestrians on the platform,and the target door of pedestrians is determined by the distance,density and waiting area.The rationality of the model is verified by comparing the simulation results with the actual data.The results show that increasing the number of pedestrians and escalators can help to improve the balance of the distribution of waiting pedestrians.The pedestrian companion behavior has little effect on the pedestrian distribution,but it can reduce the overall average speed of pedestrians.The distance between multiple escalators should be the same as far as possible.Setting indicators or guides in front of the doors with high density of waiting pedestrians is beneficial to balance the distribution of waiting pedestrians on the platform.Finally,the pedestrian evacuation model considering the guide is established to study the influence of the guide on the evacuation behavior.Bayesian information criterion is used to determine the optimal number of guides,EM algorithm is used to determine the optimal position of guides,fuzzy logic theory is used to simulate the selection behavior of pedestrians to guides,and pedestrians and guides are driven by cellular automata model.The results show that the number of guides can improve the evacuation efficiency to a certain extent,but the number of guides is not the more the better.Increasing the exit width within a certain range can improve the exit capacity and effectively reduce the evacuation time.When the guide speed is 75% of the pedestrian speed,the evacuation efficiency is the highest.Pedestrians should leave the station as soon as possible after getting off the train to reduce their stay on the platform.Pedestrians must keep calm during evacuation to avoid panic.