Advanced traffic management systems information infrastructure: the ITS Centre and Testbed (ICAT) platform
Bibliographic record
Abstract
The ITS Centre and Testbed at the University of Toronto enables the research and development of the advanced computer algorithms required to improve the efficiency of traffic networks. The ITS Centre receives live traffic data and video from the Ontario Ministry of Transportation (MTO) and from the City of Toronto via fibre-optic connections. The ITS Centre and Testbed (ICAT) platform was created to furnish an elaborate information infrastructure for Advanced Traffic Management Systems (ATMS). The ICAT platform is designed to handle data from many jurisdictions. It has been tested with data from MTO, the City of Toronto, Minnesota and the Netherlands. The ICAT software architecture facilitates future expansions in new analysis tools. In addition to providing many basic data visualization capabilities, the ICAT also facilitates traffic data driven research and development that is both repeatable and transferable. In this thesis, we apply the ICAT framework and develop three substantial research tools. Detailed methodologies, results and analysis are provided. The first tool provides a new look at traffic data using concepts from information theory. It provides insights about the randomness of traffic flow variables, as well as the dependency between the variables. The second tool is a dynamic probabilistic traffic flow model (PTFM). The model has an efficient calibration procedure and it provides soft predictions---i.e., the uncertainty of the predictions can be determined. It is shown that the PTFM performs well compared to other current models. The third tool is the first to quantify the impact of CMS messages on traffic flow diversion. It also quantifies the temporal span beyond which the impact on diversion diminishes, in both the short and long term. In addition to being contributions on their own, the above research tools show the versatility of the ICAT platform and the benefits of research that is both repeatable and transferable. This thesis also documents three research works that were led by others but involved the author in a significant way. They demonstrate how the ICAT platform enables a broad spectrum of research topics.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".