PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning
Bibliographic record
Abstract
Sophisticated location and tracking technologies have led to the generation of vast amounts of trajectory data. Of interest is constructing a small set of basic building blocks that can represent a wide range of trajectories, known as a trajectory pathlet dictionary. This dictionary can be useful in various tasks and applications, such as trajectory compression, travel time estimation, route planning, and navigation services. Existing methods for constructing a pathlet dictionary use a top-down approach, which generates a large set of candidate pathlets and selects the most popular ones to form the dictionary. However, this approach is memory-intensive and leads to redundant storage due to the assumption that pathlets can overlap. To address these limitations, we propose a bottom-up approach for constructing a pathlet dictionary that significantly reduces memory storage needs of baseline methods by multiple orders of magnitude (by up to ~24K× better). The key idea is to initialize unit-length pathlets and iteratively merge them, while maximizing utility. The utility is defined using newly introduced metrics of trajectory loss and representability. A deep reinforcement learning method is proposed, PathletRL, that uses Deep Q Networks (Dqn) to approximate the utility function. Experiments show that our method outperforms the current state-of-the-art, both on synthetic and real-world data. Our method can reduce the size of the constructed dictionary by up to 65.8% compared to other methods. It is also shown that only half of the pathlets in the dictionary is needed to reconstruct 85% of the original trajectory data.
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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 | 0.001 |
| 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".