Abstract: Bus arrival time prediction is an important part of the smart bus system.It can provide passengers with accurate arrival time and help dispatchers make more reasonable scheduling arrangements.A bus arrival time prediction algorithm FCTNet(FFT-Conv-Transformer)based on convolution,attention mechanism and FFT is proposed for dual-domain deep learning of time domain and frequency domain.The algorithm integrates Fourier transform,convolutional neural network and attention mechanism to predict bus arrival times at single stops and multiple stops.Among them,Fourier transform and convolutional neural network are used to learn the spatiotemporal characteristics of the input data in the frequency domain while retaining the time domain signal.The attention mechanism is used to learn the global dependence of the input sequence and predict the final result.In the arrival time prediction experiment of three bus lines 465,506 and 262 in Chongqing,the average absolute percentage error and average absolute error of the FCTNet network model are better than the experimental comparison algorithm.In the busiest bus No.465,the average relative error of the FCTNet network model is better than that of the experimental comparison algorithm.Compared with the existing best model,it is reduced by 2.34%,and the average absolute error is reduced by 4.59s.

Key words: Arrival forecast, Attention mechanism, Time-frequency transformation, Convolution neural network, Deep neural network