Effective Trajectory Imputation using Simple Probabilistic Language Models
Bibliographic record
Abstract
Trajectory data collected by GPS has found many critical applications. Unfortunately, most trajectory datasets have missing data due to technical problems or due to the sampling strategy used. Trajectory imputation is the task of filling in the gaps in actual trajectories by computing points that fit "naturally" within existing trajectories. Considering that both trajectories and natural language are essentially sequences of symbols, we explore the use of probabilistic language models for trajectory imputation. Using a grid-based representation of the space, and not considering the underlying road network, we convert trajectory points into tokens corresponding to the grid cell where they appear and train models of different sizes. We report experiments on a real dataset of over 500,000 taxi trips, showing that we can accurately fill gaps of up to 2km between GPS observations with 83% precision. These results are comparable to approaches using much more computationally demanding Large Language Models based on transformers. We discuss why transformers are overkill for the task through experiments that show that trajectory data does not exhibit very long dependencies, as is the case with natural language.
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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 | 0.001 | 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) | 0.001 | 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 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".