With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level of complexity and the huge amount of data, mobility profiling faces huge challenges. Digital Twin (DT) technology paves the way for cost-effective and performance-optimised management by digitally creating a virtual representation of the network to simulate its behaviour. In order to capture the complex spatio-temporal features in traffic scenario, we construct alignment diagrams to assist in completing the spatio-temporal correlation representation and design dilated alignment convolution network (DACN) to learn the fine-grained correlations, i.e., spatio-temporal interactions. We propose a digital twin mobility profiling (DTMP) framework to learn node profiles on a mobility network DT model. Extensive experiments have been conducted upon three real-world datasets. Experimental results demonstrate the effectiveness of DTMP.

翻译：随着大数据时代的到来，移动剖面分析已成为利用海量移动数据构建智能交通系统的可行方法。该方法能从移动数据中提取城市交通的潜在模式，对各类交通相关应用至关重要。然而，由于高度的复杂性和庞大的数据量，移动剖面分析面临着巨大挑战。数字孪生技术通过数字化创建网络虚拟表征来模拟其行为，为实现成本效益高且性能优化的管理铺平了道路。为捕捉交通场景中复杂的时空特征，我们构建了对齐图以辅助完成时空相关性表征，并设计了扩张对齐卷积网络来学习细粒度相关性，即时空交互。我们提出了一种数字孪生移动剖面分析框架，用于在移动网络DT模型上学习节点剖面。基于三个真实数据集开展了大量实验，实验结果验证了DTMP的有效性。