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Sidewalk-Based Accessible Pedestrian Routing

2024· article· en· W4404564271 on OpenAlexaff
Finian Lugtigheid, Andrew J. Park, Eunju Hwang, Valerie Spicer, Patricia L. Brantingham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsSimon Fraser UniversityTrinity Western UniversityWestern University
Fundersnot available
KeywordsPedestrianComputer scienceRouting (electronic design automation)Pedestrian detectionComputer networkTransport engineeringEngineering

Abstract

fetched live from OpenAlex

As access to public transportation expands, people increasingly find themselves able to travel to unfamiliar areas. Routing systems may assist people in navigating to unfamiliar locations by identifying the most efficient and safe paths for their journeys. However, most routing systems are optimized for vehicles and do not find the best routes for pedestrians. Additionally, pedestrians with mobility impairments have specific requirements that must be met for a route to be safe and traversable. These requirements may be different for each individual user. To address these issues, this study presents a sidewalk-based routing system that allows users to create accessibility profiles which the router uses to provide safe and accessible routes for that user.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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 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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