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 https://github.com/mayunyi-1999/TraffNet_code.git.

翻译：摘要：道路网络数字孪生（RNDTs）在下一代智能交通系统的发展中扮演着关键角色，能够实现更精准的交通规划与控制。为支持即时决策，RNDTs需要一种从在线传感器数据中动态学习交通模式并生成高保真仿真结果的模型。尽管当前基于图神经网络的交通预测技术已取得最优性能，但这些技术仅通过挖掘历史交通数据中的相关性来预测未来交通，忽略了交通需求与路线选择等交通生成的根本原因。因此，其性能在即时决策中不可靠。为填补这一空白，我们提出名为TraffNet的新型深度学习框架，从车辆轨迹数据中学习交通量的因果关系。首先，使用异构图表示道路网络，使模型能够整合交通量的因果特征；其次，受交通领域知识启发，提出交通因果关系学习方法，为每个路段学习编码出行需求与路径级依赖关系的嵌入向量；随后，对时间依赖性进行建模以匹配交通生成的内在过程；最后，实验验证了TraffNet的实用性。TraffNet的代码已开源：https://github.com/mayunyi-1999/TraffNet_code.git。