TraffNet: Learning Causality of Traffic Generation for Road Network Digital Twins

Road network digital twins (RNDTs) play a critical role in the development of next-generation intelligent transportation systems, enabling more precise traffic planning and control. To support just-in-time (JIT) decision making, RNDTs require a model that dynamically learns the traffic patterns from online sensor data and generates high-fidelity simulation results. Although current traffic prediction techniques based on graph neural networks have achieved state-of-the-art performance, these techniques only predict future traffic by mining correlations in historical traffic data, disregarding the causes of traffic generation, such as Origin-Destination (OD) demands and route selection. Therefore, their performance is unreliable for JIT decision making. To fill this gap, we introduce a novel deep learning framework called TraffNet that learns the causality of traffic volumes from vehicle trajectory data. First, we use a heterogeneous graph to represent the road network, allowing the model to incorporate causal features of traffic volumes. Next, inspired by the traffic domain knowledge, we propose a traffic causality learning method to learn an embedding vector that encodes OD demands and path-level dependencies for each road segment. Then, we model temporal dependencies to match the underlying process of traffic generation. Finally, the experiments verify the utility of TraffNet. The code of TraffNet is available at https://github.com/mayunyi-1999/TraffNet_code.git.