Development of a transfer‐cost‐based logit assignment model for the Beijing rail transit network using automated fare collection data
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".