Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning

Abstract

Predicting urban traffic (e.g., flow, speed) is of great importance to intelligent transportation systems and public safety, yet is very challenging as it is affected by two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among different timestamps; 2) diversity of such spatio-temporal correlations, which vary from location to location and depend on the surrounding geographical information, e.g., points of interests and road networks. To tackle these challenges, we proposed a deep-meta-learning based traffic model, entitled ST-MetaNet, to collectively predict urban traffic in all location at once. ST-MetaNet employs a sequence-to-sequence network architecture, consisting of an encoder to learn historical traffic information and a decoder to make predictions step by step. More specifically, the encoder and decoder have the same network structure, which contains a recurrent neural network (RNN) to encode the urban traffic, a meta graph attention network (Meta-GAT) to capture diverse spatial correlations, and a meta recurrent neural network (Meta-RNN) to consider diverse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of ST-MetaNet against several state-of-the-art methods.