Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach

TL;DR

This paper introduces a graph-based deep learning model that leverages multimodal spatiotemporal data to improve bike sharing demand prediction by capturing interactions across different transportation modes.

Contribution

It proposes a novel multi-relational graph neural network that encodes cross-mode spatial dependencies, addressing the lack of multimodal integration in existing demand prediction models.

Findings

The proposed model outperforms existing methods in NYC data.

Multimodal data integration improves demand prediction accuracy.

The approach effectively captures cross-mode spatial dependencies.

Abstract

Bike sharing is an increasingly popular part of urban transportation systems. Accurate demand prediction is the key to support timely re-balancing and ensure service efficiency. Most existing models of bike-sharing demand prediction are solely based on its own historical demand variation, essentially regarding bike sharing as a closed system and neglecting the interaction between different transport modes. This is particularly important because bike sharing is often used to complement travel through other modes (e.g., public transit). Despite some recent efforts, there is no existing method capable of leveraging spatiotemporal information from multiple modes with heterogeneous spatial units. To address this research gap, this study proposes a graph-based deep learning approach for bike sharing demand prediction (B-MRGNN) with multimodal historical data as input. The spatial dependencies across modes are encoded with multiple intra- and inter-modal graphs. A multi-relational graph neural network (MRGNN) is introduced to capture correlations between spatial units across modes, such as bike sharing stations, subway stations, or ride-hailing zones. Extensive experiments are conducted using real-world bike sharing, subway and ride-hailing data from New York City, and the results demonstrate the superior performance of our proposed approach compared to existing methods.