Driving Simulation Design and Evaluation of Highway–Railway Grade and Transit Crossings
Bibliographic record
Abstract
This chapter addresses the design and evaluation of a section of roadway using driving simulation. A driving simulator was used to model a section of roadway which crossed a rail crossing and two at-grade intersections, one of which was a bus transitway, all in close proximity. The purpose of the simulator was to optimize the design through visual simulation as well as through testing with a sample of drivers. Specifically, plans for the section of roadway, on Fallowfield Road, north of Ottawa, Canada, were computer modeled in extensive detail and integrated into the University of Calgary Driving Simulator (UCDS), which was a research first in Canada. After driving the simulated Fallowfield Roadway tile, numerous elements of the design were changed and modified based on expert input from the organizations involved in the project. Approximately 20 design changes were incorporated into the final simulation model. To further evaluate the design elements, 47 participants, stratified into the age groups of 18–24, 25–55, and 55 and older, drove the Fallowfield simulation model in both directions, and on selected runs were challenged by two traffic events, namely a late yellow light and a stalled truck just past the railroad tracks. Results showed that older drivers had significantly lower speeds at the rail crossing in both eastbound and westbound directions and had lower comprehension of a number of signs than other age groups. Eye movement analyses indicated that several signs were not fixated by the majority of participants and these signs had still fewer fixations when traffic was present. Driver responses to the two challenging traffic events in these contexts were similar to those observed in real-world situations. A set of recommendations is made with respect to signs and signals identified in the evaluation phase. The utility of high-fidelity driving simulation models to visualize and problem-solve complex highway engineering designs and evaluate resulting solutions had a number of positive safety and design benefits, which are discussed.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".