Improving Travel Information Systems with the Assistance of GPS
Bibliographic record
Abstract
Travel delays and the ever increasing traffic queues negatively affect the everyday lives of commuters.Users of public transit systems, such as the TTC, are left unaware of the length of the delay they might experience on their journey.One of the most important factors in an Intelligent Transportation System (ITS) is the ability to predict the bus's arrival time accurately which not only increases the current customers' satisfaction, but also attracts new customers for the system.The objectives of this paper are (1) to predict the bus travel times using GPS data, and (2) to develop a parametric optimization model to improve bus arrival times based on the speed distribution.The bus travel time optimization model proposed in this research explicitly includes arrival time, dwell times, schedule adherence, and traffic congestion, all critical factors for accurate bus arrival times.In this paper, a test bus route was used in downtown Toronto, Canada.The optimization model performed significantly better than the historical data-based models.The proposed model identified the relationship between travel times and the independent variables, leading to superior results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".