  Traffic congestion has significant economic, environmental, and social
ramifications. Intersection traffic flow dynamics are influenced by numerous
factors. While microscopic traffic simulators are valuable tools, they are
computationally intensive and challenging to calibrate. Moreover, existing
machine-learning approaches struggle to provide lane-specific waveforms or
adapt to intersection topology and traffic patterns. In this study, we propose
two efficient and accurate "Digital Twin" models for intersections, leveraging
Graph Attention Neural Networks (GAT). These attentional graph auto-encoder
digital twins capture temporal, spatial, and contextual aspects of traffic
within intersections, incorporating various influential factors such as
high-resolution loop detector waveforms, signal state records, driving
behaviors, and turning-movement counts. Trained on diverse counterfactual
scenarios across multiple intersections, our models generalize well, enabling
the estimation of detailed traffic waveforms for any intersection approach and
exit lanes. Multi-scale error metrics demonstrate that our models perform
comparably to microsimulations. The primary application of our study lies in
traffic signal optimization, a pivotal area in transportation systems research.
These lightweight digital twins can seamlessly integrate into corridor and
network signal timing optimization frameworks. Furthermore, our study's
applications extend to lane reconfiguration, driving behavior analysis, and
facilitating informed decisions regarding intersection safety and efficiency
enhancements. A promising avenue for future research involves extending this
approach to urban freeway corridors and integrating it with measures of
effectiveness metrics.
