Transit Users’ Mode Choice Behavior During Light Rail Transit Short-Term Planned Service Disruption
Bibliographic record
Abstract
Planned service disruptions (PSDs) of light rail transit (LRT) improve service reliability, extend infrastructure’s life, and reduce the frequency and impact of unplanned service disruption caused by system failure. However, the literature on the impact of LRT PSDs on transit customers’ travel mode choice behavior is scarce relative to that on unplanned service disruptions. This study aimed to investigate transit customers’ mode choice behavior in response to short-term LRT PSD in the City of Calgary, AB, Canada. A stated preference survey was designed to gather respondents’ mode choices under a set of hypothetical scenarios. A mixed multinomial logit model was estimated using stated preference data. Findings of this study include: (i) stated LRT ridership dropped by about 35% during the examined short-term LRT service disruption; (ii) transit customers who hold a LRT payment pass (monthly, subsidized seniors, low income, and students) and are frequent weekend LRT users are more likely to stay with the LRT mode in case of short-term PSD; (iii) the value of time for transit users during short-term LRT PSD was found to be 11.76 $/h and 13.0 $/h for travel time (excluding wait time) and wait time during travel, respectively. A sensitivity analysis was conducted on key variables to predict choice probabilities of transit alternatives. Recommendations are made to improve Calgary Transit customers’ experience during short-term LRT PSDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".