Match the items in Group-I with the most appropriate stages of travel demand modelling in Group-II.
\[\begin{array}{|c|c|} \hline \textbf{Group I} & \textbf{Group II} \\ \hline (P)\ \text{US-EPA's MOVES} & (1)\ \text{Trip Assignment} \\ (Q)\ \text{Fratar Model} & (2)\ \text{Trip Production} \\ (R)\ \text{Growth Factor Model} & (3)\ \text{Trip Distribution} \\ (S)\ \text{User Equilibrium} & (4)\ \text{Mobile source emission estimation} \\ & (5)\ \text{Destination Choice} \\ \hline \end{array} \]
Step 1: US-EPA's MOVES.
MOVES (Motor Vehicle Emission Simulator) developed by US-EPA is specifically used for mobile source emission estimation.
\[
P \Rightarrow 4
\]
Step 2: Fratar Model.
The Fratar model is used in Trip Distribution to update future-year OD (Origin-Destination) matrices based on growth factors.
\[
Q \Rightarrow 3
\]
Step 3: Growth Factor Model.
Growth factor models estimate future trips by projecting current traffic volumes using socioeconomic data → part of Trip Production.
\[
R \Rightarrow 2
\]
Step 4: User Equilibrium.
User equilibrium principle (Wardrop's principle) is applied in traffic assignment, where no driver can reduce travel time by changing route → Trip Assignment.
\[
S \Rightarrow 1
\]
\[
\boxed{P-4, \ Q-3, \ R-2, \ S-1}
\]
P and Q play chess frequently against each other. Of these matches, P has won 80% of the matches, drawn 15% of the matches, and lost 5% of the matches.
If they play 3 more matches, what is the probability of P winning exactly 2 of these 3 matches?