Urban Region Embeddings from Service-Specific Mobile Traffic Data
This work addresses urban research by providing a method to analyze urban dynamics using mobile data, but it is incremental as it builds on existing embedding techniques with new data types.
The paper tackled the problem of generating high-quality representations of urban regions by leveraging service-specific mobile traffic data, resulting in embeddings that effectively capture urban characteristics and outperform a state-of-the-art competitor in downstream tasks.
With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present a methodology for creating urban region embeddings from service-specific mobile traffic data, employing a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to capture key urban features. In the extensive experimental evaluation conducted using a real-world dataset, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. Specifically, our embeddings are compared against those of a state-of-the-art competitor across two downstream tasks. Additionally, through clustering techniques, we investigate how well the embeddings produced by our methodology capture the temporal dynamics and characteristics of the underlying urban regions. Overall, this work highlights the potential of service-specific mobile traffic data for urban research and emphasizes the importance of making such data accessible to support public innovation.