A Method for Bus OD Matrix Estimation Using Multisource Data
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 automated fare collection (AFC) system has gained increasing popularity among transit systems worldwide. The AFC system is usually an entry-only system that only records the serial number of the smart card and the transaction time of each use. Neither the AFC data nor the bus global positioning system (GPS) could reveal the passenger’s alighting information, namely, alighting time and station. Hence, the station-to-station origin-destination (OD) trip information cannot be obtained directly from the available data sources. To address this problem, this paper proposes a methodology that estimates the OD matrix by using smart card and GPS data. In this paper, the characteristics of the basic data sources are first analyzed, based on which the bus arrival time is generated using the density-based clustering algorithm and a time correction strategy, based on which the passenger’s boarding station is identified. The alighting stations are inferred based on the characteristics of bus trip chaining, which could identify over 80% of the alighting stations on average. Finally, the proposed methodology is verified by a comprehensive field survey in Suzhou, China, with 100% sample rate.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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