As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation and speed in a unified way. This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. The experimental results show that the method has a better performance than ARIMA, LSTM, and GCN.

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As an essential parameter in the belief rule base (BRB), referential values refer to evaluation criteria for describing attributes using quantitative data or linguistic terms, the rationality and preciseness of which are important to the modeling accuracy. At present, the studies on referential values of BRB are mainly related to single-valued data. However, due to the inherent uncertainty, ambiguity, and vagueness of expert knowledge, the single-valued references provided by experts cannot represent qualitative information adequately. In this paper, a novel BRB with interval-valued references (BRB-IR) is proposed, in which qualitative knowledge and quantitative data can be integrated to construct models. First, the interval-valued referential values provided by experts are optimized by a nonlinear optimization algorithm to obtain the optimal referential values. Furthermore, other model parameters are optimized by the projection covariance matrix adaptation evolutionary strategy (P-CMA-ES) algorithm. Finally, a case study for pipeline leak detection is constructed to verify the model's effectiveness, and the results show that the proposed BRB-IR is more effective and characterizes expert knowledge better than the classical BRB using single-valued references.

In this paper, a new assessment method based on the interval evidential reasoning (IER) rule is proposed to solve the problem of physical and mechanical property assessment (PMPA) for particleboards. Because the detection data of the density and thickness swelling (TS) of particleboards are in an interval form, a model with precise values as input becomes inappropriate, so the PMPA of particleboards is not feasible. In the proposed method, expert knowledge and interval data are integrated to solve the assessment problem. First, the overall reliability of attributes is calculated, and the interval data are transformed into an interval belief structure. Then, the multiple interval belief structures are aggregated by ER nonlinear optimization models. Finally, the assessment results are obtained by utility theory. With the proposed method, the PMPA of particleboards with interval values can be assessed reasonably, and the combination interval belief degree of different grades of particleboard can be obtained, which has a certain guiding significance for the production and subsequent operation of enterprises. A case study for the PMPA of particleboards is conducted to demonstrate the effectiveness of the proposed method.

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