Design Vehicles: Frome Turning Templates to Smart Systems
Bibliographic record
Abstract
The application of a design vehicle as an integral part of highway geometric was formally establish by AASHO in 1940. This early AASHO documentation contained a set of four design vehicles, generally the largest in each category including an automobile, bus and two trucks. Swept paths for simple 90 degree turns for the single wheelbase vehicles were developed based on equations. The 1954 AASHO “A Policy on Geometric Design of Rural Highways”, was published and Mr. Jack Leisch, shortly thereafter developed the first set of plastic turning vehicle templates at a scale of 1”=50’, 1”=40’ and 1”=20’ for the use of DeLeuw Cather staff and a limited number of others. Jack Leisch moved to Toronto, Canada in 1964 and CGRA under Mr. Leisch’s direction, developed the second generation of turning vehicle templates which were sold by CGRA throughout Canada and the US beginning in 1969. These plastic turning templates and their future generations became the standard approach to the design of any vehicle facility and is still used widely today. The development of the computer led to several developments on mainframe computers that allowed the simulation of turning vehicles, however it wasn’t until the early 1990s, with the dawn of the micro-computer that programs such as AutoTRACK, AutoPATH, and AutoTURN®, that operated directly within CAD, became a popular approach to designing for turning vehicles at intersections, roundabouts, parking facilities truck terminals and bus stations. These approaches provided designers with considerable flexibility and efficiency in a CAD environment over fixed plastic (mylar) turning templates. One future direction is for design to be fully inclusive of vehicles turning requirements, such that it allows for a highly dynamic and integrated approach, eliminating the need to check for the required space after the fact. Coupled with expert systems capabilities, these programs will provide powerful and highly effective methods for providing for the spatial requirements for design vehicles, in all dimensions – horizontal and vertical, as well as speed. This paper will chronicle the evolution of the application of design vehicles in geometric design from inception in the late 1930’s to the present and provided insight into the future of this fundamental component of road and facility planning and design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".