Measuring, Describing and Modeling Travel Time Reliability
Bibliographic record
Abstract
Assessing the performance of a highway network is a challenging task. Many authorities are hardly dealing with the responsibility of gathering data and estimating relevant performance indicators. With the important evolution of congestion in many urban areas, it has become critical to better assess the evolution of travel times. In fact, with the increase in the frequency and duration of congestion states, it is becoming less possible to focus on sole detection of decreases in travel times. Hence, the challenge is now to ensure reliable travel times. Monitoring highway network performances requires the definition of new indicators involving both travel times and their variability. In Montreal, researches are being conducted to assess the reliability of travel times on the main highway corridors. This paper presents the outputs of a methodology that was developed to estimate travel times using almost 30,000 observations from floating cars. It builds upon previous results based on the systematic division of routes in one kilometer segments. Clustering techniques are used to associate segments to particular clusters based on the similarity of travel time distributions. The process outputs sixteen clusters of segments, eight for each period (AM, PM). The travel time distributions of each cluster are modeled using three additive log normal distributions. New performance indicators are also proposed: a network malfunction indicator and a mean-variability indicator. In the future, the proposed method will enable planners to simulate the expected travel time on various routes, evaluate its reliability as well as the probability to face atypical travel conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".