  Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides
with their own private cars, without the involvement of professional drivers.
It is a prominent collective intelligence application producing significant
benefits both for individuals (reduced costs) and for the entire community
(reduced pollution and traffic), as we showed in a recent publication where we
proposed an online approximate solution algorithm for large-scale P2P-RS. In
this paper we tackle the fundamental question of assessing the benefit of
predicting ridesharing requests in the context of P2P-RS optimisation. Results
on a public real-world show that, by employing a perfect predictor, the total
reward can be improved by 5.27% with a forecast horizon of 1 minute. On the
other hand, a vanilla long short-term memory neural network cannot improve upon
a baseline predictor that simply replicates the previous day's requests, whilst
achieving an almost-double accuracy.
