Examining the Relationship between Drivers’ Anticipated Travel Time and Previous Experienced Travel Times
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
Travel time reliability refers to the day-to-day variability of trip travel times. There is a belief that there is a cost associated with unreliability and this cost can be quantified as a function of the difference between the travel time that was experienced and the travel time that was anticipated. In the realm of public transport systems, the anticipated travel time is essentially the scheduled travel time and is therefore easy to compute. However, for personal auto modes, it is not clear how the anticipated travel time should be computed. This paper focuses on addressing the following two questions: What is the relationship between the distribution of the travel times that travelers experience and the travel times that travelers anticipate for a future trip? What effect do unusually long travel times have on this anticipated travel time? The authors explored both questions through a stated preference survey that was distributed to over 3,000 individuals. Just over 300 valid responses were received, and on the basis of the survey results, a two-stage model for estimating the anticipated travel time as a function of the experienced travel time distribution was formulated and calibrated. The proposed model can be applied to travel times obtained from simulation models or from field observations.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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