HSETA: A Heterogeneous and Sparse Data Learning Hybrid Framework for Estimating Time of Arrival
Bibliographic record
Abstract
The estimated time of arrival (ETA) plays a vital role in intelligent transportation systems and has been widely used as a basic service in ride-hailing platforms. Obtaining a precise ETA is a challenging task due to the complexity of the real-world geographic and traffic environments. Previous works suffer from heterogeneous sparse data learning and multiple-correlation extraction issues. Therefore, this paper presents a hybrid deep learning framework (HSETA) to estimate the vehicle travel time from massive data. First, we encode heterogeneous data to represent various features in different respects. Then, we develop an ensemble factorization machine block (EFMB) structure combined with gated recurrent unit (GRU) and multilayer perceptron (MLP) to extract information from sparse and dense features. Next, the multiple-correlation learning block (MCLB) structure that we propose is utilized to aggregate information based on multiple correlations. Finally, the travel time can be estimated by simple regression. Our extensive evaluations on two real-world datasets show that HSETA significantly outperforms all baselines. Our PyTorch implementation of HSETA and sample data are available athttps://github.com/LouisChenki/HSETA
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".