Decision support for simulating the car park activity in an urban area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Complexity of car park activity is reproduced from a concurrent execution of behaviour of various drivers. This paper presents a step in the development of a multimodal traffic simulator based on multi‐agent paradigm and designed as a decision aid tool as well as a video game. The user‐player has the opportunity to test different scenarios. We propose an approach for designing the decision‐making rules and the learning mechanism for a car driver agent. For that, a panel of methods such as stated preference modelling, Design Of Experiments and data fusion is used. Initial behavioural models, based on similar preferences, are developed for specified categories. Each agent will adapt its behaviour after executing its learning process. Our approach can be used in order to optimize needs of road network users and those of people in charge of traffic regulation. A demonstrator has been developed to test parking policies in an urban area as well as changes of car park characteristics.
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.
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 it