High speed train ride comfort evaluation method based on IFOA-BPNN

Abstract

In order to improve the effectiveness of the comprehensive evaluation of passenger riding comfort of high-speed trains, and simplify the complexity of the evaluation process, it can guide the optimization design of high-speed trains to a certain extent. In this paper, a comprehensive evaluation comfort degree model based on improved Drosophila algorithm and back propagation neural network (IFOA-BPNN) is proposed. The Drosophila algorithm is improved by using chaos mapping the initial position of Drosophila population and changing the search step of flies in different periods to search for the optimal initial weight value and threshold of BPNN. According to the index evaluation data collected, the weight of the index was comprehensively calibrated from both subjective and objective aspects, and the ride comfort of the high-speed train was pre-analyzed by fuzzy comprehensive evaluation. Then the IFOA-BPNN model is used to evaluate the riding comfort of high-speed trains. The results of experiments and comparison with other different models show that the IFOA-BPNN model has reduced the mean square error (MSE), mean absolute error (MAE) and other indexes, and can effectively evaluate the comfort degree of passengers.

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“…However, major differences still exist in the definition of the uncomfortable range. Wang (2014) believed that a weighted acceleration range between 0.315 and 0.8 is comfortable and meets the comfort requirements of passengers traveling on high-speed trains. In his evaluation of a type of passenger seat, Zhang et al (2019) considered that a total weighted acceleration greater than 0.315 was in the uncomfortable range.…”