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

• URL: http://arxiv.org/abs/2405.17507v1
• Date: Sun, 26 May 2024 17:14:50 GMT
• Title: Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach
• Authors: ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu,
• Abstract summary: We present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows on roadways. We propose a two-stage neural network framework (STGNN) to process telecom data and integrate directional and geographic insights. We show how to incorporate the framework into real-world transportation systems enhancing sustainable urban mobility.
• Score: 26.92971702938603
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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