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Aerial Cable Cars in Urban Public Transit: Queuing Capacity Thresholds

2024· article· en· W4408696610 on OpenAlexaff
Morten Flesser, Amer Shalaby, Bernhard Friedrich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsUniversity of Toronto
FundersDeutsche Forschungsgemeinschaft
KeywordsPublic transportQueueing theoryTransport engineeringTransit (satellite)Computer scienceTelecommunicationsComputer networkEngineering

Abstract

fetched live from OpenAlex

This study analyzes the feasibility of urban aerial cable cars as a mobility option combined with conventional modes of transport, discusses the use of cable cars in urban public transit, and places the results in an extended context of existing literature. Specifically, the study aims at (1) defining the capacity thresholds of conventional modes of transport that serve as feeders to cable cars and (2) determining the corresponding space requirements for arising queues. Key performance indicators are accordingly waiting time, queue length and queue space. Queuing theory is utilized to determine waiting times and queue lengths, and subsequently, the space requirements for queues are derived. Findings indicate that cable cars can serve as a viable mobility option if passenger demand from feeder conventional modes of transport is appropriately limited and excessively large batches of passengers arriving simultaneously are avoided. For example, subways and commuter rails, even if operating in long headways of 30 minutes, would lead to overcrowded transport systems and long waiting times. However, feeders such as buses, BRT, or trams demonstrate good operability. Accordingly, the work identifies suitable thresholds for interoperability to further promote transit as a crucial part of future urban mobility.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.566

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 communication0.0000.000
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 head0.037
GPT teacher head0.248
Teacher spread0.210 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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