Error correction of arrival time prediction in real time bus information system
Bibliographic record
Abstract
Abstract Many large cities in Korea have either implemented or are planning to install a bus information system (BIS) in order to improve the quality of service for bus passengers. This is mainly being conducted by providing bus arrival times at bus stops. In these systems, similar systematic errors occur in the estimation of bus arrival times, which are influenced by the information updating time (cycle length) taken to identify each bus location, the information processing time, and the cycle length required to update the bus arrival information on each terminal. The systematic errors can occur in the collection of data, information processing, and in the passenger waiting time. This study investigated these systematic errors and developed a statistical method for correcting these errors in order to improve the accuracy of the BIS information. The proposed method is based on probability density functions and the random incidence concept. The developed method was then applied to the BIS of a city in Korea in order to verify the efficacy of the method. Through the verification results, there was a 23% error reduction after applying the error correction method to the BIS. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".