Research On The Uncertainties Of Large-scale Crowd Evacuation Under Unconventional Emergencies

With the worldwide occurrence of unconventional emergencies in recent years, the problem of large-scale crowd evacuation has increasingly attached great importance to the field of public security. To date, there have been a lot of researches on the micro or macro evacuation models, bringing a strong impetus to the development of the field of emergency evacuation. However, the uncertainties of large-scale evacuation under unconventional emergencies have not yet been fully elucidated. In order to reveal the impact of various uncertain factors and their mode of action in the large-scale evacuation process, we combine theoretical analysis and mathematical modeling together to study the uncertainties which affect both the efficiency and risk of large-scale crowd evacuation. Under the background of unconventional emergency, three levels, namely the disaster environment, the external evacuation (rescue) guidance and the massive evacuees are focused on respectively to achieve our goals, aiming to provide some theoretical and technical support for the emergency evacuation under sudden disasters.In the level of disasters, firstly we studied the difference of unconventional and conventional disasters on their randomness analysis and forecast. The unban fire is used as an example of conventional disasters and a probability prediction model of urban fire occurrence is established based on the power-law distribution characteristics of urban fires, while the unconventional disasters have more uncertainty and complexity in both temporal and spatial scales. Therefore, referring to some parameters of unconventional disasters, we mainly take the form of assumptions. Based on some reasonable assumptions of disaster spread and damage, we applied the dynamic network flow method to establish a multi-source multi-destination (MSMD) large-scale evacuation model, in which the evacuation priority and the general pattern of disaster spread have been comprehensively considered. Using CCRP algorithm, the evacuation planning has been solved and compared. Based on these analyses, we further proposed a concept of "route travelling efficiency risk"(RTE risk) and established its quantitative assessment framework. The value of RTE risk calculated by the framework for each road link in different moment after the disaster occurs can effectively reflect the dynamic change of the "appropriateness for travelling" of the evacuation road network in a disaster environment considering both efficiency and risk. In the level of rescue guidance, we firstly studied the propagation characteristics of panic emotion which is prone to appear in the large-scale evacuation. Applying the system dynamics method, we built a qualitative simulation model of large-scale evacuation. According to the implementation of a series of scenarios with different input, it is found that the severity of disaster is exponentially positive correlated with the panic spread without rescue guidance. On the contrary, with rescue guidance, the panic spread can be effectively controlled and the effectiveness of rescue guidance is influenced by the leading emotion in the whole crowds. The qualitative simulation model well reveals the interactions between key elements of the evacuation system and the uncertainties in the spread of panic, and reproduces a well-known phenomenon-"fast is slow"-in crowd evacuation.Secondly, we conducted quantitative research on the risk of large-scale evacuation integrated with the evacuation guidance. We proposed a concept of "characteristic densities" of large-scale crowd flow and deduced a series of characteristic crowd densities that affect the large-scale people movement, as well as the maximum bearing density when the crowd is extremely congested. Based on the characteristic crowd densities, the queuing theory has been applied to simulate the crowd movement under a situation of infinite crowd flow crossing bridge in lines. The moving characteristics of the crowd and the effects of typical crowd density on rescue strategies have been studied. Furthermore, a "risk axle of crowd density" is proposed to determine the efficiency of rescue strategies in large-scale evacuation with three regions in the risk axle, i.e. the effective flow, the critical zone and the non-effective flow. Finally, through some rational hypotheses for the value of evacuation risk, the risk axle of crowd density is illustrated quantitatively.In the level of massive evacuees, we introduced and quantified the impact of physiological and psychological factors on the large-scale evacuation, based on which we established and modified a random Markov route selection model of the evacuees. Under a background of instantaneous leakage and diffusion of CO poison gas, the uncertainties of evacuation process and results according to the probabilistic description of Markov process has been detailed analyzed. It is found that when single factor changes, the logarithm of left population presents piecewise linear characteristics with the evacuation time, the clearance time will increase logarithmic linearly with the increase of initial population and decrease linearly with the increase of node capacity. Besides, the modification of evacuation speed according to the degree of psychological panic in the situation of hiking evacuation has little influence on the overall evacuation results, while the personnel physiological risk under the atmosphere of poison gas, for example, can obviously affect the evacuation results and must be paid enough attention to in the large-scale emergency evacuation.