Investigation of methods and approaches for collecting and recording highway inventory data.
Bibliographic record
Abstract
Many techniques for collecting highway inventory data have been used by state and local agencies in the United States. These techniques include field inventory, photo/video log, integrated global positioning system/geographic information system (GPS/GIS) mapping systems, aerial photography, satellite imagery, virtual photo tourism, terrestrial laser scanners, mobile mapping systems (i.e., vehicle-based light detection and ranging (LiDAR), and airborne LiDAR). These highway inventory data collection methods vary in terms of equipment used, time requirements, and costs. Each of these techniques has its specific advantages, disadvantages, and limitations. This research project sought to determine cost-effective methods to collect highway inventory data not currently stored in Illinois Department of Transportation (IDOT) databases for implementing the recently published Highway Safety Manual (HSM). The highway inventory data collected using the identified methods can also be used for other functions within the Bureau of Safety Engineering, other IDOT offices, or local agencies. A thorough literature review was conducted to summarize the available techniques, costs, benefits, logistics, and other issues associated with all relevant methods of collecting, analyzing, storing, retrieving, and viewing the relevant data. In addition, a web-based survey of 49 U.S. states and 7 Canadian provinces has been conducted to evaluate the strengths and weaknesses of various highway inventory data collection methods from different state departments of transportation. To better understand the importance of the data to be collected, sensitivity analyses of input variables for the HSM models of different roadway types were performed. The field experiments and data collection were conducted at four types of roadway segments (rural two-lane highway, rural multi-lane highway, urban and suburban arterial, and freeway). A comprehensive evaluation matrix was developed to compare various data collection techniques based on different criteria. Recommendations were developed for selecting data collection techniques for data requirements and roadway conditions.
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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 | 0.107 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".