Predicting Haul Truck Travel Times in Underground Mines
Bibliographic record
Abstract
Accurately predicting haul truck (HT) travel times (TT) in underground mines is essential for enhancing operational planning, as it allows planners to forecast extraction rates at each work face, minimize queue-related downtime, and ultimately increase productivity. However, in underground environments where GPS signals are unavailable, beacon-based locating systems have not yet been utilized for this predictive purpose. This study addresses that gap by introducing a machine learning approach for HT TT prediction that relies exclusively on beacon detection data, thus eliminating the need for traditional telemetry. The proposed method combines three route-segmentation strategies-full-route, short-segment, and major-segment predictions-with Gaussian mixture models, long short-term memory networks, and a stacking ensemble. Validated on two underground mines, it outperformed industry benchmarks, reducing prediction error by up to 34% on ascending routes and 18% on descending routes while achieving even greater precision for autonomous HTs. It showcases the untapped potential of beacon-based location systems for predictive applications, supporting mine planners.
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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 | 0.001 |
| 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".