Learning heterogeneous spatial-temporal representation for bike-sharing demand prediction

Abstract

Bike-sharing systems, aiming at meeting the public's need for”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long short-term memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.