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Record W2119997346 · doi:10.1002/atr.5670350307

Behavioral monitoring of public transport users through a mobile communication system

2001· article· en· W2119997346 on OpenAlexvenueno aff
Yasuo Asakura, Eiji Hato

Bibliographic record

VenueJournal of Advanced Transportation · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportTransport engineeringTransport systemInformation systemComputer scienceMobile telephonyAutomatic vehicle locationCommunications systemTelecommunicationsGlobal Positioning SystemEngineeringMobile radioElectrical engineering

Abstract

fetched live from OpenAlex

Abstract There are various activities now taking place in ITS research and development in Japan. Advanced information and communication technologies have been applied to improve public transport systems, as well as automated highway systems. In the first part of this paper, we show three examples of public transport systems recently developed in ITS environment. These transport systems are operated in local cities and towns in Japan: the travel information system for tram users in Hiroshima, the demand responsive bus system in Nakamura and the co‐operative use of electric vehicle in Ebina. In the second part of the paper, we explain how we have monitored individual passenger on public transport using cellular phones for location positioning. Location positioning technology for mobile object is essential for the operation and management of ITS supported public transport systems. Furthermore, such accurate and detailed positioning data can be utilized for travel behaviour analysis in demand modeling. The mobile instrument and monitoring systems shown in this paper can be combined with any of the case studies of ITS application to public transport systems.

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)0.001
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 score0.219
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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

Citations8
Published2001
Admission routes1
Has abstractyes

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