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 [Bistaffa et al., “A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers”, IEEE T-ITS]. In this project we tackle the fundamental question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation, by investigating whether including predictions of both classical and deep-learning methods results in an improvement of the above-mentioned benefits.
