Constructing Origin-Destination Matrix using Wi-Fi and AFC Data
Bibliographic record
Abstract
<title>Abstract</title> Transportation systems planning and management rely heavily on Origin-Destination (OD) demand matrices. However, the traditional approach of creating these matrices using household travel survey data is not only time-consuming but also expensive, making it challenging to apply them to detailed and time-sensitive analyses. Automated fare collection (AFC) systems can provide a solution to these challenges. However, many transit fare systems are entry-only with no requirement to tap at the exit station, which makes it challenging to determine the location of alighting stations and analyze spatial demand patterns. This study proposes a framework that uses Wi-Fi traces and passenger counts at AFC entry/exit gates to construct OD matrices for entry-only Urban Rail Transit (URT) systems. The City of Toronto's subway system was used as a case study, and the framework was compared to 2016 Transportation Tomorrow Survey (TTS), which is the primary source of OD-matrix estimation in the city. The generated OD-matrices were found to be very close to the OD-matrix from the household travel survey, with a cosine similarity close to one for most subway regions. Our estimated OD-matrices offer several advantages over traditional methods. They have low matrix sparsity and fast computational time and convergence, and they exhibit strong capability of recognizing demand patterns at the station-level.
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How this classification was reachedexpand
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".