Travel Mode Choices for Connecting Urban Rail Transit System During Irregular Time Periods: A Case Study in Beijing
Bibliographic record
Abstract
The varying operating schedules of urban rail transit (URT) lines, combined with the distance between travelers’ origins and the URT stations, pose challenges for selecting their travel modes during irregular time periods such as early mornings and late evenings (EMLE). The choices during these special time periods may be influenced by personal attributes, travel attributes, environmental attributes, and psychological perceptions. We first conduct a questionnaire survey to explore travelers’ choice behaviors when they commute to or from URT stations, considering various influencing factors. After completing the statistical analysis, we then proceed with a preliminary assessment of the factors impacting travel mode preferences. Subsequently, a hybrid methodology that integrates structural equation modeling (SEM) and a random parameter logit model (RPLM) is introduced to investigate the impacts of factors. Notably, the interaction terms among travel time, cost, and psychological perception are considered as random variables. As a result, the heightened interaction between travel time and safety perception leads to a reduced probability of opting for walking or bike‐sharing as transportation modes. Similarly, there is a notable decrease in the probability of selecting a taxi when the interaction terms of travel cost and safety perception increase. The above results identify that travelers prefer to take safer and more convenient travel modes during the EMLE period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".