Modeling Commuters’ Response to Pre-Trip Information Provided for Prolonged and Large-Scale Network Disruptions: Case study of West LRT Construction in the City Calgary, Canada
Bibliographic record
Abstract
This paper models commuters’ response to pre-trip information disseminated through electronic Newsletters, which are advising route diversions and mode change due to a major Light Rail Trasit (LRT) Construction in the City of Calgary, Canada. The West LRT line is a major construction affecting daily commute around an urbanized area. The construction lasts three years and during this time roads and lane closures take place in the vicinity of the construction zones. Data on commuters' route making decisions were obtained by conducting a survey on a sample of users of the main affected roads. Two discrete choice models were calibrated for this purpose. The first model examines commuters' response to traffic information disseminated through Newsletters, and the second model investigates the reason behind the low response rate of commuters. In these models the effects of socio-economic characteristics, congestion level, trip characteristics, weather conditions, frequency of driving in the vicinity of the LRT construction zone, familiarity with alternative routes, route characteristics and access to other sources of traffic alerts are examined. Although 13% of commuters are likely to make no changes in their routes and trips, 46% of the respondents stated that they would make pronounced changes in trip planning by either changing modes, departure time or destination; 41% stated to change their route. The attitude and perception towards the quality of information provided by the Newsletters were found to be critical contributing factors affecting the travelers’ responses to these systems. Respondents stated that the perceived unreliability of Newsletter information and the expected similarity in travel time on alternate route are major reasons behind the low compliance rate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".