Explore Uncertainty in Residual Networks for Crowds Flow Prediction

摘要

The residual network has witnessed a great success in computer vision particularly on classification tasks, however, it has not been well studied in regression. In this work, we show its competence in a regression task - crowds flow prediction, which has strong implication to city safety and management. The problem of crowds flow prediction is challenging due to its fast dynamics. To address this issue, we explore residual learning with Gaussian regularization and propose a novel convolutional neural network called Gaussian noise residual networks (Noise-ResNet). Compared with the benchmark ST-ResNet on crowds flow prediction, the proposed architecture has three advantages: 1) Superior performance. Especially, it attains the state-of-the-art results on benchmark dataset BikeNYC. 2) Light architecture. Noise-ResNet only utilises one residual unit rather than STResNet with multiple ones, which greatly reduces the training time. 3) Interpretable input sequences. Noise-ResNet takes an input sequence that only considers the most important periodic data and closeness data, which makes the learning process more interpretable. Furthermore, experimental results substantiate that the Noise-ResNet can outperform ResNet with dropout on the same regression task.