STDI-Net: Spatial-Temporal Network with Dynamic Interval Mapping for Bike Sharing Demand Prediction

TL;DR

This paper introduces STDI-Net, a deep learning model that predicts bike sharing demand by modeling joint spatial-temporal data and dynamically adjusting for different time intervals, improving accuracy over existing methods.

Contribution

The paper presents a novel deep learning framework that integrates joint spatial-temporal modeling with dynamic interval mapping for bike sharing demand prediction.

Findings

STDI-Net outperforms existing methods on NYC Bike dataset.

Dynamic interval mapping improves demand prediction accuracy.

Joint spatial-temporal modeling captures complex demand patterns.

Abstract

As an economical and healthy mode of shared transportation, Bike Sharing System (BSS) develops quickly in many big cities. An accurate prediction method can help BSS schedule resources in advance to meet the demands of users, and definitely improve operating efficiencies of it. However, most of the existing methods for similar tasks just utilize spatial or temporal information independently. Though there are some methods consider both, they only focus on demand prediction in a single location or between location pairs. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Interval Network (STDI-Net). The method predicts the number of renting and returning orders of multiple connected stations in the near future by modeling joint spatial-temporal information. Furthermore, we embed an additional module that generates dynamical learnable mappings for different time intervals, to include the factor that different time intervals have a strong influence on demand prediction in BSS. Extensive experiments are conducted on the NYC Bike dataset, the results demonstrate the superiority of our method over existing methods.