Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning
  Approach
Xin Chen Mingliang Hou Tao Tang Achhardeep Kaur Feng Xia
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.