Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach

TL;DR

This paper introduces a novel spatio-temporal graph neural network framework that leverages telecom data to accurately predict directional urban mobility flows, addressing limitations of traditional sensor-based traffic prediction.

Contribution

The paper presents the TeltoMob dataset and a two-stage STGNN framework that integrates telecom counts with directional and geographic data for improved mobility flow prediction.

Findings

Framework is compatible with various STGNN models

Demonstrates improved prediction accuracy

Can be integrated into real-world transportation systems

Abstract

Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows, to predict directional mobility flows on roadways. To address this, we propose a two-stage spatio-temporal graph neural network (STGNN) framework. The first stage uses a pre-trained STGNN to process telecom data, while the second stage integrates directional and geographic insights for accurate prediction. Our experiments demonstrate the framework's compatibility with various STGNN models and confirm its effectiveness. We also show how to incorporate the framework into real-world transportation systems, enhancing sustainable urban mobility.