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TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
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Introduces geographical cellular traffic (GCT) flows from a telecom operator and a multifaceted graph model (MFGM) that uses channel-specific graph attention to predict vehicle-related GCT flow across multivariate, temporal, and spatial facets.
Problem
Traffic prediction from location-bound sensors is costly and has limited coverage; the paper asks whether telecom cellular traffic flows can predict vehicle-related traffic on road segments over time.
Approach
Defines multi-type GCT flows (vehicle, pedestrian, stationary) and models vehicle flow with subtracted-type flows (V-P, V-S) to reveal regional functionality, plus a channel-specific GAT layer that applies independent graph attention per CNN channel, with short/long temporal kernels and bidirectional spatial graphs.
Key results
- MFGM achieves MAE 5.23, RMSE 8.27, MAPE 29.8% at 15 min, outperforming the best baseline ESG (5.26, 8.43, 31.1%).
- At 60 min, MFGM reaches MAE 5.51 and RMSE 9.10 versus ESG's 5.65 and 9.46, with gains largest in long-term horizons.
- Ablation on the full dataset: removing spatial facet raises MAE from 5.34 to 5.46; removing multivariate facet gives 5.44; removing temporal gives 5.40.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add Spatial Facet Modeling (ablation) | MFGM · V-GCT prediction, steps 1–12 average | MAE: 5.46 -> 5.34 | Helped |
| add Multivariate Facet Modeling (ablation) | MFGM · V-GCT prediction, steps 1–12 average | MAE: 5.44 -> 5.34 | Helped |
| add Temporal Facet Modeling (ablation) | MFGM · V-GCT prediction, steps 1–12 average | MAE: 5.40 -> 5.34 | Helped |
| use V-GCT with all subtraction-type GCT flows instead of only V-GCT and (V-P)-GCT or (V-S)-GCT | MFGM · V-GCT prediction, steps 1–12 | prediction error | Helped |
Surprising findings
- Pearson correlation between V-GCT and subtracted flows (V-P, V-S) differs far more across region types than correlations between raw flow types, suggesting subtracted flows reveal regional attributes.
Limitations
- Proof-of-concept limited to 21 road segments in one city (Hsinchu) over one month.
- Data is proprietary to a single telecom company, with privacy-driven restrictions on collected fields.
Experimental setup
Datasets: Hsinchu GCT flow data from a Taiwanese telecom operator (21 road segments)
Benchmarks: Hsinchu GCT flow V-GCT prediction dataset
Metrics: MAE RMSE MAPE
Baselines: TCN GWNet Gman MTGNN MPNet DMGCN ESG
Builds on: graph attention network WaveNet-style gated temporal convolution Graph WaveNet MTGNN
Closest papers by meaning
- Efficient Traffic Prediction Through Spatio-Temporal Distillation (AAAI 2025 · similarity 0.87)
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- Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting (AAAI 2019 · similarity 0.86)
- Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting (AAAI 2024 · similarity 0.86)
- Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective (AAAI 2024 · similarity 0.85)
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