Hierarchical Neural Architecture Search for Travel Time Estimation

Abstract

We propose a novel automated deep learning framework, namely Automated Spatio-Temporal Dual Graph Convolutional Networks (Auto-STDGCN), for travel time estimation. Specifically, a hierarchical neural architecture search approach is introduced to capture the joint spatio-temporal correlations of intersections and road segments, whose search space is composed of internal and external search space. In the internal search space, spatial graph convolution and temporal convolution operations are adopted to capture the spatio-temporal correlations of the dual graphs. In the external search space, the node-wise and edge-wise graph convolution operations from the internal architecture search are built to capture the interaction patterns between the intersections and road segments. We conduct several experiments on two real-world datasets, and the results demonstrate that Auto-STDGCN is significantly superior to the state-of-art methods.