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Record W653698129

Investigating Economic Viability of Personal Rapid Transit (PRT) System for a University Campus and Its Surroundings

2013· article· en· W653698129 on OpenAlexaboutno aff
Shahram Tahmasseby, Lina Kattan

Bibliographic record

VenueTransportation Research Board 92nd Annual MeetingTransportation Research Board · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMicrosimulationInvestment (military)Internal rate of returnAbu dhabiTransport engineeringTransit (satellite)Public transportCost–benefit analysisRevenueBusinessEnvironmental economicsEnvironmental scienceEngineeringMetropolitan areaEconomicsFinanceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper discusses the methodology and results of an economic viability analysis of a demand-responsive personal rapid transit (PRT) system in a Canadian city. A microsimulation model was built to examine the feasibility of a PRT system linking the University of Calgary and surrounding major attractions. The environmental benefits of PRT operation were estimated in terms of reduction of air pollutants. Microsimulation was used to obtain accurate estimates of travel time and access/egress time for PRT and other transit modes, travel time savings for PRT patrons, and estimates of emissions. The investment costs were estimated using data from existing PRT projects (e.g., Heathrow Airport, Cardiff and Daventry, UK and Masdar City, Abu Dhabi, United Arab Emirates). Ridership was estimated by means of a catchment area method adopted from public transit. A cost-benefit analysis model was developed to evaluate the economic viability of the system, considering the capital and operation costs associated with serving the dispersed sites. The sensitivity of the internal rate of return to changes in demand and investment cost was also investigated.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.341
Teacher spread0.290 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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