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Record W2149308255 · doi:10.1002/atr.210

Blocking roads to increase the evacuation efficiency

2012· article· en· W2149308255 on OpenAlexvenueno aff
Olga Huibregtse, Andreas Hegyi, Serge Hoogendoorn

Bibliographic record

VenueJournal of Advanced Transportation · 2012
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsnot available
FundersRijkswaterstaatTechnische Universiteit Delft
KeywordsBlocking (statistics)Traffic flow (computer networking)Computer scienceIntervention (counseling)Flow (mathematics)Lead (geology)Transport engineeringFunction (biology)SimulationOperations researchComputer securityEngineeringComputer networkMathematicsGeology

Abstract

fetched live from OpenAlex

SUMMARY In order to evacuate as many people as possible in case of an approaching disaster, evacuation plans have to be made in advance. These plans usually consist of optimized traffic flows. Theoretically, these flows lead to an efficient evacuation, but in practice evacuees will most probably not behave like these flows without any intervention. This difference is partly caused by evacuees using roads that are unused in the optimized traffic flows case. In this paper, a new approach is investigated that can be applied to approach these flows during an evacuation: blocking the roads (i.e., making the roads inaccessible) that have a flow equal to zero in the optimal traffic flows. This approach is expected to be effective because the people are forced to behave in the direction of the optimized traffic flows. In a case study, this approach turns out to be effective. For two different cases, the efficiency of the evacuation (a function of the arrivals) improves with respectively 10.0% and 13.4% when the zero‐flow links are blocked compared with an evacuation without any intervention. Copyright © 2012 John Wiley & Sons, Ltd.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.241
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2012
Admission routes1
Has abstractyes

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