Reproducing Longitudinal In-Vehicle Traveler Experience and the Impact of a Service Reduction with Public Transit Smart Card Data
Bibliographic record
Abstract
In-vehicle traveler experience is an influencing factor in the mode choice and satisfaction of public transit users. Vehicle load is an operator-centric indicator used as a level-of-service standard. This indicator considers crowding an isolated and deterministic event generalizable to all travelers. It has been argued that this indicator does not reflect the actual perception from the user perspective. This paper proposes a more comprehensive approach to measure in-vehicle experience by introducing an individual-based indicator that encompasses multiple days. The rationale is that the traveler’s perception is not determined by a single trip but by multiple events over time within a specific trip pattern. Multiday public transit smart card transaction data from an express bus route were used to demonstrate the concept. First, a data processing technique was developed to enrich the data with operations and vehicle capacity information. These data were used to reproduce the longitudinal in-vehicle experience of each traveler. The individual results were then summarized and analyzed according to trip and user attributes. Noticeable differences in the in-vehicle experience were found between fare groups and also were found associated with trip direction. Through the analysis of data before and after an actual service reduction, it was revealed that the level of impact on individual in-vehicle experience also varied according to fare group and trip direction. The traveler-based indicators complement traditional measures and could be integrated into operational planning, such as service reduction and increase studies, to anticipate the impact on traveler experience and customer satisfaction.
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 | 0.004 |
| 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".