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

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.