Bibliographic record
Abstract
Travel time is a simple and robust network performance measure that is perceived and well understood by the public and politicians. However, travel time data collection can be costly especially if the analysis area is extensive. This thesis proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. The approach makes use of travel time correlation between nearby (neighbour) links to estimate travel times on links with no data using neighbour links travel time data. A framework is proposed that estimates link travel times using available data from neighbouring links. The proposed framework was validated using real-life data from the City of Vancouver, British Columbia. The travel time estimation accuracy was found comparable to the existing literature. The concept of neighbour links travel time estimation was extended and applied at a corridor level. Regression and Non-Parametric (NP) models were developed to estimate travel times of one corridor using data from another corridor. To analyze the impact of the probes’ sample size on the accuracy of the proposed methodology, a case study was undertaken using a VISSIM microsimulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. The methodology provided reasonable estimation accuracy even using small probe samples. The use of bus travel time data to estimate automobile travel times of neighbour links was explored. The results showed that bus probes data on neighbour links can be useful for estimating link travel times in the absence of vehicle probes. The fusion of vehicle and bus probes data was analyzed. Using transit data for neighbour links travel time estimation was shown to improve the accuracy of estimation at low market penetration levels of passenger probes. However, the significance of transit probe data diminishes with the increase of market penetration level of probe vehicles. Overall, the results of this thesis demonstrate the feasibility of using neighbour links data as an additional source of information that might not have been extensively explored.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".