A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.