Estimation of Daily Bicycle Traffic Volumes Using Spatiotemporal Relationships
Bibliographic record
Abstract
Automatic counters (e.g., loop detectors) used for the continuous collection of cycling count data are subject to periodic malfunctions, leading to sporadic data gaps. This problem could affect the calculated values of the annual average daily bicycle (AADB) volumes and impact the estimates of the daily and monthly adjustment factors at these count stations. The impacts become more significant when the data gaps take place frequently and/or for long periods. This research addresses the problem of missing cycling traffic volumes at the count stations that experience frequent sensor malfunctions. The main hypothesis is that a strong correlation may exist among the cycling volumes of nearby facilities within a network. This correlation can be used to develop neighborhood models based on the available historical data. This study made use of a data set of more than 14,000 daily bicycle volumes from the city of Vancouver, Canada. The data were collected between 2009 and 2011 at 22 different count stations. A correlation analysis was first undertaken, and the results showed a strong correlation between the cycling volumes at most of the analyzed facilities. Furthermore, a cross-correlation analysis showed that the strongest correlation between each pair of count stations took place at a time lag of zero days (i.e., concurrent data). Accordingly, a correlation threshold was selected and used to define a set of neighbors for each cycling facility. Statistical models were developed to relate the daily cycling volumes of each pair of neighbors. The models were validated; the mean absolute percentage error (MAPE) was used as an evaluation measure. In general, the MAPE was less than 20% for most facilities when a correlation threshold of 0.6 was used to identify neighbors. However, the error dropped to approximately 15% when higher thresholds were used. The concept should prove useful in estimating the missing cycling volumes in a monitoring program or a data clearinghouse implementation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".