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Record W3036551148 · doi:10.1139/cjce-2019-0628

Evaluation of freeway travel speed estimation using anonymous cellphones as probes: a field study in China

2020· article· en· W3036551148 on OpenAlexaffvenue
Haihang Han, Liqun Peng, Ai Teng, Chen‐Hao Wang, Tony Z. Qiu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaDepartment of Transportation of Zhejiang ProvinceU.S. Department of Transportation
KeywordsComputer scienceTransport engineeringReal-time computingFloating car dataTravel timePenetration rateEstimationSimulationEngineeringTraffic congestion

Abstract

fetched live from OpenAlex

The high penetration rate of mobile phones among drivers and passengers indicates an opportunity for obtaining detailed information on vehicle spatial movement and estimating traffic state at a lower cost than traditional traffic monitoring techniques. In this study, we examine and assess the use of cellphones as probes to measure vehicle speeds. Network-based wireless location technology is adopted and calibrated, and the cellphone signalling data are used for traffic state estimation. Then, we introduce the relationship between the cellular network and road network, and explain how it can be used to estimate link-based average travel speed. Cellular probe-based measurements are compared with those obtained by microwave detectors for each five-minute time interval on busy road links along a freeway in Zhejiang, China, on weekdays, weekends, and holidays respectively. The analysis results show that the proposed cellphone-based system can effectively estimate travel speed on freeways with high cellphone penetration rates.

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)0.002
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.037
GPT teacher head0.290
Teacher spread0.254 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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