Estimating Pedestrian Volumes at Each Crosswalk of Intersections: Comparison of Land-Use Models and Short-Term Count Methods
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Bibliographic record
Abstract
Estimating pedestrian exposure for all the intersections in a jurisdiction is crucial for developing strategies with a focus on pedestrians. Some engineering applications require the annual average daily pedestrian traffic (AADPT) to be disaggregated per crosswalk. When continuous counts are available at the intersection, this indicator can be calculated directly. However, when only short-term counts (STCs) or no information on pedestrian volume is available, the AADPT per crosswalk cannot be calculated and must be estimated using other means. This work (1) evaluated the degree of confidence for estimating the pedestrian volume in each crosswalk based on point estimates of percentage shares per crosswalk obtained from STCs; and (2) developed models to estimate the percentage share of pedestrian volume per crosswalk as a function of attributes of the intersection that commonly are available for jurisdictions, referred to as the land-use (LU) model. The two methods were evaluated using continuous count data from three different jurisdictions, and a naive estimate assuming equal shares per crosswalk was used as a benchmark. The performance of each method was measured as the fraction of the intersection AADPT that was allocated wrongly to each crosswalk. The use of the LU model generated an average wrong allocation of 0.301, a statistically significant improvement of 11.4% compared with the naive estimate. The use of a STC from a single day produced an average wrong allocation of 0.153, an improvement of 54.9% from the naive estimate. Increasing the number of days of STCs to two or three resulted in average performance indicators of 0.117 and 0.106, respectively. The benefits of using STCs for more than three days are minimal. The STC method was developed using STCs from the same 1-year period in which the observed share was averaged. In practice, STCs are likely to be between 1 and 5 years old. Analysis using STCs from previous years showed that estimation error in practice may be as much as twice as large as the aforementioned errors.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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