Like edwinem mentioned, the motion model just describes how the object should move. Consider gravity:
$$
\ddot{y} = -g \\
$$
If you wanted a motion model for position, then:
$$
y = y_0 + \dot{y}t + \frac{1}{2}\ddot{y}t^2 \\
y = y_0 + \dot{y}t - \frac{1}{2}gt^2 \\
$$
So if you have a ball at $y_0 = 5$, does the ball go down in the next instant or does it go up? The answer to that depends on the previous state of your system. If it had a vertical velocity such that $\left(\dot{y}t\right)>\left(\frac{1}{2}gt^2\right)$ then the ball goes up.
If you are finding that your predictions are very far from your measured values, then either your estimated states X_predict(t-1) are wrong or your model is wrong. That is, you're either starting in the wrong location or you're moving in the wrong direction.
:EDIT:
If you don't think anything should be moving, then you could do the same, but now instead of:
$$
\ddot{y} = -g\\
$$
You could model acceleration as resulting strictly from some noise:
$$
\ddot{y} = \sigma\\
$$
And then modify the position model to rely on "incremental" updates, such that:
$$
y = y_0 + \dot{y}t + \frac{1}{2}\ddot{y}t^2\\
$$
becomes:
$$
y_k = y_{k-1} + \dot{y}\Delta t + \frac{1}{2}\ddot{y}\Delta t^2 \\
$$
And then, if the noisy acceleration is an input to the system, you wind up with:
$$
\left[\begin{matrix}
\dot{y} \\
\ddot{y} \end{matrix}\right] = \left[\begin{matrix}
0 & 1\\
0 & 0 \end{matrix}\right]\left[\begin{matrix}
y \\
\dot{y}\end{matrix}\right] +
\left[\begin{matrix}
0 \\
1\end{matrix}\right]\sigma
$$
If you depict this as:
$$
\dot{x} = Ax + Bu \\
$$
then you can do a kind of integration, such that:
$$
x_k = I*x_{k-1} + \left(\dot{x}\Delta t\right) \\
$$
and so you get:
$$
\begin{matrix}
x_k \\
\left[\begin{matrix}
y_k \\
\dot{y}_k \end{matrix}\right]\end{matrix} = \begin{matrix}
I \\
\left[\begin{matrix}
1 & 0\\
0 & 1 \end{matrix}\right]\end{matrix} \begin{matrix}
x_{k-1} \\
\left[\begin{matrix}
y_{k-1} \\
\dot{y}_{k-1}\end{matrix}\right]\end{matrix} + \left(\begin{matrix}
A\Delta t \\
\left[\begin{matrix}
0 & \Delta t\\
0 & 0 \end{matrix}\right]\end{matrix}\begin{matrix}
x_{k-1} \\
\left[\begin{matrix}
y_{k-1} \\
\dot{y}_{k-1}\end{matrix}\right]\end{matrix} +
\begin{matrix}
B\Delta t & \sigma\\
\left[\begin{matrix}
0 \\
\Delta t\end{matrix}\right] & \sigma\end{matrix}\right)
$$
which of course reduces to:
$$
\left[\begin{matrix}
y_k \\
\dot{y}_k \end{matrix}\right] = \left[\begin{matrix}
1 & \Delta t\\
0 & 1 \end{matrix}\right]\left[\begin{matrix}
y_{k-1} \\
\dot{y}_{k-1}\end{matrix}\right] +
\left[\begin{matrix}
0 \\
\Delta t\end{matrix}\right]\sigma
$$
The above would be a decent first motion model if you had no prior knowledge of how you expected something to move.
