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Record W2018829876 · doi:10.3141/1760-12

Simulation Model for Evaluating Intelligent Paratransit Systems

2001· article· en· W2018829876 on OpenAlexaff
Liping Fu

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsParatransitField (mathematics)Computer scienceReliability (semiconductor)Transport engineeringVariety (cybernetics)TRIPS architectureIntelligent transportation systemPublic transportSystems engineeringRisk analysis (engineering)EngineeringSimulationOperations research

Abstract

fetched live from OpenAlex

The latest advances in information technology, such as automatic vehicle location, digital telecommunications, and computers, have renewed interest among public transit agencies in applying these technologies to enhance the efficiency and reliability of their paratransit systems. This interest has also induced a need for relevant methodologies and tools that can help address some fundamental questions on the cost-effectiveness of these technologies. This type of evaluation has mostly relied on field studies that usually require a significant expenditure and are conditioned to limited operating environments. A simulation system is presented that has been developed to model a variety of technology-oriented dial-a-ride paratransit systems, offering a way to complement or replace the field evaluation method. The general concepts and models applied in the simulation system are discussed, focusing on how various components are modeled and how they interact with each other in the overall simulation framework. The simulation system is applied to evaluate the effects of trip cancellation on the operational performance of a partly synthetic paratransit system, with the goal of quantifying the potential benefits that may be attained by accepting real-time demand trips.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.265
GPT teacher head0.454
Teacher spread0.190 · 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.

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
Published2001
Admission routes1
Has abstractyes

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