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 traffic 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 volume 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, motivated by the traffic
domain knowledge, we propose a traffic causality learning method to learn an
embedding vector that encodes travel 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
this https URL.

使用 TraffNet 这一深度学习框架，通过车辆轨迹数据学习交通流量的因果关系，支持基于时机的决策制定并验证其有效性。