Space-time clustering-based method to optimize shareability in real-time ride-sharing.

ABSTRACT: Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the sha

SUBMITTER: Alisoltani N 

PROVIDER: S-EPMC8759693 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature