Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

AbstractTo meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data ar...