Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data
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Bibliographic record
Abstract
SUMMARY Literature review indicates that little is known about traveler behavior, such as transfer and route choices, in large transit systems because of the number of alternative routes involved and lack of empirical data. Even though many transit route assignment models have been developed and ample automated fare collection data have been collected, nearly no study has quantified how accurate resulting flow assignments are, especially for transfer flows. However, as a multi‐stakeholder system, it is essential to estimate passenger flows over the Beijing rail transit network for revenue sharing and daily management/operation purpose. In this paper, major factors (including total travel time and transfer cost) that influence passenger flow pattern in the Beijing rail transit network are considered in a logit‐based network flow assignment model. Specifically, a full transfer cost function, including transfer walking time, vehicle waiting time, and a penalty to additional transfers, is proposed to better simulate passengers' transfer behaviors. A generalized cost function for urban rail transit network is presented, and the corresponding route choice behavior of travelers is analyzed. An improved logit‐based model is then presented for solving this network flow assignment problem. The depth‐first method is used to search for “effective paths” among all O–D pairs. The average errors of estimated transfer flows from the proposed assignment model, which is proven to be more realistic in searching a set of effective paths, are below 20%. The results indicate that the models being developed in this study are capable of reasonably reproducing passengers' transfer and route choices and thus helpful for understanding the transfer behaviors of passengers of large rail transit networks. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 it