Origin-Destination Estimation plays an important role in traffic management and traffic simulation in the era of Intelligent Transportation System (ITS). Nevertheless, previous model-based methods face the under-determined challenge, thus desperate demand for additional assumptions and extra data exists. Deep learning provides an ideal data-based method for connecting inputs and outputs by probabilistic distribution transformation. While relevant researches of applying deep learning into OD estimation are limited due to the challenges lying in data transformation across representation space, especially from dynamic spatial-temporal space to heterogeneous graph in this issue. To address it, we propose Cyclic Graph Attentive Matching Encoder (C-GAME) based on a novel Graph Matcher with double-layer attention mechanism. It realizes effective information exchange and establishes coupling relationship across underlying feature space. The proposed model achieves state-of-the-art results in experiments, and offers a novel framework for inference task across spaces in prospective employments.
翻译:在智能运输系统(ITS)时代,基于模型的以往方法在交通管理和交通模拟方面起着重要作用。然而,以前的方法面临着不确定的挑战,因此对额外假设和额外数据的需求非常迫切。深层次的学习提供了一种理想的数据方法,通过概率分布转换将投入和产出连接起来。在将深层次学习应用于OD估计方面的相关研究有限,因为在整个代表空间的数据转换中存在挑战,特别是从动态空间-时空空间到这个问题的多元图。为了解决这个问题,我们提议以具有双重关注机制的新图匹配器为基础,采用C-GAME(C-GAM),实现有效的信息交流,并在基础地貌空间之间建立连接关系。拟议的模型在实验中取得了最新的结果,并为未来就业中跨空间的推断任务提供了一个新的框架。