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Airport front-of-house vulnerabilities and mitigation options

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Abstract

A method for risk-informed comparison of mitigation options in situations with large epistemological uncertainties is presented by example: that of an improvised explosive device (IED) attack at a generic airport front-of-house (FoH). Specifically, a probabilistic model is built and distributions of scaled fatalities from probabilistic combinations of vehicle-based IED and personnel-based IED threats are generated using Monte Carlo methods. A risk assessment of the threats is given from the statistics of the scaled fatality distributions. We consider the risk reduction due to a combination of mitigation options, including IED screening, vehicle stand-off, and interception and detection. Sensitivity and stress analysis show that the ranking of the options, by their risk reduction, is relatively robust to the epistemological uncertainty incorporated into the model. The qualified conclusion of the example is that vehicle stand-off is the priority risk-effective mitigation option for IED attack at a generic FoH.

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Notes

  1. @Risk 5.0 in Palisade’s DecisionTools® Suite 5.0 Industrial, http://www.palisade.com/decisiontools_suite/default.asp.

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Authors and Affiliations

  1. Centre for the Quantification and Management of Risk, University of Adelaide, Adelaide, SA, 5005, Australia

    Steven Lord & Glenis Crane

  2. Counter Terrorism and Security Technology Centre, Defence Science and Technology Organisation, P.O. Box 1500, Edinburgh, SA, 5111, Australia

    Rick Nunes-Vaz & Alexei Filinkov

Authors
  1. Steven Lord
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  2. Rick Nunes-Vaz
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  3. Alexei Filinkov
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  4. Glenis Crane
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Corresponding author

Correspondence to Steven Lord.

Appendix: Simulation tables

Appendix: Simulation tables

The sample size for the Monte-Carlo simulation was balanced between adequate convergence in the statistics of the distributions of consequences and available resources, see Lord et al. (2010) for more details. The sample size chosen for the individual simulations was N = 10,000, which provided statistically sufficient convergence for the statistics of the mean and the 95th-percentile, and proportions in the range 1–99% (see Table 14), while making the time required for the complete analysis manageable. See Table 13 for the sample statistics and Table 14 for standard error (99% confidence interval) estimates for the distribution of consequences.

Standard error estimates cannot be applied to the maximum value. Sample estimates of maximum value of the consequence distribution with this sample size only roughly reflect the actual maximum. The graphs of the distributions in Lord et al. (2010) provide evidence for sample maximum ranges. Standard error estimates for chance of relative consequences greater than 5 (Rel. Con. > 5) when the sample has a very low (0–1%) percentage may also suffer from low population of the proportion (<100). Using standard error estimates for the 98th-percentile or 99th-percentile (not displayed), chance of Rel. Con. > 5 for sample values (0–1%) is given in Table 14 by < 1% or < 2%.

For simplicity of presentation the sampling error present from Monte-Carlo simulation (Table 14) was not reported in the statistics of the main text. The error is well within the resolution of the study and does not affect the conclusions of the main text.

Statistics of the unmitigated distribution of consequences are contained in Table 12. Table 14 contains the standard error (99% confidence interval) estimates for the values in Table 12.

Table 12 Statistics of the distributions of relative consequences for threat scenarios and distributions.
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Statistics of the estimated effect of mitigation on the distribution of consequences are contained in Table 13. Table 14 contains the standard error (99% confidence interval) estimates for the values in Table 13.

Table 13 Statistics of the distribution of relative consequences with mitigation.
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Table 14 Parameter estimates for the distributions of relative consequences with mitigation.
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Lord, S., Nunes-Vaz, R., Filinkov, A. et al. Airport front-of-house vulnerabilities and mitigation options. J Transp Secur 3, 149–177 (2010). https://doi.org/10.1007/s12198-010-0045-0

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