The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike‐Sharing
Bibliographic record
Abstract
Bike‐sharing has a significant impact on commuters’ rational planning of their travel times, which can lead to an advance or delay in the peak passenger flow of the subway system during the morning peak. To explore the impact of bike‐sharing on subway commuters’ choices of departure times, we developed a departure time choice model considering the effect of bike‐sharing. This model considers both constant and linear marginal‐activity utility and compares it with traditional departure time choice models. Research indicates that within the timeframe that ensures on‐time arrival at work, models not accounting for bike‐sharing services underestimate both the departure rate and the total number of commuters compared to actual figures. Specifically, under the constant marginal‐activity utility, about 6.76% of commuters actually choose to depart earlier, while under the linear marginal‐activity utility, this figure is 6.91%. Conversely, during the departure timeframes that lead to late arrival at work, the traditional model overestimates both the departure rate and total number of commuters. Finally, through case analysis, we further revealed the dynamic relationship between commuter departure rates, commuting fatigue, and number of bike‐sharing and calculated the actual commuting costs for different proportions of bike‐sharing. The results indicate that when the number of bike‐sharing reaches 30% of the commuting demand, it can maximally reduce the commuting costs for commuters by approximately 23.32%. These findings offer a crucial basis for optimizing management strategies for morning peak subway commuting.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".