  We present a novel approach for traffic forecasting in urban traffic
scenarios using a combination of spectral graph analysis and deep learning. We
predict both the low-level information (future trajectories) as well as the
high-level information (road-agent behavior) from the extracted trajectory of
each road-agent. Our formulation represents the proximity between the road
agents using a weighted dynamic geometric graph (DGG). We use a two-stream
graph-LSTM network to perform traffic forecasting using these weighted DGGs.
The first stream predicts the spatial coordinates of road-agents, while the
second stream predicts whether a road-agent is going to exhibit overspeeding,
underspeeding, or neutral behavior by modeling spatial interactions between
road-agents. Additionally, we propose a new regularization algorithm based on
spectral clustering to reduce the error margin in long-term prediction (3-5
seconds) and improve the accuracy of the predicted trajectories. Moreover, we
prove a theoretical upper bound on the regularized prediction error. We
evaluate our approach on the Argoverse, Lyft, Apolloscape, and NGSIM datasets
and highlight the benefits over prior trajectory prediction methods. In
practice, our approach reduces the average prediction error by approximately
75% over prior algorithms and achieves a weighted average accuracy of 91.2% for
behavior prediction. Additionally, our spectral regularization improves
long-term prediction by up to 70%.
