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Dynamic Evacuation Routing Plan after an Earthquake

2015· article· en· W1997154344 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNatural Hazards Review · 2015
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsUniversité Laval
FundersUniversity of Tehran
KeywordsRouting (electronic design automation)Computer sciencePlan (archaeology)Transport engineeringSimulated annealingOperations researchComputer networkEngineeringGeography

Abstract

fetched live from OpenAlex

This study proposes an earthquake evacuation routing plan from local shelters to regional ones for a long-term safe settlement using public vehicles. In a post-earthquake situation, the unpredicted changes in travel demand patterns and accessibility conditions of the transportation network affect the travel time. The contribution of this study is to propose a dynamic evacuation routing approach that can update the routing plan by incorporating time-dependent travel times. The problem is modeled as a vehicle routing problem and a two-stage solution procedure based on the simulated annealing algorithm is developed. The model is applied in part of Tehran’s transportation network. The results confirm that the dynamic evacuation routing approach is able to increase the number of evacuated shelters and decrease both the evacuation time and total travel time of the vehicles. The findings in this study indicate that the application of the proposed model can provide beneficial information for disaster management.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.603

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 head0.017
GPT teacher head0.283
Teacher spread0.265 · 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