Data Collection Methodology for Container Truck Traffic in Inland Port Cities
Bibliographic record
Abstract
This paper describes a data collection methodology to address insufficient data sources for estimation of urban container truck traffic (drayage) volumes. The methodology is sensitive to the characteristics of drayage and offers a systematic approach for acquiring container truck traffic data for constructing models to estimate drayage volumes. The methodology consists of (a) acquiring urban truck traffic estimates and national and provincial- or state-level container traffic databases, (b) characterizing shippers and carriers through field investigations and surveys, and (c) designing a container truck data collection program. Short-term manual truck classification intersection turning movement counts were conducted to obtain body style and axle configuration data for articulated trucks. Temporal expansion factors were developed and applied to short-term count data to produce average daily container truck traffic volume estimates and reveal temporal, physical, and spatial distribution differences between container trucks and other articulated trucks. The paper provides a rationale for selecting count station locations and their temporal characteristics, choosing the number of counts and their duration, determining the types of data to collect, and identifying container generators. The methodology is generally applicable to North American inland port cities. The data feed a model that is intended to assist transportation engineers in understanding urban drayage operations and quantifying the exposure of these trucks.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".