Analysis of Freight Global Positioning System Data
Bibliographic record
Abstract
Many firms use Global Positioning System (GPS) trackers to monitor their vehicle fleets. Such data are increasingly being made available to researchers and provide a rich source of information to support modeling of the freight system. The objective of this paper is to determine the best procedure for clustering GPS trip ends into repeated visits to common destinations. This clustering allows the GPS data to be used for analyzing the repetitiveness of commercial vehicle travel and for combined models of stop frequency and stop duration. Various hierarchical agglomeration and partitioning clustering methods were compared by using data for one firm based in Toronto, Canada, that provided GPS records and driver records for a 3-month period in 2007. The analysis found that Ward's method was preferred for this application because of good clustering results and reasonable computational effort. This method was then tested on GPS data for 40 firms by using visual inspection of the clustering results. Issues were identified in the use of a single clustering distance threshold. A novel two-step clustering procedure was developed to address the issues. First, the GPS trip ends were clustered by using Ward's method. Second, clusters whose median GPS point fell within the boundary of a single property were combined. Results from the two-step clustering procedure are promising. Preliminary analysis indicated that few destinations were visited on a daily basis. Hence, travel behavior for commercial vehicles for one day is often not representative of other days; this aspect highlights concerns about travel demand models estimated by using a travel data set collected over short time periods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| 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) | 0.002 | 0.001 |
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".