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Record W2802620160 · doi:10.1139/cjce-2017-0518

Reliability estimation of public bus routes: Applicability of multivariate adaptive regression splines approach

2018· article· en· W2802620160 on OpenAlexvenueno aff
Mustafa Özuysal, Süheyla Pelin Çalışkanelli

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsReliability (semiconductor)RoundaboutMultivariate adaptive regression splinesComputer scienceMultivariate statisticsReliability engineeringNonparametric statisticsNonparametric regressionEngineeringTransport engineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

The performance of public bus lines is generally evaluated by comparing demand and ridership. However, reliability gain or loss by a proposed bus route should also be considered in the decision-making process to ensure a service that is preferable for users and operable for providers. In this study, it is aimed to provide a tool for predicting the reliability of a proposed bus route by considering route layout and traffic conditions. Travel time based reliability is predicted by using a novel nonparametric method, multivariate adaptive regression splines (MARS). Some critical thresholds of route layout parameters that should be considered for higher reliability are found. It is concluded that route lengths longer than 10 km, and number of intersections over 22 considerably decrease whole day based reliability. For peak hour based reliability, the types and numbers of intersections are found to be more efficient than the ones in whole day based model and a reliability regulator impact of roundabout numbers under nine is observed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.024
GPT teacher head0.257
Teacher spread0.232 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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