Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Smart Card Automated Fare Collection (SCAFC) system is an Intelligent Transportation System that is becoming increasingly popular among transit operators. In addition to fare control, the data collected by these systems can be very useful in transit planning. Many SCAFC systems store the location where the passenger boarded due to the positioning device carried onboard; however, in most systems alighting locations are not validated and, thus, not stored in databases. This article presents a model to estimate the destination location for each individual boarding a bus with a smart card. Experiments carried out with a database programming approach show that the data must be thoroughly validated and corrected prior to the estimation process. The first application of the model provided a success rate of 66% for destination estimation, reaching about 80% at peak hours. Further research will tackle the issues of error detection, correction, and link results, comparing them with those of other data sources.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it