Bibliographic record
Abstract
The interactions between individual vehicles and their interactions with transportation infrastructure create an interesting and difficult to model system. The patterns many of us see daily, how and where traffic flows, depend on many factors such as weather, time of day, road closures and access to major thoroughfares. Creating a model that is capable of displaying the complexities that emerge from such a system can be a daunting task. This difficulty emerges because our assumptions about the driving force behind traffic systems, namely human behaviour, may either be hard to quantify or simply incorrect. However, through the novel applications of existing technologies we can create real-time interactive models that allow us to see and influence the way traffic flows through an urban environment. With over 24 million current cell phone subscriptions in Canada, cell phone trajectory data can provide the necessary information on the ebb and flow of the movements of large populations. To create the road based environment for the model, large GIS databases such as OpenStreetMap.org can be used. Finally, by using a platform such as the Unity game engine to construct and run the model allows for high levels of interactivity and large scale 3D visualization. Agent Based Modeling is the chosen method of simulation. This is advantageous for two main reasons. The first is that traffic is a phenomenon that emerges from the interactions of many individuals within an environment. Secondly, the fact that the provided cellular trajectories track the rough movement of individuals over time harmonizes well with the above. This presentation illustrates the construction of a model currently being developed and its validation process and results and its possible uses in other areas such as simulating the spread of pandemic contact based infections.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".