基于天牛须小波神经网络的公交到站时间预测

doi:10.13229/j.cnki.jdxbgxb20191042

Abstract

Abstract:

Through analysis of the real bus operating environment， considering the difference in operating characteristics between working days and non-working days， a new bus arrival time predicting method was proposed based on the beetle Wavelet neural network （BAS-WNN） prediction model of search algorithm. This model uses the beetle whisker search algorithm with stronger optimization performance to optimize the initial parameters of the WNN so that the WNN has better performance in the prediction of time series. Finally， the historical driving data was used to train and model the neural network to achieve accurate prediction of the arrival time. The optimization algorithm is simulated with the traditional WNN algorithm and the Elman neural network algorithm in MATLAB. The comparison results show that no matter the working day or on non-working days， the BAS-WNN prediction model has a higher accuracy in predicting bus arrival time and the results are more stable.

Key words: intelligent transportation, bus arrival time prediction, wavelet neural network, beetle search algorithm, public transportation

CLC Number:

U491

Xian-yan KUANG,Hui-chao LUO,Rui ZHONG,Peng OUYANG. Bus arrival time prediction based on wavelet neural network optimized by Beetle Antennae Search[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 110-117.

Figures/Tables 11

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预测日期	预测模型	评估指标
预测日期	预测模型	MAPE/%	RMSE	PSI
3月24日（工作日）	BAS?WNN	8.36	9.71	0.0723
	WNN	11.52	10.24	0.1315
	Elman	14.96	11.24	0.1708
3月26日（非工作日）	BAS?WNN	7.98	9.65	0.0817
	WNN	10.34	10.07	0.1609
	Elman	14.63	11.15	0.1831

Bus arrival time prediction based on wavelet neural network optimized by Beetle Antennae Search

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