  As Public Transport (PT) becomes more dynamic and demand-responsive, it
increasingly depends on predictions of transport demand. But how accurate need
such predictions be for effective PT operation? We address this question
through an experimental case study of PT trips in Metropolitan Copenhagen,
Denmark, which we conduct independently of any specific prediction models.
First, we simulate errors in demand prediction through unbiased noise
distributions that vary considerably in shape. Using the noisy predictions, we
then simulate and optimize demand-responsive PT fleets via a linear programming
formulation and measure their performance. Our results suggest that the
optimized performance is mainly affected by the skew of the noise distribution
and the presence of infrequently large prediction errors. In particular, the
optimized performance can improve under non-Gaussian vs. Gaussian noise. We
also find that dynamic routing could reduce trip time by at least 23% vs.
static routing. This reduction is estimated at 809,000 EUR/year in terms of
Value of Travel Time Savings for the case study.
