TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling

• URL: http://arxiv.org/abs/2401.03138v1
• Date: Sat, 6 Jan 2024 06:44:06 GMT
• Title: TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
• Authors: ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu
• Abstract summary: We present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. We propose a graph neural network that integrates multivariate, temporal, and spatial facets for improved accuracy.
• Score: 29.41878123692351
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. Our extensive analysis validates its potential for transportation. Focusing on vehicle-related GCT flow prediction, we propose a graph neural network that integrates multivariate, temporal, and spatial facets for improved accuracy. Experiments reveal our model's superiority over baselines, especially in long-term predictions. We also highlight the potential for GCT flow integration into transportation systems.

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