Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction

This paper proposes a convolution neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with high accuracy. Spatiotemporal traffic dynamics is converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. CNN is applied to the image following two consecutive steps: abstract traf...

Transportation on Demand

Transportation Demand Management

Rolling out of bed, José checks his CommuterAlert traveler information system while his coffee brews. The regional commuter assistance program was offering the system through a local automobile dealer when José purchased the family's vehicle. CommuterAlert is predicting that his 10-mile commute on I-275, which normally takes 29 minutes, will now take 46 to 52 minutes. In his printer are identif...

Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation

Despite the tremendous achievements of deep convolutional neural networks (CNNs) in most of computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step visualization method that aims to shed light on how deep CNNs recognize images and the objects therein. We start out with a layer-wise relevance propagation (LRP) step w...

Layer-wise learning of deep generative models

When using deep, multi-layered architectures to build generative models of data, it is difficult to train all layers at once. We propose a layer-wise training procedure admitting a performance guarantee compared to the global optimum. It is based on an optimistic proxy of future performance, the best latent marginal. We interpret autoencoders in this setting as generative models, by showing tha...