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Record W7023677851

Optimization of semi-flexible transit operation for low demand scenarios

2024· dissertation· en· W7023677851 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsParatransitHeadwayTransit (satellite)Service (business)AdaptabilityPublic transportOperator (biology)Operating costTransshipment (information security)
DOInot available

Abstract

fetched live from OpenAlex

Many transit agencies in North America suggest semi-flexible transit (SFT) as a viable solution to the growing demand for highly personalized and expensive paratransit services with an increasingly aging population, high operating costs associated with low demand bus transit routes, and lacking adaptability of fixed-route bus transit to serve increasingly diverse spatiotemporal travel needs. This thesis proposes an effective methodology for the optimization of SFT for operation along an under-performing low demand bus transit route in Regina, Canada. In this thesis, three research questions are addressed: (1) What levels of demand are optimal for SFT operation, given the two service delivery models, in-house transit, IHT, and contracted-out taxi, COT? (2) How to optimally design service headway (h) and slack time per trip for route-deviation (Δt) in an integrated SFT that serves both fixed-route and paratransit demand? and (3) What is the optimal vehicle size and vehicle technology for SFT operation when comparing two technologies: battery-electric vehicles (BEV) and diesel-based vehicles (ICEV)? Analytical and metaheuristic optimization techniques are employed to determine the optimal value for decision variables. The findings suggest that SFT with COT delivery model is most economical in terms of operator cost when demand is unexpectedly low, SFT with IHT delivery model is more economical when demand is low to medium, and conventional bus transit operating in-house is more cost-effective when transit demand is high. Operator cost favours solutions with low service frequency (i.e., high h), user cost favours lower ranges of h and Δt, and higher service benefit is derived from high Δt; thus, medium ranges of h and Δt appear to provide the most reasonable trade-off for service. It is also observed that for low demand (5-15 pass/hr), in terms of the total cost (i.e., operator, user, and environmental) a minivan ICEV outperforms all other scenarios as the potential savings in energy cost in favour of BEB were offset by the present high cost of installing fast chargers and when demand increases, minivan BEV outperforms. Besides contributing to the state-of-the-art research on SFT optimization, the study models are used as part of a decision support tool to establish contracting, transit network planning, vehicle technology, operation, and fare policies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.201
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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