A Methodology for Container Truck Traffic Data Collection for Inland Port Cities
Bibliographic record
Abstract
This paper describes a container truck traffic data collection methodology for inland port cities. The data collection methodology is developed in response to a lack of data sources to estimate urban container truck traffic volumes. The methodology is sensitive to the unique characteristics of container truck traffic and is one component of on-going research to develop a container trucking model for inland port cities in the Canadian Prairie Region. This model is intended to assist transportation engineers understand the impact of container trucking in their cities and reveal issues that should be considered in defining, evaluating, and choosing among alternative options to improve container freight transportation in urban areas. This data collection methodology consists of (i) shipper and carrier characterization, (ii) database acquisition, and (iii) the design of the container truck data collection program. Existing databases are municipal truck turning movement counts, permanent traffic counts, and provincial/state level border crossing data. The data collection program performs short-term manual truck classification intersection turning movement counts which are guided by recommendations from the Federal Highway Administration’s Traffic Monitoring Guide. These counts obtain body type and axle configuration data for articulated trucks. The methodology described in this paper offers a systematic approach to acquire container truck traffic data and a process to validate the data and results of the container truck model. Container truck traffic volumes are estimated using the data from this methodology and shown on a flow map. Although traffic flow discontinuities are evident on certain links due to data gaps, the container truck traffic volumes are proven to be reasonable and support the validity of the data collection methodology. This methodology is developed for Winnipeg, Manitoba and other Canadian Prairie cities but is generally applicable to similar inland port cities in other jurisdictions.
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How this classification was reachedexpand
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".