A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model
1.1. About the Disaster Evacuation Demand Curve
1.2. Social Influence on Individual Evacuation Decision
1.3. Disaster Evacuation Demand Curve Modeling
1.4. Motivation and Objective of This Study
2. Model Development for Evacuation Demand Curves Estimation Based on SI Model
2.1. Model Framework
2.2. Formulization for Effect of Factors on Disaster Evacuation Demand Curves
2.2.1. Individual Characteristic
2.2.2. Social Influence
Evacuation State Inside the Community
Evacuation State Outside the Community
2.2.3. Geographical Location
2.2.4. Warning Degree
2.3. The Complete Model for Formulizing a Disaster Evacuation Demand Curve
3. Method for Parameter Sensitivity Analyses
4. Case Study-Tianjin Explosions
4.1. Tianjin Explosions Description
4.3. Model Results
4.3.1. Total Evacuation Demand Curve
4.3.2. Effect of Individual Characteristics
4.3.3. Social Influence and the Effect of Geographic Location
4.3.4. The Effect of Warning Degree
4.4. Sensitivity Analyses
- The evacuation demand curve in this model is like an S-curve, in which the evacuation demand increasing rate starts increasing slowly, then rapidly, slowly again, and gradually closes to zero. This model result agrees with those obtained in mathematical statistics based on empirical data.
- The individual characteristics can dramatically influence people’s evacuation decision making and the cumulative evacuation demands of the “Impressionable” people are always greater than that of “Neutral” and “Standpat” people.
- The cumulative evacuation demands of people in isolated communities would be less than the communities with many adjacent communities, which can be explained in that the isolated communities lack social influence and people in these areas cannot easily get information on the real-time evacuation state of the society.
- A higher warning degree issued can enhance people′s perceived risk levels so as to significantly increase evacuation demand. Since the influence of warning degrees raised is imposed on the whole people in the risk areas, the authorities can accelerate or slow the evacuation process by changing the warning degree.
- In the sensitivity analyses of parameters, the model is more sensitive to the contact frequency among people over unit time than other parameters. To improve the prediction precision of evacuation demand curves, great attention should be paid to the contact frequency among people.
Conflicts of Interest
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|Value||0.25||0.5 0.3 0.2||0.01 1||1 0.1||0.01 0.01||0.01 0.001 0.2 0.01|
|Community||Distance to the Explosion Source (km)||Surrounding Community|
|10||2.06||8, 9, 16|
|31||4.21||23, 30, 38, 48, 50|
|Parameter||Basic Reference Value||Value Range||Maximum Sensitivity||Average Sensitivity|
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Song, Y.; Yan, X. A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model. Int. J. Environ. Res. Public Health 2016, 13, 986. https://doi.org/10.3390/ijerph13100986
Song Y, Yan X. A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model. International Journal of Environmental Research and Public Health. 2016; 13(10):986. https://doi.org/10.3390/ijerph13100986Chicago/Turabian Style
Song, Yulei, and Xuedong Yan. 2016. "A Method for Formulizing Disaster Evacuation Demand Curves Based on SI Model" International Journal of Environmental Research and Public Health 13, no. 10: 986. https://doi.org/10.3390/ijerph13100986