TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridors

TGDT takes input parameters (highlighted in orange), including ingress aggregated traffic waveforms, signal timing parameters (e.g., cycle length, offset, and maximum green duration for each phase), driving behavior parameters (e.g., speed, acceleration, space cushion, lane-changing behavior), turning movement ratios, and the distances between intersections along the major corridor. It simultaneously generates multiple outputs (highlighted in blue), such as westbound travel times along the corridor, queue lengths for each lane group phase, and average waiting times for each lane group phase. The time intervals of the output time series match those of the input inflow waveforms.Figure 2: Overview of TGDT framework. This diagram illustrates the architecture of our proposed Digital Twin for urban corridors, which consists of three main modules. Simulation records, extracted from the logs of a microscopic traffic simulator, are transformed into graph-structured data that uniquely represent the corridor's traffic state for each scenario. The inflow module ( M inf) performs a graph imputation task to reconstruct 2D traffic volumes on every intermediate road segment. The travel time module ( M tt) carries out a graph regression task to estimate bidirectional corridor-level travel time series. Finally, the queue length ( M ql) and waiting time ( M wt) modules apply temporal convolution and deconvolution operations on the spatiotemporal representations learned by M tt, producing 3D outputs for maximum queue length and waiting time. These estimates are generated at the intersection and phase levels for each lane group associated with a specific movement phase. is then passed through additional layers that combine con-volutional and transposed convolutional operations for multi-channel temporal feature learning from 1D multivariate inputs.