Spatial Transferability of Expansion Factors for Estimating Pedestrian Volume at Intersections
Bibliographic record
Abstract
Pedestrian exposure at intersections is a key input for jurisdictions to develop pedestrian-centric strategies. Exposure is typically expressed as the annual average daily pedestrian traffic (AADPT). The expansion factor method is the most common technique employed to estimate AADPT, consisting of the expansion of short-term counts (STCs) using factors calculated from sites with continuous counts (CCs). However, jurisdictions often lack sufficient CCs to determine expansion factors, which motivated this work of assessing the spatial transferability of expansion factors. This paper examines the spatial transferability across three jurisdictions in Ontario, Canada, with similar school holiday and weather seasonality, and one jurisdiction in Arizona, U.S.A., with different characteristics. It was assumed that the "base jurisdiction" has CCs available, while the "target jurisdiction" has only STC sites, and applies expansion factors from the base jurisdiction. Two approaches were tested: the single factor method, averaging expansion factors across all sites in the base jurisdiction, and modeling methods, which assign STC sites to factor groups based on models developed in the base jurisdiction. The single factor method showed acceptable transferability within the Ontario jurisdictions, with an average absolute increase of 2.3% (8.7% relative increase) in the mean absolute percent error of AADPT achieved when comparing the spatially transferred factors to those obtained when the base and target jurisdictions are the same. However, poor transferability was observed between jurisdictions with differing characteristics. The application of modeling methods yielded inconsistent results, possibly because of the limited number of sites available in this study, and requires further investigation.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".