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

Li Huang, Pei Li, Qiang Gao, Guisong Liu, Zhipeng Luo, Tianrui Li

Abstract: 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.