FCTNet:基于双域深度学习的公交车到站时间预测方法

FCTNet:Bus Arrival Time Prediction Method Based on Dual Domain Deep Learning

公交车到站时间预测是智能公交系统的重要组成部分,可以给乘客提供精确的到站时间,还可以帮助调度员进行更合理的调度安排。为此,提出一种基于卷积、注意力机制和FFT的对时域和频域进行双域深度学习的公交车到站时间预测算法FCTNet(FFT-Conv-Transformer),该算法融合了傅里叶变换、卷积神经网络和注意力机制,其可以对公交车单站和多站的到站时间进行预测。其中利用傅里叶变换和卷积神经网络在频域上学习输入数据的时空特征,同时保留时域信号,利用注意力机制学习输入序列的全局依赖关系,预测最终结果。在重庆市465,506和262这3条公交线路到站时间预测实验中,FCTNet网络模型的平均绝对百分比误差和平均绝对误差优于实验对比算法,在最繁忙的465线路中FCTNet网络模型的平均相对误差相对已有最好模型降低了2.34%,平均绝对误差降低了4.59 s。

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.