Inference of Transit Alighting From Automatic Boarding Count Data: A Double DQN Clustering
Bibliographic record
Abstract
The availability of both boarding and alighting counts is crucial for transit planning, as it allows for the estimation of Origin-Destination patterns that reflect demand. However, automatic data collection methods often provide only boarding counts. This paper investigates the use of transit symmetry to address the lack of alighting counts by analyzing cluster-level symmetry, which captures the dynamics of trip exchanges between nearby stops to achieve balanced ingoing and outgoing flows. An optimization problem is formulated to maximize symmetry across clusters, evaluating the effectiveness of clustering based on stop location using k-means. A novel deep reinforcement learning algorithm utilizing the Double Deep Q-Network framework is proposed to identify optimal clusters, demonstrating an 8% improvement in symmetry optimization over k-means. Notably, k-means clusters still achieve an average symmetry level of 62% when accounting for geographical proximity and walking distance limits. Through the proposed clusters, transit planners can better understand passenger flow dynamics and predict alighting counts with incomplete data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".