Edmonton Urban Roadside Truck Survey: Planning and Operations
Bibliographic record
Abstract
The City of Edmonton's Transportation Master Plan identifies the City's commitment to efficient goods movement as part of enabling economic development and supporting the competitiveness of businesses. In April 2012, a number of questions were raised by councillors wanting to better understand truck movement needs within the city, including impacts of infrastructure investments over the past decade and changes in goods movement patterns. After a review of possible methodologies, a roadside truck survey was implemented in order to gather qualitative and quantitative goods movement data within tight time and budget constraints. A survey was designed that included questions about vehicle characteristics, travel patterns, route preferences, commodities carried and driver experience. The survey was conducted with 2,294 participants over 14 days in fall 2012. The survey data was supplemented by classified volume counts across the city conducted on the day of and the day before the survey. Subsequent data processing and analysis were performed in order to report results that could be compared with past goods movement surveys which used regional roadside cordon and business establishment survey methods. Based on the experience of this survey, recommendations are made for the planning, field operation and data analysis involved in an urban roadside truck survey. Survey site planning and staff training are important for delivering a safe and effective survey. Through careful planning and implementation of best practices, an urban roadside survey can yield cost effective results for jurisdictions contemplating urban goods movement studies. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".