An Approach for Detecting Data Anomalies at Permanent Cycling Count Stations
Bibliographic record
Abstract
With the large amounts of available traffic data, it becomes necessary to develop tools that can perform several tasks related to the collected data. These tasks include storing the data in a standard format, filtering the data/flagging suspicious records, processing the data and calculating useful quantitative traffic indices, and finally, visualizing the outcomes. In this paper, a data-driven, yet novel, data-filtering approach was proposed to flag outliers in daily cycling counts at automatic traffic counters (ATCs). The approach was motivated by the spatiotemporal relationship of cycling counts collected at permanent count stations. The proposed approach is flexible because it assumes no prior knowledge about which locations may experience sensor malfunction (i.e., outliers). The approach was tested using a large data set of more than 111,000 daily bicycle volumes collected in 4 years (2016–2019) at more than 60 different permanent count stations in the City of Vancouver, Canada. The approach was validated using complete annual sets of data at four count stations in 2016. Scenarios of undercounting and overcounting were simulated using different percentages of the actual counts. The results showed that the proposed approach has a strong ability in detecting and removing most outliers, especially for cases of substantial undercounting.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".