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Record W2127941462 · doi:10.5278/ojs.td.v1i1.5507

The Usability of Passenger Delay Models in Socio-Economic Analysis

2020· article· en· W2127941462 on OpenAlexaff
Mikkel Thorhauge

Bibliographic record

VenueAalborg University Library · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsTransport Canada
Fundersnot available
KeywordsComputer sciencePassenger transportOperations researchTransport engineeringEngineering

Abstract

fetched live from OpenAlex

The following paper discusses how a passenger delay model can be used in socio-economic calculations. At present passenger delays are often omitted in the modeling phase and therefore not included in the socio economic analysis. By using a passenger delay model passenger delays can be included in a cost-benefit analysis. Including passenger delays in the cost-benefit analysis will increase the level of details and thereby improve the accuracy of socio-economic analysis. In this paper the third generation passenger delay model is used. This model is the newest and most detailed passenger delay model created so far. When using a passenger delay model, the main problem is how to properly include the delays in a socio- economic analysis. This is due to the fact that passenger delays are not necessarily unambiguously. In general, delays can occur on different parts of a journey; while the passengers are waiting for the train (waiting time, first waiting time or even hidden waiting time) or while the passenger are sitting in the train (or bus). Furthermore a delay can also be negative, meaning that a passenger will arrive before planned (a so-called negative delay). This article proposes that a delay is defined solely by the difference between the scheduled and realized arrival time. The recommendations are listed as follows: • The value of time for a delay is defined as done by the Danish Ministry of Transport (Trafikministeriet, 2003) no matter how or when the delay has occurred. The size of the delay is calculated solely by the difference between the planned and realized arrival time. • A negative delay is defined as the value of time of hidden waiting time. A negative delay is calculated as the difference between the planned and realized arrival time and is considered a surplus in the cost benefit analysis. • A delay will not be included if the passenger arrives at his or her final destination on time even though the passenger may have experienced a delay (or travelled along a different route than planned) during the journey. Note that this paper is regarded as a prequel to the article “Optimization of timetable supplement from a passenger based socio-economic point of view” (Thorhauge, 2010). This article is based on the results of (Thorhauge & Piester, 2010).

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 score0.071
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.215
Teacher spread0.198 · 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
Published2020
Admission routes1
Has abstractyes

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