Global Positioning System (GPS)/Automatic Vehicle Location (AVL) Use, Challenges, and Cost-Benefit in Operations
Bibliographic record
Abstract
Automatic Vehicle Location (AVL) systems are helping Departments of Transportation (DOTs) achieve a variety of new efficiencies while improving or maintaining level of service (LOS) through periods of state budget shortfalls. The trucking, emergency response, and transit communities have used GPS/AVL for years. Now DOTs are realizing new efficiencies with this technology as well. Recent findings on the challenges and cost-benefit advantages DOTs are finding with these technologies are discussed in this paper, summarizing the author’s 2011 and 2012 surveys of DOTs on this topic. For example, in addition to the 10% materials savings that DOTs in the US and Canada have reported, automated data collection associated with GPS/AVL is saving DOT maintenance forces thousands of hours filling out paperwork, boosting morale as well as effectiveness. Washington State (WS) DOT estimated the agency and the public benefit from an additional 10,000 hours per year that maintenance employees are out plowing instead of filling out paperwork, equating to a biennial savings of $700,000 in labor costs. The savings they found were such that WSDOT now aims to have all winter material application records, and material inventory issues recorded automatically, and the agency will begin to use their GPS/AVL equipment to help automate documentation of the maintenance staff performs on the state’s permanent stormwater control structures in the right-of-way, associating hours worked with GPS located stormwater facilities, to better understand life cycle costs, maintenance requirements, and document and communicate maintenance needs to the state legislature, for better funding.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".