Predicting Requests in Large-Scale Online P2P Ridesharing
This work addresses the problem of optimizing ridesharing efficiency for service providers and users, but it is incremental as it builds on prior research on online solution algorithms.
The paper investigates the benefit of predicting ridesharing requests in peer-to-peer ridesharing optimization, finding that a perfect predictor improves total reward by 5.27% with a 1-minute forecast horizon, while a vanilla LSTM neural network fails to outperform a baseline predictor despite higher accuracy.
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.