TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
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.
Cite
Text
Lin et al. "TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30331Markdown
[Lin et al. "TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/lin2024aaai-teltrans/) doi:10.1609/AAAI.V38I21.30331BibTeX
@inproceedings{lin2024aaai-teltrans,
title = {{TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling}},
author = {Lin, ChungYi and Tung, Shen-Lung and Su, Hung-Ting and Hsu, Winston H.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {22927-22933},
doi = {10.1609/AAAI.V38I21.30331},
url = {https://mlanthology.org/aaai/2024/lin2024aaai-teltrans/}
}