A Comparative Study of Passenger Multitasking Activities on Commuting and Leisure Electrified Intercity Railways
Bibliographic record
Abstract
The electrification of intercity railways plays a significant role in energy conservation and emission reduction. Research on passenger travel activities to optimize vehicle services can help to understand how best to improve passenger attraction and the use of intercity railways. In this study, we conducted observational research on two electrified intercity railways that were segregated by the attributes, leisure, and commute. The purpose was to determine the influence of line attribute, passenger gender, age, and seat availability on the types of activities performed onboard, with specific attention placed on the use of information and communication technology (ICT). Using structured observations, the travel multitasking activity data of 467 passengers were collected on two intercity railways in real-life situations. Using the chi-square test and binary logistic regression analysis, it was found that line attribute, gender, age, and seat availability have an impact on passenger activities. Differences in factors affecting passenger activities were also found according to the nature of their travel, whether for commute or leisure. Our results suggest that passengers on the leisure line prefer to engage in some social activities. For example, the probability of conversation among passengers on the leisure line was 3.47 times that of the commuting line, and the middle-aged and elderly travelers on this line were more likely to be in a daze and look around. The probability of taking a break for passengers on the commuting line was 3.625 times that of the leisure line, and passengers who were not seated on this line were found to be more likely to be idle. In addition, male travelers and young travelers preferred to engage in ICT immersive activities, such as using mobile phones, while women, middle-aged, and elderly travelers were more likely to engage in non-ICT immersive activities. Seated passengers were more likely to engage in simultaneous multitasking activities, rest, and conversations than passengers without seats.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".