Urban Region Embeddings from Service-Specific Mobile Traffic Data

TL;DR

This paper introduces a novel methodology for creating high-quality urban region embeddings from service-specific mobile traffic data, utilizing advanced neural network models to capture urban features and dynamics.

Contribution

It presents a new approach combining temporal convolutional networks, transformers, and learnable models to generate urban embeddings from detailed mobile traffic data, demonstrating effectiveness in real-world tasks.

Findings

Embeddings effectively capture urban characteristics.

Method outperforms state-of-the-art in downstream tasks.

Clustering reveals temporal dynamics of urban regions.

Abstract

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.