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Record W2461241631 · doi:10.1002/atr.1394

Urban development with financially sustainable rail service

2016· article· en· W2461241631 on OpenAlexvenueno aff
David Z.W. Wang, Hong K. Lo

Bibliographic record

VenueJournal of Advanced Transportation · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessService (business)Sustainable developmentTransport engineeringEnvironmental planningEngineeringEnvironmental scienceMarketingPolitical science

Abstract

fetched live from OpenAlex

Summary The positive correlation between urban population density and transit service patronage is well recognized, as was ascertained via statistical approaches in previous studies. In this study, we seek to derive some prescriptive results of the relationship between urban population density and the financial sustainability of rail transit service via analytical approaches. We consider an idealized metropolitan region with a central business district at its center, whose population is distributed according to a certain density saturation gradient pattern. Trips generated from the region to the central business district are served either by the rail service supplemented with feeder buses or by autos. Travelers choose one of the two modes to minimize the travel cost. The traffic congestion effects on highway system for auto users will be considered by assuming flow‐dependent travel time delay. The crowding costs of transit services will also be taken into account. The spatial equilibrium travel pattern with modal choices will be modeled by applying a differential equation approach. Then, we study the sensitivity of urban development density on the financial sustainability of the rail service by examining the supply and demand patterns. Through the analysis, the result sheds light on the threshold urban density required, below which the service cannot be sustained financially. The results also provide guidelines for planning urban development with financially sustainable rail services. Copyright © 2016 John Wiley & Sons, Ltd.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.239

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 communication0.0000.001
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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