Analyzing and optimizing pedestrian flow through a topological network based on <i>M</i>/<i>G</i>/<i>C</i>/<i>C</i> and network flow approaches
Bibliographic record
Abstract
Summary An M/G/C/C state dependent queuing network measures the performance of a system whose service rate decreases with the increasing number of residing entities. However, the performance in terms of throughputs, levels of congestions, the expected number of entities, and the expected service time is typically analyzed based on a series of arrival rates without any further discussion on the optimal arrival rate. This paper derives the optimal arrival rates of corridors in a topological network using calculus and numerical analysis approaches. These optimal rates are then used as capacity parameters in the network's flow model to obtain the optimal arrival rates that maximize its total throughput. To ease the construction and performance evaluation of the network, we design and construct an M/G/C/C framework based on the Object‐Oriented Programming approach that integrates the lingo software as an optimization tool. The framework is then tested on virtual and real networks. This framework can be used to develop a more advanced traffic management tool for studying and managing traffic flow through a complex network. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".