TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of
  Experts
Hyunwook Lee Sungahn Ko
Accurate traffic forecasting is challenging due to the complex dependency on
road networks, various types of roads, and the abrupt speed change due to the
events. Recent works mainly focus on dynamic spatial modeling with adaptive
graph embedding or graph attention having less consideration for temporal
characteristics and in-situ modeling. In this paper, we propose a novel deep
learning model named TESTAM, which individually models recurring and
non-recurring traffic patterns by a mixture-of-experts model with three experts
on temporal modeling, spatio-temporal modeling with static graph, and dynamic
spatio-temporal dependency modeling with dynamic graph. By introducing
different experts and properly routing them, TESTAM could better model various
circumstances, including spatially isolated nodes, highly related nodes, and
recurring and non-recurring events. For the proper routing, we reformulate a
gating problem into a classification problem with pseudo labels. Experimental
results on three public traffic network datasets, METR-LA, PEMS-BAY, and
EXPY-TKY, demonstrate that TESTAM achieves a better indication and modeling of
recurring and non-recurring traffic. We published the official code at
https://github.com/HyunWookL/TESTAM