Dynamic partitioning of urban road networks based on their topological and operational characteristics
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
Modelling, observation and control of realistic urban road networks of significant size may be subject to the `curse' of dimensionality. On the other hand, the `nature' of the physical system introduces the necessity of its unified treatment, for capturing the interactions and the spatial connectivity of the complete system. In order to facilitate the handling of such complex traffic systems, an approach could be the optimal partitioning of the complete network in sub-regions/networks based on one-or more-criteria. The resulted sub-networks then can be processed more easily (e.g. utilizing tactics of parallel processing), increasing computational performance. Several methods are available and can be applied for partitioning graph/networks, though road networks exhibit particularities that pose constraints in applying standard mesh/graph partitioning technics. One of the most important features stands for the fact that road networks are directed networks with many bidirectional arcs of dynamic operational characteristics, which should be partitioned in compact closed sub-regions. In the current paper the results of an investigation on dynamic network partitioning are provided and discussed in detail. A data-based clustering method has been initially tested, namely k-means, accounting for network's topology, structure and operational characteristics, exhibiting the strengths and restrictions of its application in realistic and dynamic settings. However, the fact that k-means is not a method dedicated in providing road network's partitions, the use of a graph partitioning model, namely METIS. The data used here have been collected from the real-time surveillance system operated in Nicosia, Cyprus, that provides detailed traffic observations (flow and speed), while the comparative results of the two approaches are also provided. It has been observed that METIS partitioning could be used for network partitioning purposes, in the various alternative datasets used for performing a dynamic network partitioning `exercise'. The results showed the effect of traffic dynamics on partitioning realistic urban systems.
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 | 0.000 |
| 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