  Traffic state forecasting is crucial for traffic management and control
strategies, as well as user- and system-level decision making in the
transportation network. While traffic forecasting has been approached with a
variety of techniques over the last couple of decades, most approaches simply
rely on endogenous traffic variables for state prediction, despite the evidence
that exogenous factors can significantly impact traffic conditions. This paper
proposes a multi-dimensional spatio-temporal graph attention-based traffic
prediction approach (M-STGAT), which predicts traffic based on past
observations of speed, along with lane closure events, temperature, and
visibility across the transportation network. The approach is based on a graph
attention network architecture, which also learns based on the structure of the
transportation network on which these variables are observed. Numerical
experiments are performed using traffic speed and lane closure data from the
California Department of Transportation (Caltrans) Performance Measurement
System (PeMS). The corresponding weather data were downloaded from the National
Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing
Systems (ASOS). For comparison, the numerical experiments implement three
alternative models which do not allow for the multi-dimensional input. The
M-STGAT is shown to outperform the three alternative models, when performing
tests using our primary data set for prediction with a 30-, 45-, and 60-minute
prediction horizon, in terms of three error measures: Mean Absolute Error
(MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
However, the model's transferability can vary for different transfer data sets
and this aspect may require further investigation.
