Modeling Travelers' Responses to Incident Information Provided by Variable Message Signs in Calgary, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an investigation of drivers' response behaviors to intelligent transportation systems. It describes the results of a detailed survey and the results of an econometric model of route diversion behavior in response to real-time information provided by variable message signs (VMSs). The study location was Deerfoot Trail in Calgary, Canada. In case of major delays because of accidents on Deerfoot Trail, the City of Calgary uses 12 VMSs along Deerfoot Trail to divert drivers to alternative parallel arterials. A survey of 500 Deerfoot Trail commuters was conducted to examine the factors affecting drivers' compliance with VMSs. A latent discrete choice model was developed to model the responses of drivers to VMSs. This model introduces behavioral variables within a discrete choice model by endogenously estimating the latent variables. The primary finding of the study is that the en route information provided by VMSs convinces few drivers to change their trip destinations. Of the 500 respondents, 63.3% of drivers alter their trip plans in light of the information provided. However, 36.7% of drivers experience inertia by not altering their route, despite the excessive delays because of route blockage. The empirical model shows that driving experience, familiarity with alternative routes, trip purpose, trip time, trip length, and complementary information sources (e.g., the radio) are the most important factors influencing route-switching behavior in response to VMSs. In addition, drivers' attitudes toward VMSs were found to have the most significant impact on their responses to these systems.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it