Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free Traffic via a Spatio-Temporal Framework (2403.12991v1)
Abstract: Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activities, or cellular traffic, on roadways as a proxy for vehicle flow. However, as counts of cellular traffic may not directly align with vehicle flow due to data from various user types, we present a new task: predicting vehicle flow in camera-free areas using cellular traffic. To uncover correlations within multi-source data, we deployed cameras on selected roadways to establish the Tel2Veh dataset, consisting of extensive cellular traffic and sparse vehicle flows. Addressing this challenge, we propose a framework that independently extracts features and integrates them with a graph neural network (GNN)-based fusion to discern disparities, thereby enabling the prediction of unseen vehicle flows using cellular traffic. This work advances the use of telecom data in transportation and pioneers the fusion of telecom and vision-based data, offering solutions for traffic management.
- BoT-SORT: Robust associations multi-pedestrian tracking. arXiv preprint arXiv:2206.14651 (2022).
- A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Scientific data (2015).
- How Attentive are Graph Attention Networks?. In International Conference on Learning Representations.
- Weiwei Jiang. 2022. Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications (2022), 117163.
- Pay Attention to Multi-Channel for Improving Graph Neural Networks. In Proc. of ICLR.
- TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling. arXiv preprint arXiv:2401.03138 (2024). Accepted by AAAI 2024. To appear.
- Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic. In Proc. of CIKM.
- Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems (2021).
- Philip Sedgwick. 2012. Pearson’s correlation coefficient. Bmj 345 (2012).
- Spatio-temporal analysis and prediction of cellular traffic in metropolis. IEEE Transactions on Mobile Computing (2018).
- Graph WaveNet for Deep Spatial-Temporal Graph Modeling.. In Proc. of IJCAI.
- Urban flow prediction from spatiotemporal data. Information Fusion (2020).
- Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting. In Proc. of KDD. 2296–2306.
- Is dynamic traffic sensor network profitable for network-level real-time information prediction? Transportation Research Part C: Emerging Technologies 102 (2019), 32–59.
- Multivariate time-series anomaly detection via graph attention network. In Proc. of ICDM. 841–850.
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