Comparative study on travel time reliability indexes for highway users and operators
Bibliographic record
Abstract
SUMMARY Travel time reliability is an emerging evaluation measure for highway network service levels. This paper focuses on travel time reliability indexes for a highway network and proposes new travel time indexes for users. The paper also discusses and compares several previously and newly proposed indexes for use by both highway users and operators. Because this comparative study requires travel time variation, two models are developed for estimating travel time variation. One is the multi‐hierarchical stochastic model for estimating travel time variation under uncertainty in demand and service. The other is the temporal and spatial transition model using actual traffic detector data. Then, new indexes for assessing travel time reliability are proposed. Because operators of highway network are interested in the degree of congestion and delay, the previously proposed indexes are regarded as operator‐side indexes. The highway users are, however, interested in the accuracy of their travel time and in the earlier arrival at their destination. Thus, this paper proposes the user‐side indexes. Travel time reliability indexes, including previously proposed indexes, are calculated on the basis of the cumulative distribution function outcome from these models and are compared and discussed. In two sections, 11 indexes with different characteristics are compared. One of the results is that these indexes behave differently for the same route. This suggests that understanding an index's formulation and characteristics is important for selecting the appropriate index according to both the purpose of use and the characteristics of the study route. Another important result is that indexes useful for the user side do not always agree with the operator‐side indexes. Finally, the results of the comparative study suggest that the combination of average travel time and an appropriate travel time reliability index is very important for both users and operators for assessing the route reliability properly. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".