Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Abstract

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 affect traffic conditions. This paper proposes a multidimensional spatiotemporal 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 a large transportation network. The approach is based on a graph attention network architecture, which 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 Caltrans Performance Measurement System (PeMS) and corresponding weather data from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). The numerical experiments implement three alternative models which do not allow for multidimensional input, along with two alternative multidimensional models, based on the literature. The M-STGAT outperforms the five alternative models in validation and testing with the primary data set, as well as for one transfer data set across all three prediction horizons for all error measures. However, the model’s transferability varies for the remaining two transfer data sets, which may require further investigation. The results demonstrate that M-STGAT has the most consistently low error values across all transfer data sets and prediction horizons.