to estimate collision risk. i.e. collision risk model
*Note: If the calculated risk does not meet the evaluation criteria, then risk reduction measures are examined to see how risk can be reduced.
Table 8 - 1: Evaluating System risk against a threshold
As regards [iii] in Table 8 - 1, it can be seen that CRM relies very heavily upon Cause-Consequence Modelling which effectively provides the basis for the Hazard identification process. Cause-Consequences modelling, which uses decision trees, operates on the assumption that truly independent variables contribute to occurrences and outcomes; i.e., independent events must occur to bring about an event. Cause-consequence analysis looks at the possible outcomes of these events by -
a) Identifying the sources of the potential hazard.
b) Identifying the events that could initiate such hazard occurring (fault trees).
c) Establishing the possible sequence of events that could result from such occurrences (event trees).
d) Quantifying - in probability and frequency terms - the likelihood of b) and c).
e) Determining the overall risk by aggregating all the known quantified hazards.
Predictably, collision risk modelling (and therefore risk estimation and evaluation, [iii] and [iv] in Table 8-1), takes account of the following parameters -
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Exposure of one aircraft to another (with aircraft operating on parallel ATS routes, this refers to passing frequency);

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Navigation performance in the lateral, longitudinal and vertical plane;

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Effects of surveillance and communication (e.g. effectiveness of ATC capability to detect aircraft on conflicting. This effectiveness is determined by the efficiency of the surveillance and communication capability available to ATC).

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As can be seen, Collision Risk Modelling is not intended for direct use by the airspace designer as envisaged within the context of this document. Nevertheless, the ATC perspective and input is critical in the setting up of correct assumptions on which the CRM is based, and during the HAZID process.
8.2.3 USE OF COLLISION RISK MODELLING
CRM is frequently to provide evidence for safety assessments. As explained in Chapter 3 of Part C, CRM – measurement against an absolute threshold – is only required when the proposed system does not bear sufficient resemblance to the reference system. This would be the case, for example, where RVSM is to be introduced (i.e. the ‘reference’ system is predicated conventional vertical separation minima, CVSM).
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8.3.1 INTRODUCTION
Monte Carlo Simulations rely upon the use of random numbers and probability statistics to solve mathematical problems. Although these methods were originally developed for the Manhattan Project during World War II, they are now applied to a wide range of problems, including nuclear reactor design, econometrics, stellar evolution, stock market forecasting - .
8.3.2 HOW MONTE CARLO SIMULATIONS WORK
These simulations take their name from the capital of Monaco – a city whose main attractions include casinos. Roulette, dice and slot machines feature in these casinos, and each of these games provide entertainment by exploiting the random behaviour of the roulette wheel, dice or slot machine.
Similarly, Monte Carlo methods randomly select values to create scenarios of a problem. These values are taken from within a fixed range and selected to fit a probability distribution
　
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