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Record W1592723517 · doi:10.21949/1527279

An Assessment of Automatic Passenger Counters

2015· article· en· W1592723517 on OpenAlexaboutno aff
David Vozzolo, John Attanucci

Bibliographic record

VenueROSA P · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersU.S. Department of Transportation
KeywordsScheduling (production processes)Transport engineeringReliability (semiconductor)Computer sciencePassenger transportTransit (satellite)Operations researchEngineeringOperations managementPublic transport

Abstract

fetched live from OpenAlex

This report summarizes the objectives and current application of automatic passenger counter programs in twelve North American transit properties. Findings are also presented regarding an assessment of APC technology on the basis of accuracy, equip- ment reliability, data turnaround time, and cost. The report concludes with a case study of the APC program in Ottawa, Ontario, the only North American property which currently depends almost entirely on APC techniques to supply their service planning needs . The primary objective of automated passenger counters is to efficiently acquire accurate data on passenger activity and transit travel times. These data, which are essential for on-going planning and scheduling activities, may include boardings, alightings, passenger loads, and vehicle running times. Automated techniques enable the reporting and analysis of these data in varying levels of detail. Findings of this report indicate that the APC technology and its creative use may not be the "magical solution" to the bus transit monitoring dilemma; however, it does offer a reasonably cost-effective option which operators can seriously consider to satisfy their data collection needs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.097

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 head0.033
GPT teacher head0.382
Teacher spread0.350 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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