  Spatio-temporal graphs (ST-graphs) have been used to model time series tasks
such as traffic forecasting, human motion modeling, and action recognition. The
high-level structure and corresponding features from ST-graphs have led to
improved performance over traditional architectures. However, current methods
tend to be limited by simple features, despite the rich information provided by
the full graph structure, which leads to inefficiencies and suboptimal
performance in downstream tasks. We propose the use of features derived from
meta-paths, walks across different types of edges, in ST-graphs to improve the
performance of Structural Recurrent Neural Network. In this paper, we present
the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic
framework that can be applied to any spatio-temporal task in a simple and
scalable manner. We employ MESRNN for pedestrian trajectory prediction,
utilizing these meta-path based features to capture the relationships between
the trajectories of pedestrians at different points in time and space. We
compare our MESRNN against state-of-the-art ST-graph methods on standard
datasets to show the performance boost provided by meta-path information. The
proposed model consistently outperforms the baselines in trajectory prediction
over long time horizons by over 32\%, and produces more socially compliant
trajectories in dense crowds. For more information please refer to the project
website at https://sites.google.com/illinois.edu/mesrnn/home.
