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

TLDR

Urban traffic prediction is critical for intelligent transportation systems but is hindered by complex, location‑dependent spatio‑temporal correlations. The authors introduce ST‑MetaNet, a deep‑meta‑learning model designed to predict traffic across all locations simultaneously. ST‑MetaNet employs a sequence‑to‑sequence architecture with encoder‑decoder networks that share a recurrent neural network, a meta graph attention network for spatial dependencies, and a meta recurrent neural network for temporal dynamics. Experiments on two real‑world datasets demonstrate that ST‑MetaNet outperforms several state‑of‑the‑art baselines.

Abstract

Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among 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 model, entitled ST-MetaNet, to collectively predict traffic in all location at once. ST-MetaNet employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step. In specific, the encoder and decoder have the same network structure, consisting of a recurrent neural network to encode the traffic, a meta graph attention network to capture diverse spatial correlations, and a meta recurrent neural network to consider diverse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of ST-MetaNet beyond several state-of-the-art methods.