With the worldwide occurrence of unconventional emergencies in recent years, increasing importance is now attached to the problem of large-scale crowd evacuation in the field of public security. To date, considerable research has been conducted into micro and macro evacuation models, fuelling 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 determine the impact of various uncertain factors on the large-scale evacuation process, we combine theoretical analysis and mathematical modeling to study the uncertainties which affect both the efficiency of and risks involved in large-scale crowd evacuation. We focus on three aspects of large-scale crowd evacuation, namely disaster environment, external evacuation (rescue) guidance and massive evacuees, to achieve our goals, and aim to provide some theoretical and technical support for those responsible for emergency evacuation in the face of sudden disaster.
We first studied the differences between unconventional and conventional disasters as regards their randomness analysis and forecasting. Urban fire is used as an example of a conventional disaster, and a probability prediction model of urban fire occurrence is established, based on the power-law distribution characteristics of urban fires. Unconventional disasters, however, are marked by greater uncertainty and complexity on both the temporal and spatial scales. Therefore, when considering unconventional disaster parameters, we rely principally on assumptions. Using some reasonable assumptions as to 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 evacuation priority and the general pattern of disaster spread were comprehensively considered. Using the CCRP algorithm, evacuation planning was solved and compared to previous work. As a result of 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 from the framework of each road link at a different moment after the disaster occurs can effectively reflect the dynamic change in "appropriateness for travelling" of the evacuation road network in a disaster environment, considering both efficiency and risk.
In the investigation into rescue guidance, we first studied the propagation characteristics of panic emotion, which is prone to occur in large-scale evacuation. Applying the system dynamics method, we built a qualitative simulation model of large-scale evacuation. From the implementation of a series of scenarios with different inputs, it was found that an exponentially positive correlation exists between the severity of disaster and the spread of panic without rescue guidance. Conversely, with rescue guidance, the spread of panic may be effectively controlled. Moreover, the effectiveness of rescue guidance is influenced by the leading emotion in the crowd as a whole. The qualitative simulation model clearly 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 into the risks involved in large-scale evacuation integrated with evacuation guidance. We propose a concept of "characteristic densities" of large-scale crowd flow and deduce a series of characteristic crowd densities that affect large-scale movement of groups of people, as well as the maximum bearing density when extreme congestion occurs in a crowded area. Queuing theory is applied to these characteristic crowd densities to simulate crowd movement under the condition of infinite crowd flow crossing a bridge in lines. The movement characteristics of the crowd and the effects of typical crowd density on rescue strategies are studied. Furthermore, a "risk axis of crowd density" is proposed to determine the efficiency of rescue strategies in large-scale evacuation, with three regions in the risk axis, i.e. effective flow, the critical zone and non-effective flow. Finally, using some rational hypotheses regarding the value of evacuation risk, the risk axis of crowd density is illustrated quantitatively.
As regards massive evacuees, we introduce and quantify the impact of physiological and psychological factors on such evacuation, and use those data to establish a modified random Markov route selection model of the evacuees. Under the condition of instantaneous leakage and diffusion of toxic CO gas, the uncertainties of the evacuation process and results from the probabilistic Markov process description are analyzed in detail. It is found that when a single factor changes, the logarithm of the remaining population presents piecewise linear correlation with evacuation time. Clearance time increases logarithmically linearly with an increase in initial population and decreases linearly with an increase in node capacity. In addition, the variation in evacuation speed according to the degree of psychological panic in the situation of evacuation on foot has little influence on overall evacuation results. On the other hand, physiological risks under the atmosphere of poison gas, for example, may evidently affect evacuation results, and sufficient attention should be paid to such risks in large-scale emergency evacuation.
The proposed strategies of investigating uncertainties of large-scale crowd evacuation on these three levels, namely disaster environment, external evacuation guidance and massive evacuees, have integrated key elements related to unconventional emergencies comprehensively, adhering to the research thought from whole to local. Outcomes on the level of disaster environment may provide rational attitude to uncertainties in the overall evacuation plans under sudden disasters, while outcomes on the level of evacuation guidance and massive evacuees focus on the details related to the management (from external) and implementation (from internal) of large-scale evacuation.
| Date of Award | 15 Jul 2013 |
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| Original language | English |
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| Awarding Institution | - City University of Hong Kong
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| Supervisor | Siu Ming LO (Supervisor) |
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