JSTC: Travel Time Prediction with a Joint Spatial-Temporal Correlation Mechanism
Bibliographic record
Abstract
Accurate travel time prediction is one of the most promising intelligent transportation system (ITS) services, which can greatly support route planning, ride-sharing, navigation applications, and effective traffic management. Several factors, like spatial, temporal, and external, have big effects on traffic patterns, and therefore, it is important to develop a mechanism that can jointly capture correlations of these components. However, spatial sparsity issues make travel time prediction very challenging, especially when dealing with the origin-destination (OD) method, since the trajectory data may not be available. In this paper, we introduce a unified deep learning-based framework named joint spatial-temporal correlation (JSTC) mechanism to improve the accuracy of OD travel time prediction. First, we design a spatiotemporal correlation block that combines two modules: self-convolutional attention integrated with a temporal convolutional network (TCN) to capture the spatial correlations along with the temporal dependencies. Then, we enhance our model performance through adopting a multi-head attention module to learn the attentional weights of the spatial, temporal, and external features based on their contributions to the output and speed up the training process. Extensive experiments on three large-scale real-world traffic datasets (NYC, Chengdu, and Xi’an) show the efficiency of our model and its superiority compared to other methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".