You need an asymmetric error measure. This will lead to interesting consequences, by the way, such as that your optimal forecast will not be the mean (average) anymore.
I'll give you an example of an asymmetric measure: $$|\ln \frac{f(x)} y |$$
Your model forecasts $f(x)$, while the true demand is $y$. If you overestimate the number of bikes, then your loss is the unused capacity. If you underestimate then the loss is unobtained revenue plus dissatisfied customers, who may leave your service.
Suppose, you overestimated the demand by 90%, in this case the loss is $|\ln 1.9|\approx 0.64$, while if you underestimate by 90% your loss is $|\ln 0.1|\approx 2.3$, which is much larger.
The intuition behind this loss is as follows. Look at the Taylor approximation for small errors $|e=f(x)-y|<<1$:
$$\ln \frac{f(x)} y=\ln \frac{y+e} y=\ln(1+\frac e y)
\approx\frac e y$$
For small errors $e$ this loss function is similar to relative loss. However, for larger errors it becomes very asymmetric penalizing underforecasting more than overforecasting.
Note, that this particular loss will lead to the optimal forecast being a median instead of the usual mean.