Identifying Public Transit Commuters Based on Both the Smartcard Data and Survey Data: A Case Study in Xiamen, China
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
Understanding the travel patterns of public transit commuters was important to the efforts towards improving the service quality, promoting public transit use, and better planning the public transit system. Smartcard data, with its wide coverage and relative abundance, could provide new opportunities to study public transit riders’ behaviors and travel patterns with much less cost than conventional data source. However, the major limitation of smartcard data is the absence of social attributes of the cardholders, so that it cannot clearly extract public transit commuters and explain the mechanism of their travel behaviors. This study employed a machine learning approach called Naive Bayesian Classifier (NBC) to identify public transit commuters based on both the smartcard data and survey data, demonstrated in Xiamen, China. Compared with existing methods which were plagued by the validation of the accuracy of the identification results, the adopted approach was a machine learning algorithm with functions of accuracy checking. The classifier was trained and tested by survey data obtained from 532 valid questionnaires. The accuracy rate for identification of public transit commuters was 92% in the test instances. Then, under a low calculation load, it identified the objectives in smartcard data without requiring travel regularity assumptions of public transit commuters. Nearly 290,000 cardholders were classified as public transit commuters. Statistics such as average first boarding time and travel frequency of workdays during peak hours were obtained. Finally, the smartcard data were fused with bus location data to reveal the spatial distributions of the home and work locations of these public transit commuters, which could be utilized to improve public transit planning and operations.
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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 | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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