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Record W1995366112 · doi:10.3141/2047-02

Impacts on Traffic Diversion Rates of Changed Message on Changeable Message Sign

2008· article· en· W1995366112 on OpenAlexaffabout
Simon Foo, Baher Abdulhai, Fred L. Hall

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsUniversity of CalgaryUniversity of TorontoMcMaster University
Fundersnot available
KeywordsDownstream (manufacturing)Transport engineeringTransfer (computing)Upstream (networking)Christian ministryTraffic bottleneckTraffic flow (computer networking)Computer scienceTraffic congestionEngineeringOperations managementComputer securityTelecommunicationsTraffic optimizationFloating car data

Abstract

fetched live from OpenAlex

The Ontario Ministry of Transportation has installed 27 changeable message signs (CMSs) strategically upstream of express–collector transfer locations on Highway 401 in Toronto, Ontario, Canada. Motorists are informed by the CMSs of traffic conditions downstream of the transfer location to help them decide whether to take the next transfer. Loop detectors are installed at the transfer locations to measure traffic flow. The dynamic impacts of CMS messages on traffic diversion are evaluated by using 3 years of loop detector data, from 2003 to 2005. Time-series plots of aggregated diversion rates show that the initial transient response to the message change is significant and that, in addition to the messages themselves, the occurrence of a message change plays a vital role in influencing downstream diversion. Aggregate diversion rate plots also reveal that the logic underlying the Highway 401 CMSs may be causing the signs to react to diversion rate changes on many occasions, thereby closing a feedback control loop that effectively regulates the downstream diversion rates.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.093
GPT teacher head0.341
Teacher spread0.248 · 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 designObservational
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

Citations19
Published2008
Admission routes2
Has abstractyes

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