UCMM: Unsupervised Convolutional Networks for Accurate and Efficient Map Matching with Mobile Cellular Data

The map matching of cellular data reconstructs real trajectories of users by exploiting the sequential connections between mobile devices and cell towers. The difficulty in obtaining paired cellular-GPS data and the cellular variation compromise the accuracy and reliability of existing map matching approaches. In this paper, we propose a novel unsupervised convolutional network for cellular map matching (UCMM) to address these challenges. UCMM employs a dual encoder-decoder network to capture a shared representation from both the cellular and GPS domains in an unsupervised manner. It leverages a dedicated convolutional architecture to tackle the varying lengths of output sequential data. An attention mechanism is specially introduced to deal with the cellular variation.
The effectiveness of UCMM is demonstrated through comprehensive evaluations, which show that UCMM achieves a substantial improvement in matching accuracy and deduction of training time compared with the best-known prior works. These improvements make UCMM a significant advancement in the field of map matching.
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