BusNet : a prototype implementation of an Intelligent Transportation System for Winnipeg urban transit
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) are being used in many cities of the world. In this project, as a subset of such a system for the city of Winnipeg, an instance of an Advanced Traveler Information System (ATIS) is introduced and the implementation of a prototype of it is described. Such a system could be used to access to the real-time information of the operative buses of the city transit system. Using low price GPS receivers, specific computer applications, and wireless communication/ telecommunication systems capabilities, as well as, the Bluetooth wireless technology a positioning system is designed and employed to transfer the real-time information of the locations of the operative buses to a database. The information gathered in the database is provided to the end users connected to the Internet. The information is also provided to be used by the transit system monitoring and management centre. Fuzzy logic concepts in reporting the real-time positioning information and Neural Network methods in creating transit system timetables are discussed. The system architecture, following Canadian and US national ITS architecture, is introduced and closely investigated. Such a scaleable system could be a part of an integrated Winnipeg Transportation Management Center (TMC) in future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".