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Taxi Dispatch and AEV Management in AEV Taxi Services

2021· article· en· W4200616981 on OpenAlexafffund
Dafei Zhao, Binod Vaidya, Hussein T. Mouftah

Bibliographic record

Venue2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) · 2021
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAdaptabilityScheduling (production processes)Computer scienceService (business)IdleTransport engineeringOperations researchBusinessOperations managementEngineeringMarketing

Abstract

fetched live from OpenAlex

Internet-based taxi service not only facilitates passenger travel but also effectively improves the utilization of transportation resources. Autonomous electric vehicle (AEV), as a future-oriented form of transportation, is more environmentally friendly and intelligent as it does not require a driver and uses green energy to fulfill the trip. Using idle AEVs for taxi service is an effective way to realize smart city transportation in the future. With the aim of supporting AEVs to provide taxi services, AEV Taxi Management and Dispatching Module (ATMDM), is proposed to support AEV management and scheduling in the AEV Taxi Service (ATS) system. By efficiently maintaining AEV status and information, ATMDM is able to realize the management of multiple AEVs as well as can also provide taxi service by matching orders with appropriate AEVs and providing route information based on the received trip requests. Compared with traditional taxi dispatching solutions, ATMDM fully considers the operational characteristics of AEV with better adaptability, which shows insight for the future means of transportation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.212
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.

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
Published2021
Admission routes2
Has abstractyes

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