Modeling Riders’ Behavioral Responses to Real-Time Information at Light Rail Transit Stations
Bibliographic record
Abstract
An advanced passenger information system (APIS) can play a significant role in improving the satisfaction of transit riders in the short term and increasing ridership in the long term. This research focuses on investigating riders’ behavioral responses to en route real-time information on light rail transit (LRT). A survey was designed and conducted to collect LRT riders’ behavioral responses by presenting hypothetical scenarios in Calgary, Alberta, Canada. Two scenarios were examined: an estimated arrival time of 10 min for the next LRT and an LRT service interruption attributable to an incident or weather with no information on expected recovery time. The survey collected 505 responses. Four multinomial logit models were developed and calibrated to explore the factors affecting trip decision making for the described scenarios for commuter and noncommuter trips. The results led to the conclusion that various socioeconomic attributes (e.g., age, gender, and number of autos per household), experience with an APIS (familiarity with APIS and perceived accuracy of APIS), and experience with transit and the LRT system (use of transit as the primary mode of transportation, frequency of LRT use, and familiarity with LRT) had strong influences on travelers’ behavioral responses in the context of real-time LRT information. Analysis of the data also determined that travelers’ actions varied by trip purposes, travel time, and weather conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".