T-CT2CRP-SM: The exploration of dockless bike-sharing system rebalancing problems based on multi-modal signals in social networks

With the continuous advancement of data processing technologies, decision-making problems based on multi-modal signal systems (MMSSs) can be effectively addressed. In this context, rebalancing problems in dockless bike-sharing systems (DBSSs) within social networks increasingly rely on MMSSs. However, MMSSs introduce the issue of declining information quality, making it essential to ensure the reliability of both signals and models. Specifically, the paper addresses these challenges by exploring methods to reduce the impact of low-quality signals in MMSSs, leading to the design of a trustworthy multi-modal signal processing (TMSP) model. Yet, two main challenges remain in building the model, i.e., the lack of suitable frameworks to accurately characterize MMSSs, and insufficient coupling between clustering analysis and the consensus-reaching process (CRP). To address these challenges, the signal reliability is first improved by processing MMSSs with complex intuitionistic fuzzy sets (CIFSs). Then, based on the MMSS, a trustworthy clustering and two-stage, two-index CRP model based on similarity measurement (T-CT2CRP-SM) is constructed. Subsequently, experiments select a real-world MMSS-based DBSS rebalancing problem as the scenario and illustrate detailed decision-making steps. Finally, multiple experimental analyses are conducted to further validate the reliability and stability of the constructed model.