This paper proposes a deep architecture called residual spatio-temporal network (RSTN) for short-term travel demand forecasting. It comprises fully convolutional neural networks (FCNs) and a hybrid module consisting of an extended Conv-LSTM (CE-LSTM) that can achieve trade-off of convolutional operation and LSTM cells by tuning the hyperparameters of Conv-LSTM, convolutional neural networks (CNNs) and traditional LSTM. These modules are combined via residual connections to capture the spatial, temporal and extraneous dependencies of travel demand. The end-to-end trainable RSTN redefines the traditional prediction problem as a learning residual function with regard to the travel density in each time interval. Further more, a dynamic request vector (DRV)-based data representation scheme is presented, which catches the intrinsic characteristics and variation of the trend, to improve the performance of forecasting. Simulations with two real-word data sets show that the proposed method outperforms the existing forecasting algorithms, reducing the root mean square error (RMSE) by up to 17.87%.

本文提出了一种称为剩余时空网络（RSTN）的深度架构，用于短期旅行需求预测。它包括完全卷积神经网络（FCN）和由扩展Conv-LSTM（CE-LSTM）组成的混合模块，可以通过调整Conv-LSTM，卷积神经网络的超参数来实现卷积运算和LSTM细胞的权衡（ CNN）和传统的LSTM。这些模块通过剩余连接进行组合，以捕获旅行需求的空间，时间和外部依赖性。端到端的可训练RSTN重新定义了传统的预测问题，将其作为每个时间间隔中旅行密度的学习残差函数。此外，提出了一种基于动态请求向量（DRV）的数据表示方案，抓住趋势的内在特征和变化，提高预测性能。通过两个实词数据集的仿真表明，该方法优于现有的预测算法，可将均方根误差（RMSE）降低多达17.87％。