Intelligent Transit Thru Imaging: Using CCTV Cameras for Evaluating Real Time Demand
Bibliographic record
Abstract
Abstract Ubiquitous CCTV cameras are a commonplace in cities with their presence on street intersections, bus stops, train stations and ferry terminals, etc. This paper presents an idea to use Artificial Intelligence in devising heat maps whereby traffic density is mapped from CCTV camera image grab of a location. When fed into the Traffic Management System (TMS), relevant alerts can be issued to the Traffic Controllers, consequently, deploying a bus or a metro train to meet that extra rush of passengers. Intelligent models can be developed that create novel routes during specific events in the city. New demand and response models can be created within TMS thru use of data science. Routes can be temporarily modified, with crowded stops added and uncrowded ones removed. Passengers are given adequate notice thru live alerts. Ethical issues related to data privacy are handled by specialized software. Such an intelligent system can help control crime. By active monitoring, the response time in attending accidents can be lessened. Necessary warnings are issued to bus operators concerning elderly and people with special needs, enabling better onboarding experience.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".