P-Ride: A Shareability Prediction Based Framework in Ridesharing

Abstract

Ridesharing services aim to reduce travel costs for users and optimize revenue for drivers and platforms by sharing available seats. Existing works can be roughly classified into two types, i.e., online-based and batch-based methods. The former mainly focuses on responding quickly to the requests, and the latter focuses on meticulously enumerating request combinations to improve service quality. However, online-based methods perform poorly in service quality due to the neglect of the sharing relationship between requests, while batch-based methods fail in terms of efficiency. To obtain better service quality more efficiently, we propose a shareability prediction-based framework P-Ride. Specifically, we first introduce the k-clique listing strategy in graph theory based on the shareability graph to reduce the infeasible request combinations. Moreover, we extend the shareability graph to the hypergraph structure to represent the higher-order shareable relationships among requests. Furthermore, we devise a shareability prediction model that supports the prediction of sharable relationships for request combinations of an arbitrary size, which helps further filtering of candidate request combinations with GPU devices acceleration. The extensive experimental results demonstrate the efficiency and effectiveness of our proposed P-Ride framework.