Technologies for Row and Seat Identification Onboard Aircraft for Travelers Who Are Blind
Bibliographic record
Abstract
The objective of this study, carried out for Transport Canada’s Transportation Development Centre, was to identify the state of the art of wireless technologies applicable to enhance independent wayfinding for travelers with sight limitations in Canada with consideration to current cabin safety regulations and their usability by the passenger. The aim was to develop a technological solution that enables blind passengers to identify row/seat/washroom locations on board aircrafts without assistance. An international literature review was undertaken on wireless technologies applicable to onboard orientation and wayfinding tasks. Through surveys and interviews, additional inputs were gathered from knowledgeable practitioners. A listing of suitable location, transmission, receiver and user interface technologies has been compiled and discussed. A task analysis for the traveler’s trip chain onboard aircraft resulted in ten (10) wayfinding and orientation scenarios. An evaluation framework was designed to prioritize technologies identified. Project team members were enlisted to rank both the criteria and the applicable technologies. Members concluded that the most important criteria for implementation of a selected technology were regulatory clearance, followed by receiver ownership and its effectiveness for users. The RFID (Radio Frequency Identification) location technology was chosen by members as having the best potential for wayfinding applications, followed by the Barcode/Quick Response Code (QR Code) technology. Smartphones with software and vibration features are eligible receiver systems for pilot testing due to their versatility and usability, especially by passengers who are deaf-blind. The project team recommended two technologies as candidates for a pilot test.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".