Diffusion probabilistic model for bike-sharing demand recovery with factual knowledge fusion

• Published in: Neural networks 2024-11, Vol.179, p.106538, Article 106538
• Main Authors: Huang, Li, Li, Pei, Gao, Qiang, Liu, Guisong, Luo, Zhipeng, Li, Tianrui
• Format: Article
• Language: English
• Subjects: Bike flow recovery; Diffusion learning; Spatial–temporal interaction; Urban bike-sharing system
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2024.106538

The mining of diverse patterns from bike flow has attracted widespread interest from researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from bike demand records. Nevertheless, a tricky reality is the frequent occurrence of missing bike flow, which hinders us from accurately understanding flow patterns. This study investigates an interesting task, i.e., Bike-sharing demand recovery (Biker). Biker is not a simple time-series imputation problem, rather, it confronts three concerns: observation uncertainty, complex dependencies, and environmental facts. To this end, we present a novel diffusion probabilistic solution with factual knowledge fusion, namely DBiker. Specifically, DBiker is the first attempt to extend the diffusion probabilistic models to the Biker task, along with a conditional Markov decision-making process. In contrast to existing probabilistic solutions, DBiker forecasts missing observations through progressive steps guided by an adaptive prior. Particularly, we introduce a Flow Conditioner with step embedding and a Factual Extractor to explore the complex dependencies and multiple environmental facts, respectively. Additionally, we devise a self-gated fusion layer that adaptively selects valuable knowledge to act as an adaptive prior, guiding the generation of missing observations. Finally, experiments conducted on three real-world bike systems demonstrate the superiority of DBiker against several baselines.