Sparse Mobile Crowdsensing for Cost-Effective Traffic State Estimation With Spatio–Temporal Transformer Graph Neural Network
Bibliographic record
Abstract
Recently, mobile crowdsensing (MCS) has emerged as a promising solution for traffic state estimation (TSE), which provides real-time and accurate traffic information for supporting diversified intelligent transportation systems (ITS) applications. However, the prohibitive overhead of collecting massive data in vehicular networks limits the available data amount, while the sparsification of MCS data incurs instability and degrades TSE accuracy. To this end, this paper proposes a novel sparse MCS framework to facilitate cost-effective TSE, which utilizes a small number of vehicular MCS participants distributed across all regions as data sources. By utilizing spatial and temporal correlations of traffic flow, an innovative spatiotemporal deep learning model, namely Transformer Graph Attentional Sample and Aggregate neural network (TGASA), is proposed to improve the TSE accuracy with sparse MCS data. Specifically, we design an incorporated graph neural network (GNN) to aggregate the spatial correlation by taking both node features and edge properties into account. And, the transformer neural network architecture is applied to capture the temporal correlation. Extensive simulation results based on real-world datasets demonstrate that the proposed framework can significantly address the instability incurred by the sparsification of MCS data and effectively achieve a more accurate TSE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".