Measuring temporal and spatial travel efficiency for transit route system using low-frequency bus automatic vehicle location data
Bibliographic record
Abstract
Although the bus probe data have been widely adopted for examining the transit route efficiency, this application cannot guarantee the accuracy in special temporal and spatial segments due to the inadequate probe samples. This study evaluates the feasibility of automatic vehicle location data as probes for the bus route travel time evaluation. Our techniques explore the minimum requirement of transit automatic vehicle location data to recover the bus trajectories in various spatial–temporal dimensions along the scheduled transit routes. First, a three-dimensional tensor is established to infer the uncovered link traveling information in current time slots and the last short-term period. Then, a general form is proposed to calculate the local mean travel speed and the average link travel time in each separated time slot of day. Finally, a case study has been conducted using field transit automatic vehicle location data running on a bus route corridor in Edmonton, Canada. The results demonstrate the effectiveness and efficiency of low-frequency bus automatic vehicle location data as probes for transit route efficiency measurement by comparing with baseline approaches. This work also supports the feasibility of using automatic vehicle location–equipped buses as customized buses for choosing alternate path based on evaluating the current transit efficiency on all routes.
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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 | 0.000 | 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 | 0.000 | 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 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".