Bibliographic record
Abstract
This Thesis focuses on developing robust dynamic route guidance algorithms to reduce traffic congestion in roadway networks. While recurring traffic congestion is normally the focus in planning and investment decisions, almost half of traffic congestion is caused by non-recurring traffic disturbances, primarily caused by incidents, vehicle breakdowns, extreme weather events, etc. In order to reduce traffic congestion, we focus on the problem of understanding the effect of traffic disturbances and reducing their impact on roadway networks. We introduce a systematic framework for defining the context of robustness based on the severity, frequency and predictability of traffic disturbances and for developing a robust design for roadway networks based on network design goals. We also present methods to speed up traffic assignment algorithms through compiler optimizations and parallelism, to efficiently measure the effect of traffic disturbances, and enable real-time ITS applications including dynamic route guidance systems. Next, we introduce a hybrid metric for measuring robustness in a roadway network by extending the shortest-path betweenness metric from network science, and augmenting it with links weights based on dynamic traffic flow metrics. Finally, we implement a robust dynamic traffic assignment algorithm for roadway networks based on this metric and test it on a large-scale calibrated network model for the Greater Toronto Area. Performance results show that the robust traffic assignment algorithm reduces vehicle travel times compared to existing traffic assignment algorithms, with and without the presence of disturbances in the form of traffic incidents. This makes a strong case for traffic planners and operators to use robust dynamic route guidance systems within actual implementations of real-time ITS strategies to help proactively alleviate traffic congestion due to disturbances.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".