Modelling traveller response to variable message sign
Bibliographic record
Abstract
Rapid traffic growth in Calgary has put increased pressure in city's roadway network, especially on Deerfoot Trail which provides a fast access to north–south potion of the city. The city disseminates traffic information in Deerfoot through variable message sign (VMS) in case of major delays and usually diverts traffic to alternate parallel arterials. However, the reroute tendency of travellers significantly affects the diversion pattern at the VMS locations and this research targets the frequent users of Deerfoot Trail and investigates their rerouting tendency in light of important trip attributes drivers might associate with their trips. A total of 471 responses are collected from frequent users of Deerfoot Trail through a questionnaire survey and the analysis is focused on developing a relationship between reroute tendency and a set of explanatory variables, using generalized ordered logit (GOLOGIT) model and partially generalized regression models. The results have led to the conclusion that in addition to various socio-economic attributes, network familiarity and information access characteristics; trip characteristics also possess significant influences in rerouting decision-making. The study also proposes several implications for better design and operation of advanced traveller information systems and future effort on model estimation in light of the finding of the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".