Bibliographic record
Abstract
Public Transit Information System is an online transportation system providing\n\nintegrated transit journey planning solutions to the general public. Generally, the\n\nsystem requires users to input their places of departure and arrival as well as some\n\npreferences such as the shortest or cheapest path. The system will then generate the\n\nresults with transit schedule, route, fare and so forth. Since the system is designated\n\nfor public usage, it is important that the system not only meets the needs of its users,\n\nbut also is easily operated by the general public with different level of computer skills.\n\nThus, this paper explores the design criteria of public transit information system by\n\nreviewing the development of user interface design from previous research and\n\ncomparing the user interfaces of the public transit information systems of Hong Kong,\n\nSingapore and Vancouver. This paper also studies the transit systems of these three\n\nplaces and thereby concludes that the diverse and complex transit network of Hong\n\nKong makes it difficult in developing such a system. This paper concludes that\n\naesthetics, consistency and effective error management are the technical design\n\ncriteria that can improve the usability of public transport information systems. In\n\naddition, technicians should also consider the social aspects, such as computer literacy\n\nand the computer systems when designing products for the public.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".