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Record W1996869679 · doi:10.1002/atr.157

Analogy of fixed route shared taxi (taxi khattee) and bus services under various demand density and economical conditions

2011· article· en· W1996869679 on OpenAlexvenueno aff
Ali Gholami, Afshin Shariat Mohaymany

Bibliographic record

VenueJournal of Advanced Transportation · 2011
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportTransport engineeringService (business)Mode (computer interface)Level of serviceTransit (satellite)Computer scienceOperations researchEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract Taxi khattee is a fixed route unregulated shared taxi. It is a very common mode of transportation in Iran. Fixed route, unscheduled operation, open, unlimited pick‐up and drop‐off locations, and share ride are common features of taxi khattees. Low passenger capacity and working in high demand corridors provides for the possibility of high service frequencies any time of the day. Taxi khattees are similar to jitneys, which are obsolete or illegal in many countries. The aim of this research is to design transit network of an area using taxi khattees in addition to buses. The methodology employed in this paper simultaneously considers the costs to the users and operators on the one hand, and those of the public non‐users on the other hand. Taxi khattees are used in the design of a multimodal network along with buses to characterize the appropriate economic domain for their use. Moreover, their operation indices are compared against those of buses. A sensitivity analysis is carried out on various performance measures. Results show taxi khattees should be used in areas where population density is low, work force is inexpensive, social costs are not considered in fare calculation, and users' value of time is high. The study contradicts the common belief that since taxi khattees provide a high frequency compared to buses, they are economically plausible to use in a transit fleet. Copyright © 2011 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.463

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 communication0.0000.000
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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

Citations21
Published2011
Admission routes1
Has abstractyes

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